{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DESeq2: Basic Differential Expression (DE) analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objective: Carry out a basic set of DE analyses using DESeq2 and visualize the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Registered S3 methods overwritten by 'ggplot2':\n", " method from \n", " [.quosures rlang\n", " c.quosures rlang\n", " print.quosures rlang\n", "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n", "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.1.1 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.2\n", "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 2.1.2 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 0.8.1\n", "\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 0.8.3 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", "\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 1.3.1 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.4.0\n", "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", "Loading required package: S4Vectors\n", "Loading required package: stats4\n", "Loading required package: BiocGenerics\n", "Loading required package: parallel\n", "\n", "Attaching package: ‘BiocGenerics’\n", "\n", "The following objects are masked from ‘package:parallel’:\n", "\n", " clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n", " clusterExport, clusterMap, parApply, parCapply, parLapply,\n", " parLapplyLB, parRapply, parSapply, parSapplyLB\n", "\n", "The following objects are masked from ‘package:dplyr’:\n", "\n", " combine, intersect, setdiff, union\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " IQR, mad, sd, var, xtabs\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n", " dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", " grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n", " order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", " rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n", " union, unique, unsplit, which, which.max, which.min\n", "\n", "\n", "Attaching package: ‘S4Vectors’\n", "\n", "The following objects are masked from ‘package:dplyr’:\n", "\n", " first, rename\n", "\n", "The following object is masked from ‘package:tidyr’:\n", "\n", " expand\n", "\n", "The following object is masked from ‘package:base’:\n", "\n", " expand.grid\n", "\n", "Loading required package: IRanges\n", "\n", "Attaching package: ‘IRanges’\n", "\n", "The following objects are masked from ‘package:dplyr’:\n", "\n", " collapse, desc, slice\n", "\n", "The following object is masked from ‘package:purrr’:\n", "\n", " reduce\n", "\n", "Loading required package: GenomicRanges\n", "Loading required package: GenomeInfoDb\n", "Loading required package: SummarizedExperiment\n", "Loading required package: Biobase\n", "Welcome to Bioconductor\n", "\n", " Vignettes contain introductory material; view with\n", " 'browseVignettes()'. To cite Bioconductor, see\n", " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", "\n", "Loading required package: DelayedArray\n", "Loading required package: matrixStats\n", "\n", "Attaching package: ‘matrixStats’\n", "\n", "The following objects are masked from ‘package:Biobase’:\n", "\n", " anyMissing, rowMedians\n", "\n", "The following object is masked from ‘package:dplyr’:\n", "\n", " count\n", "\n", "Loading required package: BiocParallel\n", "\n", "Attaching package: ‘DelayedArray’\n", "\n", "The following objects are masked from ‘package:matrixStats’:\n", "\n", " colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n", "\n", "The following object is masked from ‘package:purrr’:\n", "\n", " simplify\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " aperm, apply, rowsum\n", "\n", "\n", "---------------------\n", "Welcome to dendextend version 1.12.0\n", "Type citation('dendextend') for how to cite the package.\n", "\n", "Type browseVignettes(package = 'dendextend') for the package vignette.\n", "The github page is: https://github.com/talgalili/dendextend/\n", "\n", "Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues\n", "Or contact: \n", "\n", "\tTo suppress this message use: suppressPackageStartupMessages(library(dendextend))\n", "---------------------\n", "\n", "\n", "Attaching package: ‘dendextend’\n", "\n", "The following object is masked from ‘package:stats’:\n", "\n", " cutree\n", "\n" ] } ], "source": [ "library(tidyverse)\n", "library(DESeq2)\n", "library(dendextend)\n", "library(RColorBrewer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the 2019 pilot dds object from image file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "'/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData'" ], "text/latex": [ "'/home/jovyan/work/scratch/analysis\\_output/img/pilotdds2019.RData'" ], "text/markdown": [ "'/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData'" ], "text/plain": [ "[1] \"/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData: '6632de5c8a2eed06d8c40c958e6f5d03'" ], "text/latex": [ "\\textbf{/home/jovyan/work/scratch/analysis\\textbackslash{}\\_output/img/pilotdds2019.RData:} '6632de5c8a2eed06d8c40c958e6f5d03'" ], "text/markdown": [ "**/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData:** '6632de5c8a2eed06d8c40c958e6f5d03'" ], "text/plain": [ "/home/jovyan/work/scratch/analysis_output/img/pilotdds2019.RData \n", " \"6632de5c8a2eed06d8c40c958e6f5d03\" " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "'dds2019'" ], "text/latex": [ "'dds2019'" ], "text/markdown": [ "'dds2019'" ], "text/plain": [ "[1] \"dds2019\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "curdir <- \"/home/jovyan/work/scratch/analysis_output\"\n", "imgdir <- file.path(curdir, \"img\")\n", "\n", "imgfile <- file.path(imgdir, \"pilotdds2019.RData\")\n", "\n", "imgfile\n", "\n", "attach(imgfile)\n", "\n", "tools::md5sum(imgfile)\n", "\n", "### List the objects that have been attached\n", "ls(2)\n", "\n", "dds2019 <- dds2019\n", "\n", "detach(pos = 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check dimensions of the two objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect object & Slots of an S4 class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's has a look at the object we have created." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(1): counts\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(0):\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(22): Label sample_year ... RIN_normal_threshold\n", " RIN_lowered_threshold" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "see the class of dds object" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "'DESeqDataSet'" ], "text/latex": [ "'DESeqDataSet'" ], "text/markdown": [ "'DESeqDataSet'" ], "text/plain": [ "[1] \"DESeqDataSet\"\n", "attr(,\"package\")\n", "[1] \"DESeq2\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "DESeqDataSet is a S4 object. Recall that a S4 object was taught when introducing bioconductor. Note that S4 objects allow users to wrap up multiple elements into a single variables where each element is called a slot." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'design'
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  2. 'dispersionFunction'
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  3. 'rowRanges'
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  4. 'colData'
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  5. 'assays'
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  6. 'NAMES'
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  7. 'elementMetadata'
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'design'\n", "\\item 'dispersionFunction'\n", "\\item 'rowRanges'\n", "\\item 'colData'\n", "\\item 'assays'\n", "\\item 'NAMES'\n", "\\item 'elementMetadata'\n", "\\item 'metadata'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'design'\n", "2. 'dispersionFunction'\n", "3. 'rowRanges'\n", "4. 'colData'\n", "5. 'assays'\n", "6. 'NAMES'\n", "7. 'elementMetadata'\n", "8. 'metadata'\n", "\n", "\n" ], "text/plain": [ "[1] \"design\" \"dispersionFunction\" \"rowRanges\" \n", "[4] \"colData\" \"assays\" \"NAMES\" \n", "[7] \"elementMetadata\" \"metadata\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slotNames(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The metadata (columnData) is stored in the slot `colData`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 22
Labelsample_yeargroupenrich_repRNA_sample_numgenotypeconditionlibprep_personenrichment_methodenrichment_short⋯i5_primeri7_primerlibrary_numbio_replicateNanodrop_260_280Nanodrop_260_230Nanodrop_concentration_ng_ulBioanalyzer_concentration_ng_ulRIN_normal_thresholdRIN_lowered_threshold
<chr><dbl><chr><dbl><dbl><fct><fct><chr><chr><chr>⋯<chr><chr><dbl><dbl><dbl><dbl><dbl><dbl><chr><dbl>
1_2019_P_M11_2019_P_M12019P11WTpH4CmRNAM⋯i501i701112.141.52293197N/A9.8
2_2019_P_M12_2019_P_M12019P12WTpH4CmRNAM⋯i502i701222.121.79290225N/A9.9
3_2019_P_M13_2019_P_M12019P13WTpH4CmRNAM⋯i503i701332.112.49302241N/A9.9
\n" ], "text/latex": [ "A data.frame: 3 × 22\n", "\\begin{tabular}{r|llllllllllllllllllllll}\n", " & Label & sample\\_year & group & enrich\\_rep & RNA\\_sample\\_num & genotype & condition & libprep\\_person & enrichment\\_method & enrichment\\_short & i7\\_index & i5\\_index & i5\\_primer & i7\\_primer & library\\_num & bio\\_replicate & Nanodrop\\_260\\_280 & Nanodrop\\_260\\_230 & Nanodrop\\_concentration\\_ng\\_ul & Bioanalyzer\\_concentration\\_ng\\_ul & RIN\\_normal\\_threshold & RIN\\_lowered\\_threshold\\\\\n", " & & & & & & & & & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1\\_2019\\_P\\_M1 & 1\\_2019\\_P\\_M1 & 2019 & P & 1 & 1 & WT & pH4 & C & mRNA & M & ATTACTCG & AGGCTATA & i501 & i701 & 1 & 1 & 2.14 & 1.52 & 293 & 197 & N/A & 9.8\\\\\n", "\t2\\_2019\\_P\\_M1 & 2\\_2019\\_P\\_M1 & 2019 & P & 1 & 2 & WT & pH4 & C & mRNA & M & ATTACTCG & GCCTCTAT & i502 & i701 & 2 & 2 & 2.12 & 1.79 & 290 & 225 & N/A & 9.9\\\\\n", "\t3\\_2019\\_P\\_M1 & 3\\_2019\\_P\\_M1 & 2019 & P & 1 & 3 & WT & pH4 & C & mRNA & M & ATTACTCG & AGGATAGG & i503 & i701 & 3 & 3 & 2.11 & 2.49 & 302 & 241 & N/A & 9.9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 22\n", "\n", "| | Label <chr> | sample_year <dbl> | group <chr> | enrich_rep <dbl> | RNA_sample_num <dbl> | genotype <fct> | condition <fct> | libprep_person <chr> | enrichment_method <chr> | enrichment_short <chr> | ⋯ ⋯ | i5_primer <chr> | i7_primer <chr> | library_num <dbl> | bio_replicate <dbl> | Nanodrop_260_280 <dbl> | Nanodrop_260_230 <dbl> | Nanodrop_concentration_ng_ul <dbl> | Bioanalyzer_concentration_ng_ul <dbl> | RIN_normal_threshold <chr> | RIN_lowered_threshold <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1_2019_P_M1 | 1_2019_P_M1 | 2019 | P | 1 | 1 | WT | pH4 | C | mRNA | M | ⋯ | i501 | i701 | 1 | 1 | 2.14 | 1.52 | 293 | 197 | N/A | 9.8 |\n", "| 2_2019_P_M1 | 2_2019_P_M1 | 2019 | P | 1 | 2 | WT | pH4 | C | mRNA | M | ⋯ | i502 | i701 | 2 | 2 | 2.12 | 1.79 | 290 | 225 | N/A | 9.9 |\n", "| 3_2019_P_M1 | 3_2019_P_M1 | 2019 | P | 1 | 3 | WT | pH4 | C | mRNA | M | ⋯ | i503 | i701 | 3 | 3 | 2.11 | 2.49 | 302 | 241 | N/A | 9.9 |\n", "\n" ], "text/plain": [ " Label sample_year group enrich_rep RNA_sample_num genotype\n", "1_2019_P_M1 1_2019_P_M1 2019 P 1 1 WT \n", "2_2019_P_M1 2_2019_P_M1 2019 P 1 2 WT \n", "3_2019_P_M1 3_2019_P_M1 2019 P 1 3 WT \n", " condition libprep_person enrichment_method enrichment_short ⋯\n", "1_2019_P_M1 pH4 C mRNA M ⋯\n", "2_2019_P_M1 pH4 C mRNA M ⋯\n", "3_2019_P_M1 pH4 C mRNA M ⋯\n", " i5_primer i7_primer library_num bio_replicate Nanodrop_260_280\n", "1_2019_P_M1 i501 i701 1 1 2.14 \n", "2_2019_P_M1 i502 i701 2 2 2.12 \n", "3_2019_P_M1 i503 i701 3 3 2.11 \n", " Nanodrop_260_230 Nanodrop_concentration_ng_ul\n", "1_2019_P_M1 1.52 293 \n", "2_2019_P_M1 1.79 290 \n", "3_2019_P_M1 2.49 302 \n", " Bioanalyzer_concentration_ng_ul RIN_normal_threshold\n", "1_2019_P_M1 197 N/A \n", "2_2019_P_M1 225 N/A \n", "3_2019_P_M1 241 N/A \n", " RIN_lowered_threshold\n", "1_2019_P_M1 9.8 \n", "2_2019_P_M1 9.9 \n", "3_2019_P_M1 9.9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019@colData %>% as.data.frame %>% head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The design formula is stored in the slot `design`. The design holds the R formula which expresses how the counts depend on the variables in colData." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "~condition + genotype" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019@design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing you may want to do is **have a look at the raw counts** you have imported. The `DESeq2::counts` function extracts a matrix of counts (with the genes along the rows and samples along the columns). Let us first verify the dimension of this matrix." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 8499
    2. \n", "\t
  2. 24
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 8499\n", "\\item 24\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 8499\n", "2. 24\n", "\n", "\n" ], "text/plain": [ "[1] 8499 24" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(counts(dds2019))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 24 of type int
1_2019_P_M12_2019_P_M13_2019_P_M14_2019_P_M15_2019_P_M16_2019_P_M17_2019_P_M18_2019_P_M19_2019_P_M110_2019_P_M1⋯15_2019_P_M116_2019_P_M117_2019_P_M118_2019_P_M119_2019_P_M120_2019_P_M121_2019_P_M122_2019_P_M123_2019_P_M124_2019_P_M1
CNAG_00001 0 0 0 0 0 0 0 0 0 0⋯ 0 0 0 0 0 0 0 0 0 0
CNAG_00002158204149176161148172169124119⋯216224234338191201192166202235
CNAG_00003201156161171162103172170175131⋯197195211201118133109113154169
\n" ], "text/latex": [ "A matrix: 3 × 24 of type int\n", "\\begin{tabular}{r|llllllllllllllllllllllll}\n", " & 1\\_2019\\_P\\_M1 & 2\\_2019\\_P\\_M1 & 3\\_2019\\_P\\_M1 & 4\\_2019\\_P\\_M1 & 5\\_2019\\_P\\_M1 & 6\\_2019\\_P\\_M1 & 7\\_2019\\_P\\_M1 & 8\\_2019\\_P\\_M1 & 9\\_2019\\_P\\_M1 & 10\\_2019\\_P\\_M1 & 11\\_2019\\_P\\_M1 & 12\\_2019\\_P\\_M1 & 13\\_2019\\_P\\_M1 & 14\\_2019\\_P\\_M1 & 15\\_2019\\_P\\_M1 & 16\\_2019\\_P\\_M1 & 17\\_2019\\_P\\_M1 & 18\\_2019\\_P\\_M1 & 19\\_2019\\_P\\_M1 & 20\\_2019\\_P\\_M1 & 21\\_2019\\_P\\_M1 & 22\\_2019\\_P\\_M1 & 23\\_2019\\_P\\_M1 & 24\\_2019\\_P\\_M1\\\\\n", "\\hline\n", "\tCNAG\\_00001 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n", "\tCNAG\\_00002 & 158 & 204 & 149 & 176 & 161 & 148 & 172 & 169 & 124 & 119 & 90 & 81 & 188 & 177 & 216 & 224 & 234 & 338 & 191 & 201 & 192 & 166 & 202 & 235\\\\\n", "\tCNAG\\_00003 & 201 & 156 & 161 & 171 & 162 & 103 & 172 & 170 & 175 & 131 & 121 & 151 & 215 & 154 & 197 & 195 & 211 & 201 & 118 & 133 & 109 & 113 & 154 & 169\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 24 of type int\n", "\n", "| | 1_2019_P_M1 | 2_2019_P_M1 | 3_2019_P_M1 | 4_2019_P_M1 | 5_2019_P_M1 | 6_2019_P_M1 | 7_2019_P_M1 | 8_2019_P_M1 | 9_2019_P_M1 | 10_2019_P_M1 | ⋯ | 15_2019_P_M1 | 16_2019_P_M1 | 17_2019_P_M1 | 18_2019_P_M1 | 19_2019_P_M1 | 20_2019_P_M1 | 21_2019_P_M1 | 22_2019_P_M1 | 23_2019_P_M1 | 24_2019_P_M1 |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| CNAG_00001 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n", "| CNAG_00002 | 158 | 204 | 149 | 176 | 161 | 148 | 172 | 169 | 124 | 119 | ⋯ | 216 | 224 | 234 | 338 | 191 | 201 | 192 | 166 | 202 | 235 |\n", "| CNAG_00003 | 201 | 156 | 161 | 171 | 162 | 103 | 172 | 170 | 175 | 131 | ⋯ | 197 | 195 | 211 | 201 | 118 | 133 | 109 | 113 | 154 | 169 |\n", "\n" ], "text/plain": [ " 1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1\n", "CNAG_00001 0 0 0 0 0 \n", "CNAG_00002 158 204 149 176 161 \n", "CNAG_00003 201 156 161 171 162 \n", " 6_2019_P_M1 7_2019_P_M1 8_2019_P_M1 9_2019_P_M1 10_2019_P_M1 ⋯\n", "CNAG_00001 0 0 0 0 0 ⋯\n", "CNAG_00002 148 172 169 124 119 ⋯\n", "CNAG_00003 103 172 170 175 131 ⋯\n", " 15_2019_P_M1 16_2019_P_M1 17_2019_P_M1 18_2019_P_M1 19_2019_P_M1\n", "CNAG_00001 0 0 0 0 0 \n", "CNAG_00002 216 224 234 338 191 \n", "CNAG_00003 197 195 211 201 118 \n", " 20_2019_P_M1 21_2019_P_M1 22_2019_P_M1 23_2019_P_M1 24_2019_P_M1\n", "CNAG_00001 0 0 0 0 0 \n", "CNAG_00002 201 192 166 202 235 \n", "CNAG_00003 133 109 113 154 169 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(counts(dds2019),3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This slot returns gene specific information (it will be populated later)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
function () \n",
       "NULL
" ], "text/latex": [ "\\begin{minted}{r}\n", "function () \n", "NULL\n", "\\end{minted}" ], "text/markdown": [ "```r\n", "function () \n", "NULL\n", "```" ], "text/plain": [ "function () \n", "NULL\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019@dispersionFunction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate Size Factors and Dispersion Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You recall that DESeq requires that we have estimates for sample specific size factors and gene specific dispersion factors. More specifically, recall that DESeq models the count $K_{ij}$ (gene $i$, sample $j$) as negative binomial with mean $\\mu_{ij}$ and dispersion parameter $\\alpha_i$. Here $\\mu_{ij}=s_j q_{ij}$ where $\\log_2(q_{ij}) = \\beta_{0i} + \\beta_{1i} z_j$. Here $s_j$ is the sample $j$ specific size factor.\n", "\n", "**Summarize of notation**\n", "- $K_{ij}$ denotes the observed **number of reads** mapped to gene $i$ for sample $j$\n", "- $K_{ij}$ follows a **negative binomial distribution** with\n", " - **Mean** $\\mu_{ij}$\n", " - **Dispersion parameter** $\\alpha_i$\n", "- Modelling\n", " - $K_{ij} \\sim NB(\\mu_{ij}, \\alpha_i)$\n", " - $\\mu_{ij} = s_{j}q_{ij}$\n", " - $s_j$ is sample $j$ specific normalization constant\n", " - $\\log_2(q_{ij}) = \\beta_{0i} + \\beta_{1i} z_j$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 01 Size Factors\n", " We begin by estimating the size factors $s_1,\\ldots,s_n$:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dds2019 <- estimateSizeFactors(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, compare the dds object to that of before applying the estimateSizeFactors() function. What has changed? What remains unchanged?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(1): counts\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(0):\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is a **sizeFactor** added to **colData**. Let's look at it more carefully\n", "\n", "```\n", "> dds # (before estimateSizeFactors)\n", "class: DESeqDataSet \n", "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(1): counts\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(0):\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(22): Label sample_year ... RIN_normal_threshold\n", " RIN_lowered_threshold\n", "\n", "> dds # (after estimateSizeFactors)\n", "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(1): counts\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(0):\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also get the size factors directly" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1_2019_P_M1
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1.5916393221245
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2_2019_P_M1
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1.65871103315141
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3_2019_P_M1
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1.51289455728943
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4_2019_P_M1
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1.41966861598247
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5_2019_P_M1
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1.43738473025559
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6_2019_P_M1
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1.23710002282889
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1.40148291158442
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8_2019_P_M1
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1.44217254922035
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9_2019_P_M1
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1.17444094436861
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10_2019_P_M1
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1.07636468283386
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11_2019_P_M1
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1.04216270302148
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12_2019_P_M1
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0.971238282275506
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13_2019_P_M1
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0.770986843971703
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14_2019_P_M1
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0.752382049912615
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15_2019_P_M1
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0.717679610028228
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16_2019_P_M1
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0.897840290244175
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17_2019_P_M1
\n", "\t\t
0.89364164432414
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18_2019_P_M1
\n", "\t\t
0.931618100592125
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19_2019_P_M1
\n", "\t\t
0.749435710766038
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20_2019_P_M1
\n", "\t\t
0.775571527912074
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21_2019_P_M1
\n", "\t\t
0.583832529967166
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22_2019_P_M1
\n", "\t\t
0.634399759403834
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23_2019_P_M1
\n", "\t\t
0.755186085659437
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24_2019_P_M1
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0.808836405192349
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\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.5916393221245\n", "\\item[2\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.65871103315141\n", "\\item[3\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.51289455728943\n", "\\item[4\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.41966861598247\n", "\\item[5\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.43738473025559\n", "\\item[6\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.23710002282889\n", "\\item[7\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.40148291158442\n", "\\item[8\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.44217254922035\n", "\\item[9\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.17444094436861\n", "\\item[10\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.07636468283386\n", "\\item[11\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.04216270302148\n", "\\item[12\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.971238282275506\n", "\\item[13\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.770986843971703\n", "\\item[14\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.752382049912615\n", "\\item[15\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.717679610028228\n", "\\item[16\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.897840290244175\n", "\\item[17\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.89364164432414\n", "\\item[18\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.931618100592125\n", "\\item[19\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.749435710766038\n", "\\item[20\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.775571527912074\n", "\\item[21\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.583832529967166\n", "\\item[22\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.634399759403834\n", "\\item[23\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.755186085659437\n", "\\item[24\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.808836405192349\n", "\\end{description*}\n" ], "text/markdown": [ "1_2019_P_M1\n", ": 1.59163932212452_2019_P_M1\n", ": 1.658711033151413_2019_P_M1\n", ": 1.512894557289434_2019_P_M1\n", ": 1.419668615982475_2019_P_M1\n", ": 1.437384730255596_2019_P_M1\n", ": 1.237100022828897_2019_P_M1\n", ": 1.401482911584428_2019_P_M1\n", ": 1.442172549220359_2019_P_M1\n", ": 1.1744409443686110_2019_P_M1\n", ": 1.0763646828338611_2019_P_M1\n", ": 1.0421627030214812_2019_P_M1\n", ": 0.97123828227550613_2019_P_M1\n", ": 0.77098684397170314_2019_P_M1\n", ": 0.75238204991261515_2019_P_M1\n", ": 0.71767961002822816_2019_P_M1\n", ": 0.89784029024417517_2019_P_M1\n", ": 0.8936416443241418_2019_P_M1\n", ": 0.93161810059212519_2019_P_M1\n", ": 0.74943571076603820_2019_P_M1\n", ": 0.77557152791207421_2019_P_M1\n", ": 0.58383252996716622_2019_P_M1\n", ": 0.63439975940383423_2019_P_M1\n", ": 0.75518608565943724_2019_P_M1\n", ": 0.808836405192349\n", "\n" ], "text/plain": [ " 1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1 6_2019_P_M1 \n", " 1.5916393 1.6587110 1.5128946 1.4196686 1.4373847 1.2371000 \n", " 7_2019_P_M1 8_2019_P_M1 9_2019_P_M1 10_2019_P_M1 11_2019_P_M1 12_2019_P_M1 \n", " 1.4014829 1.4421725 1.1744409 1.0763647 1.0421627 0.9712383 \n", "13_2019_P_M1 14_2019_P_M1 15_2019_P_M1 16_2019_P_M1 17_2019_P_M1 18_2019_P_M1 \n", " 0.7709868 0.7523820 0.7176796 0.8978403 0.8936416 0.9316181 \n", "19_2019_P_M1 20_2019_P_M1 21_2019_P_M1 22_2019_P_M1 23_2019_P_M1 24_2019_P_M1 \n", " 0.7494357 0.7755715 0.5838325 0.6343998 0.7551861 0.8088364 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sizeFactors(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " It is preferable to limit the number of decimal places. Next show the size factors rounded to 3 decimal places" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1_2019_P_M1
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1.592
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2_2019_P_M1
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1.659
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3_2019_P_M1
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1.513
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4_2019_P_M1
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1.42
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5_2019_P_M1
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1.437
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6_2019_P_M1
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1.237
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7_2019_P_M1
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1.401
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8_2019_P_M1
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1.442
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9_2019_P_M1
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1.174
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10_2019_P_M1
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1.076
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11_2019_P_M1
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1.042
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12_2019_P_M1
\n", "\t\t
0.971
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13_2019_P_M1
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0.771
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14_2019_P_M1
\n", "\t\t
0.752
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15_2019_P_M1
\n", "\t\t
0.718
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16_2019_P_M1
\n", "\t\t
0.898
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17_2019_P_M1
\n", "\t\t
0.894
\n", "\t
18_2019_P_M1
\n", "\t\t
0.932
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19_2019_P_M1
\n", "\t\t
0.749
\n", "\t
20_2019_P_M1
\n", "\t\t
0.776
\n", "\t
21_2019_P_M1
\n", "\t\t
0.584
\n", "\t
22_2019_P_M1
\n", "\t\t
0.634
\n", "\t
23_2019_P_M1
\n", "\t\t
0.755
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24_2019_P_M1
\n", "\t\t
0.809
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\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.592\n", "\\item[2\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.659\n", "\\item[3\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.513\n", "\\item[4\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.42\n", "\\item[5\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.437\n", "\\item[6\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.237\n", "\\item[7\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.401\n", "\\item[8\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.442\n", "\\item[9\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.174\n", "\\item[10\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.076\n", "\\item[11\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.042\n", "\\item[12\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.971\n", "\\item[13\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.771\n", "\\item[14\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.752\n", "\\item[15\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.718\n", "\\item[16\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.898\n", "\\item[17\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.894\n", "\\item[18\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.932\n", "\\item[19\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.749\n", "\\item[20\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.776\n", "\\item[21\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.584\n", "\\item[22\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.634\n", "\\item[23\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.755\n", "\\item[24\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.809\n", "\\end{description*}\n" ], "text/markdown": [ "1_2019_P_M1\n", ": 1.5922_2019_P_M1\n", ": 1.6593_2019_P_M1\n", ": 1.5134_2019_P_M1\n", ": 1.425_2019_P_M1\n", ": 1.4376_2019_P_M1\n", ": 1.2377_2019_P_M1\n", ": 1.4018_2019_P_M1\n", ": 1.4429_2019_P_M1\n", ": 1.17410_2019_P_M1\n", ": 1.07611_2019_P_M1\n", ": 1.04212_2019_P_M1\n", ": 0.97113_2019_P_M1\n", ": 0.77114_2019_P_M1\n", ": 0.75215_2019_P_M1\n", ": 0.71816_2019_P_M1\n", ": 0.89817_2019_P_M1\n", ": 0.89418_2019_P_M1\n", ": 0.93219_2019_P_M1\n", ": 0.74920_2019_P_M1\n", ": 0.77621_2019_P_M1\n", ": 0.58422_2019_P_M1\n", ": 0.63423_2019_P_M1\n", ": 0.75524_2019_P_M1\n", ": 0.809\n", "\n" ], "text/plain": [ " 1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1 6_2019_P_M1 \n", " 1.592 1.659 1.513 1.420 1.437 1.237 \n", " 7_2019_P_M1 8_2019_P_M1 9_2019_P_M1 10_2019_P_M1 11_2019_P_M1 12_2019_P_M1 \n", " 1.401 1.442 1.174 1.076 1.042 0.971 \n", "13_2019_P_M1 14_2019_P_M1 15_2019_P_M1 16_2019_P_M1 17_2019_P_M1 18_2019_P_M1 \n", " 0.771 0.752 0.718 0.898 0.894 0.932 \n", "19_2019_P_M1 20_2019_P_M1 21_2019_P_M1 22_2019_P_M1 23_2019_P_M1 24_2019_P_M1 \n", " 0.749 0.776 0.584 0.634 0.755 0.809 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round(sizeFactors(dds2019),3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarize size factors" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.5838 0.7670 0.9514 1.0515 1.4060 1.6587 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(sizeFactors(dds2019))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do you see a trend?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 24 × 3
rownamesizefaclibnum
<chr><dbl><int>
1_2019_P_M1 1.5916393 1
2_2019_P_M1 1.6587110 2
3_2019_P_M1 1.5128946 3
4_2019_P_M1 1.4196686 4
5_2019_P_M1 1.4373847 5
6_2019_P_M1 1.2371000 6
7_2019_P_M1 1.4014829 7
8_2019_P_M1 1.4421725 8
9_2019_P_M1 1.1744409 9
10_2019_P_M11.076364710
11_2019_P_M11.042162711
12_2019_P_M10.971238312
13_2019_P_M10.770986813
14_2019_P_M10.752382014
15_2019_P_M10.717679615
16_2019_P_M10.897840316
17_2019_P_M10.893641617
18_2019_P_M10.931618118
19_2019_P_M10.749435719
20_2019_P_M10.775571520
21_2019_P_M10.583832521
22_2019_P_M10.634399822
23_2019_P_M10.755186123
24_2019_P_M10.808836424
\n" ], "text/latex": [ "A data.frame: 24 × 3\n", "\\begin{tabular}{r|lll}\n", " rowname & sizefac & libnum\\\\\n", " & & \\\\\n", "\\hline\n", "\t 1\\_2019\\_P\\_M1 & 1.5916393 & 1\\\\\n", "\t 2\\_2019\\_P\\_M1 & 1.6587110 & 2\\\\\n", "\t 3\\_2019\\_P\\_M1 & 1.5128946 & 3\\\\\n", "\t 4\\_2019\\_P\\_M1 & 1.4196686 & 4\\\\\n", "\t 5\\_2019\\_P\\_M1 & 1.4373847 & 5\\\\\n", "\t 6\\_2019\\_P\\_M1 & 1.2371000 & 6\\\\\n", "\t 7\\_2019\\_P\\_M1 & 1.4014829 & 7\\\\\n", "\t 8\\_2019\\_P\\_M1 & 1.4421725 & 8\\\\\n", "\t 9\\_2019\\_P\\_M1 & 1.1744409 & 9\\\\\n", "\t 10\\_2019\\_P\\_M1 & 1.0763647 & 10\\\\\n", "\t 11\\_2019\\_P\\_M1 & 1.0421627 & 11\\\\\n", "\t 12\\_2019\\_P\\_M1 & 0.9712383 & 12\\\\\n", "\t 13\\_2019\\_P\\_M1 & 0.7709868 & 13\\\\\n", "\t 14\\_2019\\_P\\_M1 & 0.7523820 & 14\\\\\n", "\t 15\\_2019\\_P\\_M1 & 0.7176796 & 15\\\\\n", "\t 16\\_2019\\_P\\_M1 & 0.8978403 & 16\\\\\n", "\t 17\\_2019\\_P\\_M1 & 0.8936416 & 17\\\\\n", "\t 18\\_2019\\_P\\_M1 & 0.9316181 & 18\\\\\n", "\t 19\\_2019\\_P\\_M1 & 0.7494357 & 19\\\\\n", "\t 20\\_2019\\_P\\_M1 & 0.7755715 & 20\\\\\n", "\t 21\\_2019\\_P\\_M1 & 0.5838325 & 21\\\\\n", "\t 22\\_2019\\_P\\_M1 & 0.6343998 & 22\\\\\n", "\t 23\\_2019\\_P\\_M1 & 0.7551861 & 23\\\\\n", "\t 24\\_2019\\_P\\_M1 & 0.8088364 & 24\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 24 × 3\n", "\n", "| rowname <chr> | sizefac <dbl> | libnum <int> |\n", "|---|---|---|\n", "| 1_2019_P_M1 | 1.5916393 | 1 |\n", "| 2_2019_P_M1 | 1.6587110 | 2 |\n", "| 3_2019_P_M1 | 1.5128946 | 3 |\n", "| 4_2019_P_M1 | 1.4196686 | 4 |\n", "| 5_2019_P_M1 | 1.4373847 | 5 |\n", "| 6_2019_P_M1 | 1.2371000 | 6 |\n", "| 7_2019_P_M1 | 1.4014829 | 7 |\n", "| 8_2019_P_M1 | 1.4421725 | 8 |\n", "| 9_2019_P_M1 | 1.1744409 | 9 |\n", "| 10_2019_P_M1 | 1.0763647 | 10 |\n", "| 11_2019_P_M1 | 1.0421627 | 11 |\n", "| 12_2019_P_M1 | 0.9712383 | 12 |\n", "| 13_2019_P_M1 | 0.7709868 | 13 |\n", "| 14_2019_P_M1 | 0.7523820 | 14 |\n", "| 15_2019_P_M1 | 0.7176796 | 15 |\n", "| 16_2019_P_M1 | 0.8978403 | 16 |\n", "| 17_2019_P_M1 | 0.8936416 | 17 |\n", "| 18_2019_P_M1 | 0.9316181 | 18 |\n", "| 19_2019_P_M1 | 0.7494357 | 19 |\n", "| 20_2019_P_M1 | 0.7755715 | 20 |\n", "| 21_2019_P_M1 | 0.5838325 | 21 |\n", "| 22_2019_P_M1 | 0.6343998 | 22 |\n", "| 23_2019_P_M1 | 0.7551861 | 23 |\n", "| 24_2019_P_M1 | 0.8088364 | 24 |\n", "\n" ], "text/plain": [ " rowname sizefac libnum\n", "1 1_2019_P_M1 1.5916393 1 \n", "2 2_2019_P_M1 1.6587110 2 \n", "3 3_2019_P_M1 1.5128946 3 \n", "4 4_2019_P_M1 1.4196686 4 \n", "5 5_2019_P_M1 1.4373847 5 \n", "6 6_2019_P_M1 1.2371000 6 \n", "7 7_2019_P_M1 1.4014829 7 \n", "8 8_2019_P_M1 1.4421725 8 \n", "9 9_2019_P_M1 1.1744409 9 \n", "10 10_2019_P_M1 1.0763647 10 \n", "11 11_2019_P_M1 1.0421627 11 \n", "12 12_2019_P_M1 0.9712383 12 \n", "13 13_2019_P_M1 0.7709868 13 \n", "14 14_2019_P_M1 0.7523820 14 \n", "15 15_2019_P_M1 0.7176796 15 \n", "16 16_2019_P_M1 0.8978403 16 \n", "17 17_2019_P_M1 0.8936416 17 \n", "18 18_2019_P_M1 0.9316181 18 \n", "19 19_2019_P_M1 0.7494357 19 \n", "20 20_2019_P_M1 0.7755715 20 \n", "21 21_2019_P_M1 0.5838325 21 \n", "22 22_2019_P_M1 0.6343998 22 \n", "23 23_2019_P_M1 0.7551861 23 \n", "24 24_2019_P_M1 0.8088364 24 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sizeFactors(dds2019) %>%\n", " as.data.frame %>%\n", " rownames_to_column %>%\n", " mutate(libnum = as.integer(str_remove(rowname, \"_2019_P_M1\"))) -> mydf\n", "\n", "colnames(mydf)[2] <- \"sizefac\"\n", "\n", "mydf" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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diotkLIM2J7sFFdrr105oeDtGPHjg7XsyNYveFGFVy4WT2dwUZ1u65gs7rywUZ1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\nSUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(mydf, aes(x = libnum, y = sizefac)) + geom_point()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the size factors have been estimated, we can get \"normalized\" counts (DESeq2 normalizes against size factor)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 5 × 2 of type int
1_2019_P_M124_2019_P_M1
CNAG_00001 0 0
CNAG_00002158235
CNAG_00003201169
CNAG_00004904523
CNAG_00005 22 40
\n" ], "text/latex": [ "A matrix: 5 × 2 of type int\n", "\\begin{tabular}{r|ll}\n", " & 1\\_2019\\_P\\_M1 & 24\\_2019\\_P\\_M1\\\\\n", "\\hline\n", "\tCNAG\\_00001 & 0 & 0\\\\\n", "\tCNAG\\_00002 & 158 & 235\\\\\n", "\tCNAG\\_00003 & 201 & 169\\\\\n", "\tCNAG\\_00004 & 904 & 523\\\\\n", "\tCNAG\\_00005 & 22 & 40\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 5 × 2 of type int\n", "\n", "| | 1_2019_P_M1 | 24_2019_P_M1 |\n", "|---|---|---|\n", "| CNAG_00001 | 0 | 0 |\n", "| CNAG_00002 | 158 | 235 |\n", "| CNAG_00003 | 201 | 169 |\n", "| CNAG_00004 | 904 | 523 |\n", "| CNAG_00005 | 22 | 40 |\n", "\n" ], "text/plain": [ " 1_2019_P_M1 24_2019_P_M1\n", "CNAG_00001 0 0 \n", "CNAG_00002 158 235 \n", "CNAG_00003 201 169 \n", "CNAG_00004 904 523 \n", "CNAG_00005 22 40 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 5 × 2 of type dbl
1_2019_P_M124_2019_P_M1
CNAG_00001 0.00000 0.00000
CNAG_00002 99.26872290.54083
CNAG_00003126.28489208.94213
CNAG_00004567.96787646.60789
CNAG_00005 13.82223 49.45376
\n" ], "text/latex": [ "A matrix: 5 × 2 of type dbl\n", "\\begin{tabular}{r|ll}\n", " & 1\\_2019\\_P\\_M1 & 24\\_2019\\_P\\_M1\\\\\n", "\\hline\n", "\tCNAG\\_00001 & 0.00000 & 0.00000\\\\\n", "\tCNAG\\_00002 & 99.26872 & 290.54083\\\\\n", "\tCNAG\\_00003 & 126.28489 & 208.94213\\\\\n", "\tCNAG\\_00004 & 567.96787 & 646.60789\\\\\n", "\tCNAG\\_00005 & 13.82223 & 49.45376\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 5 × 2 of type dbl\n", "\n", "| | 1_2019_P_M1 | 24_2019_P_M1 |\n", "|---|---|---|\n", "| CNAG_00001 | 0.00000 | 0.00000 |\n", "| CNAG_00002 | 99.26872 | 290.54083 |\n", "| CNAG_00003 | 126.28489 | 208.94213 |\n", "| CNAG_00004 | 567.96787 | 646.60789 |\n", "| CNAG_00005 | 13.82223 | 49.45376 |\n", "\n" ], "text/plain": [ " 1_2019_P_M1 24_2019_P_M1\n", "CNAG_00001 0.00000 0.00000 \n", "CNAG_00002 99.26872 290.54083 \n", "CNAG_00003 126.28489 208.94213 \n", "CNAG_00004 567.96787 646.60789 \n", "CNAG_00005 13.82223 49.45376 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\t
1_2019_P_M1
\n", "\t\t
1.5916393221245
\n", "\t
24_2019_P_M1
\n", "\t\t
0.808836405192349
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1.5916393221245\n", "\\item[24\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 0.808836405192349\n", "\\end{description*}\n" ], "text/markdown": [ "1_2019_P_M1\n", ": 1.591639322124524_2019_P_M1\n", ": 0.808836405192349\n", "\n" ], "text/plain": [ " 1_2019_P_M1 24_2019_P_M1 \n", " 1.5916393 0.8088364 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# original counts for libraries 1 and 24\n", "counts(dds2019)[1:5,c(1,24)]\n", "\n", "# normalized count\n", "counts(dds2019, normalize = TRUE)[1:5, c(1,24)]\n", "\n", "# Size factor\n", "\n", "sizeFactors(dds2019)[c(1,24)]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
CNAG_00001
\n", "\t\t
0
\n", "\t
CNAG_00002
\n", "\t\t
99.2687211252757
\n", "\t
CNAG_00003
\n", "\t\t
126.284892064433
\n", "\t
CNAG_00004
\n", "\t\t
567.9678727674
\n", "\t
CNAG_00005
\n", "\t\t
13.822226992127
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[CNAG\\textbackslash{}\\_00001] 0\n", "\\item[CNAG\\textbackslash{}\\_00002] 99.2687211252757\n", "\\item[CNAG\\textbackslash{}\\_00003] 126.284892064433\n", "\\item[CNAG\\textbackslash{}\\_00004] 567.9678727674\n", "\\item[CNAG\\textbackslash{}\\_00005] 13.822226992127\n", "\\end{description*}\n" ], "text/markdown": [ "CNAG_00001\n", ": 0CNAG_00002\n", ": 99.2687211252757CNAG_00003\n", ": 126.284892064433CNAG_00004\n", ": 567.9678727674CNAG_00005\n", ": 13.822226992127\n", "\n" ], "text/plain": [ "CNAG_00001 CNAG_00002 CNAG_00003 CNAG_00004 CNAG_00005 \n", " 0.00000 99.26872 126.28489 567.96787 13.82223 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# normalized manually using size factors for library 1\n", "counts(dds2019)[1:5, 1] / sizeFactors(dds2019)[1]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
CNAG_00001
\n", "\t\t
0
\n", "\t
CNAG_00002
\n", "\t\t
290.54082938331
\n", "\t
CNAG_00003
\n", "\t\t
208.942128365019
\n", "\t
CNAG_00004
\n", "\t\t
646.607888372217
\n", "\t
CNAG_00005
\n", "\t\t
49.4537581929038
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[CNAG\\textbackslash{}\\_00001] 0\n", "\\item[CNAG\\textbackslash{}\\_00002] 290.54082938331\n", "\\item[CNAG\\textbackslash{}\\_00003] 208.942128365019\n", "\\item[CNAG\\textbackslash{}\\_00004] 646.607888372217\n", "\\item[CNAG\\textbackslash{}\\_00005] 49.4537581929038\n", "\\end{description*}\n" ], "text/markdown": [ "CNAG_00001\n", ": 0CNAG_00002\n", ": 290.54082938331CNAG_00003\n", ": 208.942128365019CNAG_00004\n", ": 646.607888372217CNAG_00005\n", ": 49.4537581929038\n", "\n" ], "text/plain": [ "CNAG_00001 CNAG_00002 CNAG_00003 CNAG_00004 CNAG_00005 \n", " 0.00000 290.54083 208.94213 646.60789 49.45376 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# normalized manually using size factors for library 24\n", "counts(dds2019)[1:5, 24] / sizeFactors(dds2019)[24]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do you get the raw counts for gene \"GeneID: CNAG_05845\"?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
1_2019_P_M1
\n", "\t\t
1753.53797886484
\n", "\t
2_2019_P_M1
\n", "\t\t
1483.68217900155
\n", "\t
3_2019_P_M1
\n", "\t\t
1395.99946990619
\n", "\t
4_2019_P_M1
\n", "\t\t
1526.41255544016
\n", "\t
5_2019_P_M1
\n", "\t\t
1540.99313383281
\n", "\t
6_2019_P_M1
\n", "\t\t
1662.7596492127
\n", "\t
7_2019_P_M1
\n", "\t\t
1483.42871883441
\n", "\t
8_2019_P_M1
\n", "\t\t
1396.50418466832
\n", "\t
9_2019_P_M1
\n", "\t\t
1611.83072599508
\n", "\t
10_2019_P_M1
\n", "\t\t
1532.00864567435
\n", "\t
11_2019_P_M1
\n", "\t\t
1625.46596139805
\n", "\t
12_2019_P_M1
\n", "\t\t
1693.71412764532
\n", "\t
13_2019_P_M1
\n", "\t\t
1629.08097566201
\n", "\t
14_2019_P_M1
\n", "\t\t
1600.25083019968
\n", "\t
15_2019_P_M1
\n", "\t\t
1574.51874654145
\n", "\t
16_2019_P_M1
\n", "\t\t
1551.50087953968
\n", "\t
17_2019_P_M1
\n", "\t\t
1732.23798357198
\n", "\t
18_2019_P_M1
\n", "\t\t
1523.15632242235
\n", "\t
19_2019_P_M1
\n", "\t\t
1822.70471019287
\n", "\t
20_2019_P_M1
\n", "\t\t
1799.96293025118
\n", "\t
21_2019_P_M1
\n", "\t\t
1872.1121963887
\n", "\t
22_2019_P_M1
\n", "\t\t
1692.93885137862
\n", "\t
23_2019_P_M1
\n", "\t\t
1832.66088488836
\n", "\t
24_2019_P_M1
\n", "\t\t
1791.46239053794
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[1\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1753.53797886484\n", "\\item[2\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1483.68217900155\n", "\\item[3\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1395.99946990619\n", "\\item[4\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1526.41255544016\n", "\\item[5\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1540.99313383281\n", "\\item[6\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1662.7596492127\n", "\\item[7\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1483.42871883441\n", "\\item[8\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1396.50418466832\n", "\\item[9\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1611.83072599508\n", "\\item[10\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1532.00864567435\n", "\\item[11\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1625.46596139805\n", "\\item[12\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1693.71412764532\n", "\\item[13\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1629.08097566201\n", "\\item[14\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1600.25083019968\n", "\\item[15\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1574.51874654145\n", "\\item[16\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1551.50087953968\n", "\\item[17\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1732.23798357198\n", "\\item[18\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1523.15632242235\n", "\\item[19\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1822.70471019287\n", "\\item[20\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1799.96293025118\n", "\\item[21\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1872.1121963887\n", "\\item[22\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1692.93885137862\n", "\\item[23\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1832.66088488836\n", "\\item[24\\textbackslash{}\\_2019\\textbackslash{}\\_P\\textbackslash{}\\_M1] 1791.46239053794\n", "\\end{description*}\n" ], "text/markdown": [ "1_2019_P_M1\n", ": 1753.537978864842_2019_P_M1\n", ": 1483.682179001553_2019_P_M1\n", ": 1395.999469906194_2019_P_M1\n", ": 1526.412555440165_2019_P_M1\n", ": 1540.993133832816_2019_P_M1\n", ": 1662.75964921277_2019_P_M1\n", ": 1483.428718834418_2019_P_M1\n", ": 1396.504184668329_2019_P_M1\n", ": 1611.8307259950810_2019_P_M1\n", ": 1532.0086456743511_2019_P_M1\n", ": 1625.4659613980512_2019_P_M1\n", ": 1693.7141276453213_2019_P_M1\n", ": 1629.0809756620114_2019_P_M1\n", ": 1600.2508301996815_2019_P_M1\n", ": 1574.5187465414516_2019_P_M1\n", ": 1551.5008795396817_2019_P_M1\n", ": 1732.2379835719818_2019_P_M1\n", ": 1523.1563224223519_2019_P_M1\n", ": 1822.7047101928720_2019_P_M1\n", ": 1799.9629302511821_2019_P_M1\n", ": 1872.112196388722_2019_P_M1\n", ": 1692.9388513786223_2019_P_M1\n", ": 1832.6608848883624_2019_P_M1\n", ": 1791.46239053794\n", "\n" ], "text/plain": [ " 1_2019_P_M1 2_2019_P_M1 3_2019_P_M1 4_2019_P_M1 5_2019_P_M1 6_2019_P_M1 \n", " 1753.538 1483.682 1395.999 1526.413 1540.993 1662.760 \n", " 7_2019_P_M1 8_2019_P_M1 9_2019_P_M1 10_2019_P_M1 11_2019_P_M1 12_2019_P_M1 \n", " 1483.429 1396.504 1611.831 1532.009 1625.466 1693.714 \n", "13_2019_P_M1 14_2019_P_M1 15_2019_P_M1 16_2019_P_M1 17_2019_P_M1 18_2019_P_M1 \n", " 1629.081 1600.251 1574.519 1551.501 1732.238 1523.156 \n", "19_2019_P_M1 20_2019_P_M1 21_2019_P_M1 22_2019_P_M1 23_2019_P_M1 24_2019_P_M1 \n", " 1822.705 1799.963 1872.112 1692.939 1832.661 1791.462 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "counts(dds2019, normalize = TRUE)[\"CNAG_05845\",]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 02 Dispersion Parameters\n", "Next, we get the dispersion factors $\\alpha_1,\\ldots,\\alpha_{m}$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "gene-wise dispersion estimates\n", "mean-dispersion relationship\n", "final dispersion estimates\n" ] } ], "source": [ "dds2019 <- estimateDispersions(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now inspect the dds object again and note that the rowRanges slot has extra information (\"metadata column names(0):\" before versus \"column names(9): baseMean baseVar ... dispOutlier dispMAP\")\n", "- before: \n", " - `metadata column names(0):`\n", "- after: \n", " - `column names(9): baseMean baseVar ...`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(2): counts mu\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(10): baseMean baseVar ... dispOutlier dispMAP\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can you notice the difference?\n", "```\n", "> dds (before dispersion)\n", "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(1): counts\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(0):\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor\n", " \n", "> dds (after dispersion)\n", "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(2): counts mu\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(10): baseMean baseVar ... dispOutlier dispMAP\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the dispersionfunction slot is now populated" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
structure(function (q) \n",
       "coefs[1] + coefs[2]/q, coefficients = c(asymptDisp = 0.0141803487671824, \n",
       "extraPois = 0.87630661036897), fitType = \"parametric\", varLogDispEsts = 0.902466256228483, dispPriorVar = 0.802549300169356)
" ], "text/latex": [ "\\begin{minted}{r}\n", "structure(function (q) \n", "coefs{[}1{]} + coefs{[}2{]}/q, coefficients = c(asymptDisp = 0.0141803487671824, \n", "extraPois = 0.87630661036897), fitType = \"parametric\", varLogDispEsts = 0.902466256228483, dispPriorVar = 0.802549300169356)\n", "\\end{minted}" ], "text/markdown": [ "```r\n", "structure(function (q) \n", "coefs[1] + coefs[2]/q, coefficients = c(asymptDisp = 0.0141803487671824, \n", "extraPois = 0.87630661036897), fitType = \"parametric\", varLogDispEsts = 0.902466256228483, dispPriorVar = 0.802549300169356)\n", "```" ], "text/plain": [ "function (q) \n", "coefs[1] + coefs[2]/q\n", "\n", "\n", "attr(,\"coefficients\")\n", "asymptDisp extraPois \n", "0.01418035 0.87630661 \n", "attr(,\"fitType\")\n", "[1] \"parametric\"\n", "attr(,\"varLogDispEsts\")\n", "[1] 0.9024663\n", "attr(,\"dispPriorVar\")\n", "[1] 0.8025493" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dds2019@dispersionFunction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can extract the gene specific dispersion factors using dispersions(). Note that there will be one number per gene. We look at the first four genes (rounded to 4 decimal places)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "alphas <- dispersions(dds2019)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify that the number of dispersion factors equals the number of genes" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "8499" ], "text/latex": [ "8499" ], "text/markdown": [ "8499" ], "text/plain": [ "[1] 8499" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# number of disperion factors\n", "length(alphas)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. <NA>
    2. \n", "\t
  2. 0.0132
    3. \n", "\t
  3. 0.0231
    4. \n", "\t
  4. 0.0044
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item \n", "\\item 0.0132\n", "\\item 0.0231\n", "\\item 0.0044\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. <NA>\n", "2. 0.0132\n", "3. 0.0231\n", "4. 0.0044\n", "\n", "\n" ], "text/plain": [ "[1] NA 0.0132 0.0231 0.0044" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round(alphas[1:4], 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the metadata using mcols() for the first four genes\n", "\n", "| Terms | Description |\n", "|-------------|-----------------------------------------------|\n", "| baseMean | mean of normalized counts for all samples |\n", "| baseVar | variance of normalized counts for all samples |\n", "| allZero | all counts for a gene are zero |\n", "| dispGeneEst | gene-wise estimates of dispersion |\n", "| dispFit | fitted values of dispersion |\n", "| dispersion | final estimate of dispersion |\n", "| dispIter | number of iterations |\n", "| dispOut | dispersion flagged as outlier |\n", "| dispMAP | maximum a posteriori estimate |\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 10
baseMeanbaseVarallZerodispGeneEstdispGeneIterdispFitdispersiondispIterdispOutlierdispMAP
<dbl><dbl><lgl><dbl><dbl><dbl><dbl><dbl><lgl><dbl>
CNAG_00001 0.0000 0.000 TRUE NANA NA NANA NA NA
CNAG_00002192.45648133.183FALSE0.012090962 90.018733620.013202310 9FALSE0.013202310
CNAG_00003164.99083089.489FALSE0.023820456100.019491590.023065791 7FALSE0.023065791
CNAG_00004571.05471819.185FALSE0.003107148 50.015714890.00443058612FALSE0.004430586
\n" ], "text/latex": [ "A data.frame: 4 × 10\n", "\\begin{tabular}{r|llllllllll}\n", " & baseMean & baseVar & allZero & dispGeneEst & dispGeneIter & dispFit & dispersion & dispIter & dispOutlier & dispMAP\\\\\n", " & & & & & & & & & & \\\\\n", "\\hline\n", "\tCNAG\\_00001 & 0.0000 & 0.000 & TRUE & NA & NA & NA & NA & NA & NA & NA\\\\\n", "\tCNAG\\_00002 & 192.4564 & 8133.183 & FALSE & 0.012090962 & 9 & 0.01873362 & 0.013202310 & 9 & FALSE & 0.013202310\\\\\n", "\tCNAG\\_00003 & 164.9908 & 3089.489 & FALSE & 0.023820456 & 10 & 0.01949159 & 0.023065791 & 7 & FALSE & 0.023065791\\\\\n", "\tCNAG\\_00004 & 571.0547 & 1819.185 & FALSE & 0.003107148 & 5 & 0.01571489 & 0.004430586 & 12 & FALSE & 0.004430586\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 10\n", "\n", "| | baseMean <dbl> | baseVar <dbl> | allZero <lgl> | dispGeneEst <dbl> | dispGeneIter <dbl> | dispFit <dbl> | dispersion <dbl> | dispIter <dbl> | dispOutlier <lgl> | dispMAP <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|---|\n", "| CNAG_00001 | 0.0000 | 0.000 | TRUE | NA | NA | NA | NA | NA | NA | NA |\n", "| CNAG_00002 | 192.4564 | 8133.183 | FALSE | 0.012090962 | 9 | 0.01873362 | 0.013202310 | 9 | FALSE | 0.013202310 |\n", "| CNAG_00003 | 164.9908 | 3089.489 | FALSE | 0.023820456 | 10 | 0.01949159 | 0.023065791 | 7 | FALSE | 0.023065791 |\n", "| CNAG_00004 | 571.0547 | 1819.185 | FALSE | 0.003107148 | 5 | 0.01571489 | 0.004430586 | 12 | FALSE | 0.004430586 |\n", "\n" ], "text/plain": [ " baseMean baseVar allZero dispGeneEst dispGeneIter dispFit \n", "CNAG_00001 0.0000 0.000 TRUE NA NA NA\n", "CNAG_00002 192.4564 8133.183 FALSE 0.012090962 9 0.01873362\n", "CNAG_00003 164.9908 3089.489 FALSE 0.023820456 10 0.01949159\n", "CNAG_00004 571.0547 1819.185 FALSE 0.003107148 5 0.01571489\n", " dispersion dispIter dispOutlier dispMAP \n", "CNAG_00001 NA NA NA NA\n", "CNAG_00002 0.013202310 9 FALSE 0.013202310\n", "CNAG_00003 0.023065791 7 FALSE 0.023065791\n", "CNAG_00004 0.004430586 12 FALSE 0.004430586" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mcols(dds2019)[1:4,] %>% as.data.frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarize the dispersion factors using a box plot (may want to log transform)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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tn1E1K+2kZOjurUU+P+80cSUoaQtt7NB8c1ZEjkAW6O/W8gEELyZfny2BMYRUiA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\nIQEKCAlQQEiAAkICFBASoICQAAX/C//faRKOnBURAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(log(dispersions(dds2019)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Differential Expression Analysis\n", "We can now conduct a differential expression analysis using the DESeq() function. Keep in mind that to get to this step, we first estimated the size factors and then the dispersion parameters." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "using pre-existing size factors\n", "estimating dispersions\n", "found already estimated dispersions, replacing these\n", "gene-wise dispersion estimates\n", "mean-dispersion relationship\n", "final dispersion estimates\n", "fitting model and testing\n" ] } ], "source": [ "### Carry out DE analysis\n", "ddsDE <- DESeq(dds2019)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "class: DESeqDataSet \n", "dim: 8499 24 \n", "metadata(1): version\n", "assays(4): counts mu H cooks\n", "rownames(8499): CNAG_00001 CNAG_00002 ... large_MTrRNA small_MTrRNA\n", "rowData names(26): baseMean baseVar ... deviance maxCooks\n", "colnames(24): 1_2019_P_M1 2_2019_P_M1 ... 23_2019_P_M1 24_2019_P_M1\n", "colData names(23): Label sample_year ... RIN_lowered_threshold\n", " sizeFactor" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Look at object\n", "ddsDE" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "log2 fold change (MLE): genotype WT vs sre1d \n", "Wald test p-value: genotype WT vs sre1d \n", "DataFrame with 8499 rows and 6 columns\n", " baseMean log2FoldChange lfcSE\n", " \n", "CNAG_00001 0 NA NA\n", "CNAG_00002 192.456384076694 0.0459290105698189 0.0812849125012254\n", "CNAG_00003 164.990819436254 0.0432979308487266 0.101045100070341\n", "CNAG_00004 571.054651467718 0.0347715410374543 0.0464312203030827\n", "CNAG_00005 28.7106195205257 -0.377056805602812 0.192347436642309\n", "... ... ... ...\n", "ENSRNA049551942 0 NA NA\n", "ENSRNA049551964 0 NA NA\n", "ENSRNA049551993 0 NA NA\n", "large_MTrRNA 5333.97356461621 -0.396337460272526 0.13434088145007\n", "small_MTrRNA 1705.75472334291 -0.436802837852531 0.295538981353034\n", " stat pvalue padj\n", " \n", "CNAG_00001 NA NA NA\n", "CNAG_00002 0.565037337883909 0.572048368119426 0.675469542016723\n", "CNAG_00003 0.428501043777338 0.668286373790347 0.756450803050288\n", "CNAG_00004 0.748882773497678 0.453927864041203 0.566263547566551\n", "CNAG_00005 -1.96029025488907 0.0499618744055703 0.094333851813714\n", "... ... ... ...\n", "ENSRNA049551942 NA NA NA\n", "ENSRNA049551964 NA NA NA\n", "ENSRNA049551993 NA NA NA\n", "large_MTrRNA -2.95023715785154 0.00317530091247218 0.00896807960414441\n", "small_MTrRNA -1.4779872213566 0.139411198621898 0.221714577729148" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Look at some of the results\n", "results(ddsDE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that currently, the model we have is an additive model, which does not include the interaction term of `Media` and `Strain`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Look at some of the results (tidy version)\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 8499 × 7
rowbaseMeanlog2FoldChangelfcSEstatpvaluepadj
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
CNAG_00001 0.00000 NA NA NA NA NA
CNAG_00002 192.45638 0.04592901060.08128491 0.5650373385.720484e-016.754695e-01
CNAG_00003 164.99082 0.04329793080.10104510 0.4285010446.682864e-017.564508e-01
CNAG_00004 571.05465 0.03477154100.04643122 0.7488827734.539279e-015.662635e-01
CNAG_00005 28.71062-0.37705680560.19234744 -1.9602902554.996187e-029.433385e-02
CNAG_000063147.39964-0.13136969270.04955489 -2.6509934578.025540e-031.995982e-02
CNAG_000071484.87545-0.25112143440.08051906 -3.1187824551.816000e-035.492473e-03
CNAG_00008 938.01932-0.00957755680.04760749 -0.2011775048.405598e-018.873725e-01
CNAG_00009 305.19881-0.18664588700.09952871 -1.8752969226.075189e-021.116044e-01
CNAG_00010 989.04227 0.29910115060.06322330 4.7308687972.235610e-061.474309e-05
CNAG_000111001.75238-0.01506299410.08611946 -0.1749081338.611518e-019.032619e-01
CNAG_00012 773.99385 0.19474879020.05765064 3.3780853377.299243e-042.477973e-03
CNAG_00013 485.75319-0.00028230690.07949652 -0.0035511859.971666e-019.974113e-01
CNAG_00014 180.53988 0.04893072500.06795183 0.7200795534.714760e-015.825377e-01
CNAG_00015 318.87538-0.52256276610.04948141-10.5607897344.528359e-266.591188e-24
CNAG_00016 901.25567-0.17439720990.04458282 -3.9117585499.162649e-053.953666e-04
CNAG_00017 184.65031 0.25011413670.08576066 2.9164204443.540730e-039.829867e-03
CNAG_00018 876.12980 0.05336718550.03737492 1.4278876781.533242e-012.399204e-01
CNAG_000191208.90542-0.03761997930.05412226 -0.6950924854.869973e-015.980888e-01
CNAG_00020 968.39030 0.16334678170.06202149 2.6337124988.445697e-032.079157e-02
CNAG_00021 539.92676 0.34559598600.05392145 6.4092492931.462379e-102.337226e-09
CNAG_00022 746.84601-0.03380024950.06149652 -0.5496286335.825741e-016.837382e-01
CNAG_00023 278.87826-1.46009376560.26611238 -5.4867562794.093813e-083.977196e-07
CNAG_000247703.04647-0.10749591950.04323925 -2.4860728151.291615e-023.001984e-02
CNAG_000252021.99612-0.09663889950.09609375 -1.0056730593.145729e-014.265652e-01
CNAG_000262237.77134-0.22179505750.06090899 -3.6414173392.711412e-041.034194e-03
CNAG_00027 425.60765 0.12268413780.06051990 2.0271700384.264503e-028.258484e-02
CNAG_00028 381.48552-0.10529428930.05837561 -1.8037376927.127244e-021.274834e-01
CNAG_00029 52.66523-0.06987920340.10076540 -0.6934841354.880058e-015.990298e-01
CNAG_00030 76.57328-0.32881123370.12296567 -2.6740084517.495056e-031.881006e-02
⋮⋮⋮⋮⋮⋮⋮
ENSRNA049550980 0.000 NA NA NA NA NA
ENSRNA049551037 0.000 NA NA NA NA NA
ENSRNA049551063 0.000 NA NA NA NA NA
ENSRNA049551074 0.000 NA NA NA NA NA
ENSRNA049551126 0.000 NA NA NA NA NA
ENSRNA049551197 0.000 NA NA NA NA NA
ENSRNA049551244 0.000 NA NA NA NA NA
ENSRNA049551269 0.000 NA NA NA NA NA
ENSRNA049551298 0.000 NA NA NA NA NA
ENSRNA049551337 0.000 NA NA NA NA NA
ENSRNA049551365 0.000 NA NA NA NA NA
ENSRNA049551391 0.000 NA NA NA NA NA
ENSRNA049551484 0.000 NA NA NA NA NA
ENSRNA049551550 0.000 NA NA NA NA NA
ENSRNA049551574 0.000 NA NA NA NA NA
ENSRNA049551598 0.000 NA NA NA NA NA
ENSRNA049551636 0.000 NA NA NA NA NA
ENSRNA049551673 0.000 NA NA NA NA NA
ENSRNA049551717 0.000 NA NA NA NA NA
ENSRNA049551745 0.000 NA NA NA NA NA
ENSRNA049551774 0.000 NA NA NA NA NA
ENSRNA049551798 0.000 NA NA NA NA NA
ENSRNA049551814 0.000 NA NA NA NA NA
ENSRNA049551862 0.000 NA NA NA NA NA
ENSRNA049551899 0.000 NA NA NA NA NA
ENSRNA049551942 0.000 NA NA NA NA NA
ENSRNA049551964 0.000 NA NA NA NA NA
ENSRNA049551993 0.000 NA NA NA NA NA
large_MTrRNA 5333.974-0.39633750.1343409-2.9502370.0031753010.00896808
small_MTrRNA 1705.755-0.43680280.2955390-1.4779870.1394111990.22171458
\n" ], "text/latex": [ "A data.frame: 8499 × 7\n", "\\begin{tabular}{r|lllllll}\n", " row & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t CNAG\\_00001 & 0.00000 & NA & NA & NA & NA & NA\\\\\n", "\t CNAG\\_00002 & 192.45638 & 0.0459290106 & 0.08128491 & 0.565037338 & 5.720484e-01 & 6.754695e-01\\\\\n", "\t CNAG\\_00003 & 164.99082 & 0.0432979308 & 0.10104510 & 0.428501044 & 6.682864e-01 & 7.564508e-01\\\\\n", "\t CNAG\\_00004 & 571.05465 & 0.0347715410 & 0.04643122 & 0.748882773 & 4.539279e-01 & 5.662635e-01\\\\\n", "\t CNAG\\_00005 & 28.71062 & -0.3770568056 & 0.19234744 & -1.960290255 & 4.996187e-02 & 9.433385e-02\\\\\n", "\t CNAG\\_00006 & 3147.39964 & -0.1313696927 & 0.04955489 & -2.650993457 & 8.025540e-03 & 1.995982e-02\\\\\n", "\t CNAG\\_00007 & 1484.87545 & -0.2511214344 & 0.08051906 & -3.118782455 & 1.816000e-03 & 5.492473e-03\\\\\n", "\t CNAG\\_00008 & 938.01932 & -0.0095775568 & 0.04760749 & -0.201177504 & 8.405598e-01 & 8.873725e-01\\\\\n", "\t CNAG\\_00009 & 305.19881 & -0.1866458870 & 0.09952871 & -1.875296922 & 6.075189e-02 & 1.116044e-01\\\\\n", "\t CNAG\\_00010 & 989.04227 & 0.2991011506 & 0.06322330 & 4.730868797 & 2.235610e-06 & 1.474309e-05\\\\\n", "\t CNAG\\_00011 & 1001.75238 & -0.0150629941 & 0.08611946 & -0.174908133 & 8.611518e-01 & 9.032619e-01\\\\\n", "\t CNAG\\_00012 & 773.99385 & 0.1947487902 & 0.05765064 & 3.378085337 & 7.299243e-04 & 2.477973e-03\\\\\n", "\t CNAG\\_00013 & 485.75319 & -0.0002823069 & 0.07949652 & -0.003551185 & 9.971666e-01 & 9.974113e-01\\\\\n", "\t CNAG\\_00014 & 180.53988 & 0.0489307250 & 0.06795183 & 0.720079553 & 4.714760e-01 & 5.825377e-01\\\\\n", "\t CNAG\\_00015 & 318.87538 & -0.5225627661 & 0.04948141 & -10.560789734 & 4.528359e-26 & 6.591188e-24\\\\\n", "\t CNAG\\_00016 & 901.25567 & -0.1743972099 & 0.04458282 & -3.911758549 & 9.162649e-05 & 3.953666e-04\\\\\n", "\t CNAG\\_00017 & 184.65031 & 0.2501141367 & 0.08576066 & 2.916420444 & 3.540730e-03 & 9.829867e-03\\\\\n", "\t CNAG\\_00018 & 876.12980 & 0.0533671855 & 0.03737492 & 1.427887678 & 1.533242e-01 & 2.399204e-01\\\\\n", "\t CNAG\\_00019 & 1208.90542 & -0.0376199793 & 0.05412226 & -0.695092485 & 4.869973e-01 & 5.980888e-01\\\\\n", "\t CNAG\\_00020 & 968.39030 & 0.1633467817 & 0.06202149 & 2.633712498 & 8.445697e-03 & 2.079157e-02\\\\\n", "\t CNAG\\_00021 & 539.92676 & 0.3455959860 & 0.05392145 & 6.409249293 & 1.462379e-10 & 2.337226e-09\\\\\n", "\t CNAG\\_00022 & 746.84601 & -0.0338002495 & 0.06149652 & -0.549628633 & 5.825741e-01 & 6.837382e-01\\\\\n", "\t CNAG\\_00023 & 278.87826 & -1.4600937656 & 0.26611238 & -5.486756279 & 4.093813e-08 & 3.977196e-07\\\\\n", "\t CNAG\\_00024 & 7703.04647 & -0.1074959195 & 0.04323925 & -2.486072815 & 1.291615e-02 & 3.001984e-02\\\\\n", "\t CNAG\\_00025 & 2021.99612 & -0.0966388995 & 0.09609375 & -1.005673059 & 3.145729e-01 & 4.265652e-01\\\\\n", "\t CNAG\\_00026 & 2237.77134 & -0.2217950575 & 0.06090899 & -3.641417339 & 2.711412e-04 & 1.034194e-03\\\\\n", "\t CNAG\\_00027 & 425.60765 & 0.1226841378 & 0.06051990 & 2.027170038 & 4.264503e-02 & 8.258484e-02\\\\\n", "\t CNAG\\_00028 & 381.48552 & -0.1052942893 & 0.05837561 & -1.803737692 & 7.127244e-02 & 1.274834e-01\\\\\n", "\t CNAG\\_00029 & 52.66523 & -0.0698792034 & 0.10076540 & -0.693484135 & 4.880058e-01 & 5.990298e-01\\\\\n", "\t CNAG\\_00030 & 76.57328 & -0.3288112337 & 0.12296567 & -2.674008451 & 7.495056e-03 & 1.881006e-02\\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t ENSRNA049550980 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551037 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551063 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551074 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551126 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551197 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551244 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551269 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551298 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551337 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551365 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551391 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551484 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551550 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551574 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551598 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551636 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551673 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551717 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551745 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551774 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551798 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551814 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551862 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551899 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551942 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551964 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551993 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t large\\_MTrRNA & 5333.974 & -0.3963375 & 0.1343409 & -2.950237 & 0.003175301 & 0.00896808\\\\\n", "\t small\\_MTrRNA & 1705.755 & -0.4368028 & 0.2955390 & -1.477987 & 0.139411199 & 0.22171458\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 8499 × 7\n", "\n", "| row <chr> | baseMean <dbl> | log2FoldChange <dbl> | lfcSE <dbl> | stat <dbl> | pvalue <dbl> | padj <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| CNAG_00001 | 0.00000 | NA | NA | NA | NA | NA |\n", "| CNAG_00002 | 192.45638 | 0.0459290106 | 0.08128491 | 0.565037338 | 5.720484e-01 | 6.754695e-01 |\n", "| CNAG_00003 | 164.99082 | 0.0432979308 | 0.10104510 | 0.428501044 | 6.682864e-01 | 7.564508e-01 |\n", "| CNAG_00004 | 571.05465 | 0.0347715410 | 0.04643122 | 0.748882773 | 4.539279e-01 | 5.662635e-01 |\n", "| CNAG_00005 | 28.71062 | -0.3770568056 | 0.19234744 | -1.960290255 | 4.996187e-02 | 9.433385e-02 |\n", "| CNAG_00006 | 3147.39964 | -0.1313696927 | 0.04955489 | -2.650993457 | 8.025540e-03 | 1.995982e-02 |\n", "| CNAG_00007 | 1484.87545 | -0.2511214344 | 0.08051906 | -3.118782455 | 1.816000e-03 | 5.492473e-03 |\n", "| CNAG_00008 | 938.01932 | -0.0095775568 | 0.04760749 | -0.201177504 | 8.405598e-01 | 8.873725e-01 |\n", "| CNAG_00009 | 305.19881 | -0.1866458870 | 0.09952871 | -1.875296922 | 6.075189e-02 | 1.116044e-01 |\n", "| CNAG_00010 | 989.04227 | 0.2991011506 | 0.06322330 | 4.730868797 | 2.235610e-06 | 1.474309e-05 |\n", "| CNAG_00011 | 1001.75238 | -0.0150629941 | 0.08611946 | -0.174908133 | 8.611518e-01 | 9.032619e-01 |\n", "| CNAG_00012 | 773.99385 | 0.1947487902 | 0.05765064 | 3.378085337 | 7.299243e-04 | 2.477973e-03 |\n", "| CNAG_00013 | 485.75319 | -0.0002823069 | 0.07949652 | -0.003551185 | 9.971666e-01 | 9.974113e-01 |\n", "| CNAG_00014 | 180.53988 | 0.0489307250 | 0.06795183 | 0.720079553 | 4.714760e-01 | 5.825377e-01 |\n", "| CNAG_00015 | 318.87538 | -0.5225627661 | 0.04948141 | -10.560789734 | 4.528359e-26 | 6.591188e-24 |\n", "| CNAG_00016 | 901.25567 | -0.1743972099 | 0.04458282 | -3.911758549 | 9.162649e-05 | 3.953666e-04 |\n", "| CNAG_00017 | 184.65031 | 0.2501141367 | 0.08576066 | 2.916420444 | 3.540730e-03 | 9.829867e-03 |\n", "| CNAG_00018 | 876.12980 | 0.0533671855 | 0.03737492 | 1.427887678 | 1.533242e-01 | 2.399204e-01 |\n", "| CNAG_00019 | 1208.90542 | -0.0376199793 | 0.05412226 | -0.695092485 | 4.869973e-01 | 5.980888e-01 |\n", "| CNAG_00020 | 968.39030 | 0.1633467817 | 0.06202149 | 2.633712498 | 8.445697e-03 | 2.079157e-02 |\n", "| CNAG_00021 | 539.92676 | 0.3455959860 | 0.05392145 | 6.409249293 | 1.462379e-10 | 2.337226e-09 |\n", "| CNAG_00022 | 746.84601 | -0.0338002495 | 0.06149652 | -0.549628633 | 5.825741e-01 | 6.837382e-01 |\n", "| CNAG_00023 | 278.87826 | -1.4600937656 | 0.26611238 | -5.486756279 | 4.093813e-08 | 3.977196e-07 |\n", "| CNAG_00024 | 7703.04647 | -0.1074959195 | 0.04323925 | -2.486072815 | 1.291615e-02 | 3.001984e-02 |\n", "| CNAG_00025 | 2021.99612 | -0.0966388995 | 0.09609375 | -1.005673059 | 3.145729e-01 | 4.265652e-01 |\n", "| CNAG_00026 | 2237.77134 | -0.2217950575 | 0.06090899 | -3.641417339 | 2.711412e-04 | 1.034194e-03 |\n", "| CNAG_00027 | 425.60765 | 0.1226841378 | 0.06051990 | 2.027170038 | 4.264503e-02 | 8.258484e-02 |\n", "| CNAG_00028 | 381.48552 | -0.1052942893 | 0.05837561 | -1.803737692 | 7.127244e-02 | 1.274834e-01 |\n", "| CNAG_00029 | 52.66523 | -0.0698792034 | 0.10076540 | -0.693484135 | 4.880058e-01 | 5.990298e-01 |\n", "| CNAG_00030 | 76.57328 | -0.3288112337 | 0.12296567 | -2.674008451 | 7.495056e-03 | 1.881006e-02 |\n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", "| ENSRNA049550980 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551037 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551063 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551074 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551126 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551197 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551244 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551269 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551298 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551337 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551365 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551391 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551484 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551550 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551574 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551598 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551636 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551673 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551717 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551745 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551774 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551798 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551814 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551862 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551899 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551942 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551964 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551993 | 0.000 | NA | NA | NA | NA | NA |\n", "| large_MTrRNA | 5333.974 | -0.3963375 | 0.1343409 | -2.950237 | 0.003175301 | 0.00896808 |\n", "| small_MTrRNA | 1705.755 | -0.4368028 | 0.2955390 | -1.477987 | 0.139411199 | 0.22171458 |\n", "\n" ], "text/plain": [ " row baseMean log2FoldChange lfcSE stat \n", "1 CNAG_00001 0.00000 NA NA NA\n", "2 CNAG_00002 192.45638 0.0459290106 0.08128491 0.565037338\n", "3 CNAG_00003 164.99082 0.0432979308 0.10104510 0.428501044\n", "4 CNAG_00004 571.05465 0.0347715410 0.04643122 0.748882773\n", "5 CNAG_00005 28.71062 -0.3770568056 0.19234744 -1.960290255\n", "6 CNAG_00006 3147.39964 -0.1313696927 0.04955489 -2.650993457\n", "7 CNAG_00007 1484.87545 -0.2511214344 0.08051906 -3.118782455\n", "8 CNAG_00008 938.01932 -0.0095775568 0.04760749 -0.201177504\n", "9 CNAG_00009 305.19881 -0.1866458870 0.09952871 -1.875296922\n", "10 CNAG_00010 989.04227 0.2991011506 0.06322330 4.730868797\n", "11 CNAG_00011 1001.75238 -0.0150629941 0.08611946 -0.174908133\n", "12 CNAG_00012 773.99385 0.1947487902 0.05765064 3.378085337\n", "13 CNAG_00013 485.75319 -0.0002823069 0.07949652 -0.003551185\n", "14 CNAG_00014 180.53988 0.0489307250 0.06795183 0.720079553\n", "15 CNAG_00015 318.87538 -0.5225627661 0.04948141 -10.560789734\n", "16 CNAG_00016 901.25567 -0.1743972099 0.04458282 -3.911758549\n", "17 CNAG_00017 184.65031 0.2501141367 0.08576066 2.916420444\n", "18 CNAG_00018 876.12980 0.0533671855 0.03737492 1.427887678\n", "19 CNAG_00019 1208.90542 -0.0376199793 0.05412226 -0.695092485\n", "20 CNAG_00020 968.39030 0.1633467817 0.06202149 2.633712498\n", "21 CNAG_00021 539.92676 0.3455959860 0.05392145 6.409249293\n", "22 CNAG_00022 746.84601 -0.0338002495 0.06149652 -0.549628633\n", "23 CNAG_00023 278.87826 -1.4600937656 0.26611238 -5.486756279\n", "24 CNAG_00024 7703.04647 -0.1074959195 0.04323925 -2.486072815\n", "25 CNAG_00025 2021.99612 -0.0966388995 0.09609375 -1.005673059\n", "26 CNAG_00026 2237.77134 -0.2217950575 0.06090899 -3.641417339\n", "27 CNAG_00027 425.60765 0.1226841378 0.06051990 2.027170038\n", "28 CNAG_00028 381.48552 -0.1052942893 0.05837561 -1.803737692\n", "29 CNAG_00029 52.66523 -0.0698792034 0.10076540 -0.693484135\n", "30 CNAG_00030 76.57328 -0.3288112337 0.12296567 -2.674008451\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", "8470 ENSRNA049550980 0.000 NA NA NA \n", "8471 ENSRNA049551037 0.000 NA NA NA \n", "8472 ENSRNA049551063 0.000 NA NA NA \n", "8473 ENSRNA049551074 0.000 NA NA NA \n", "8474 ENSRNA049551126 0.000 NA NA NA \n", "8475 ENSRNA049551197 0.000 NA NA NA \n", "8476 ENSRNA049551244 0.000 NA NA NA \n", "8477 ENSRNA049551269 0.000 NA NA NA \n", "8478 ENSRNA049551298 0.000 NA NA NA \n", "8479 ENSRNA049551337 0.000 NA NA NA \n", "8480 ENSRNA049551365 0.000 NA NA NA \n", "8481 ENSRNA049551391 0.000 NA NA NA \n", "8482 ENSRNA049551484 0.000 NA NA NA \n", "8483 ENSRNA049551550 0.000 NA NA NA \n", "8484 ENSRNA049551574 0.000 NA NA NA \n", "8485 ENSRNA049551598 0.000 NA NA NA \n", "8486 ENSRNA049551636 0.000 NA NA NA \n", "8487 ENSRNA049551673 0.000 NA NA NA \n", "8488 ENSRNA049551717 0.000 NA NA NA \n", "8489 ENSRNA049551745 0.000 NA NA NA \n", "8490 ENSRNA049551774 0.000 NA NA NA \n", "8491 ENSRNA049551798 0.000 NA NA NA \n", "8492 ENSRNA049551814 0.000 NA NA NA \n", "8493 ENSRNA049551862 0.000 NA NA NA \n", "8494 ENSRNA049551899 0.000 NA NA NA \n", "8495 ENSRNA049551942 0.000 NA NA NA \n", "8496 ENSRNA049551964 0.000 NA NA NA \n", "8497 ENSRNA049551993 0.000 NA NA NA \n", "8498 large_MTrRNA 5333.974 -0.3963375 0.1343409 -2.950237 \n", "8499 small_MTrRNA 1705.755 -0.4368028 0.2955390 -1.477987 \n", " pvalue padj \n", "1 NA NA\n", "2 5.720484e-01 6.754695e-01\n", "3 6.682864e-01 7.564508e-01\n", "4 4.539279e-01 5.662635e-01\n", "5 4.996187e-02 9.433385e-02\n", "6 8.025540e-03 1.995982e-02\n", "7 1.816000e-03 5.492473e-03\n", "8 8.405598e-01 8.873725e-01\n", "9 6.075189e-02 1.116044e-01\n", "10 2.235610e-06 1.474309e-05\n", "11 8.611518e-01 9.032619e-01\n", "12 7.299243e-04 2.477973e-03\n", "13 9.971666e-01 9.974113e-01\n", "14 4.714760e-01 5.825377e-01\n", "15 4.528359e-26 6.591188e-24\n", "16 9.162649e-05 3.953666e-04\n", "17 3.540730e-03 9.829867e-03\n", "18 1.533242e-01 2.399204e-01\n", "19 4.869973e-01 5.980888e-01\n", "20 8.445697e-03 2.079157e-02\n", "21 1.462379e-10 2.337226e-09\n", "22 5.825741e-01 6.837382e-01\n", "23 4.093813e-08 3.977196e-07\n", "24 1.291615e-02 3.001984e-02\n", "25 3.145729e-01 4.265652e-01\n", "26 2.711412e-04 1.034194e-03\n", "27 4.264503e-02 8.258484e-02\n", "28 7.127244e-02 1.274834e-01\n", "29 4.880058e-01 5.990298e-01\n", "30 7.495056e-03 1.881006e-02\n", "⋮ ⋮ ⋮ \n", "8470 NA NA \n", "8471 NA NA \n", "8472 NA NA \n", "8473 NA NA \n", "8474 NA NA \n", "8475 NA NA \n", "8476 NA NA \n", "8477 NA NA \n", "8478 NA NA \n", "8479 NA NA \n", "8480 NA NA \n", "8481 NA NA \n", "8482 NA NA \n", "8483 NA NA \n", "8484 NA NA \n", "8485 NA NA \n", "8486 NA NA \n", "8487 NA NA \n", "8488 NA NA \n", "8489 NA NA \n", "8490 NA NA \n", "8491 NA NA \n", "8492 NA NA \n", "8493 NA NA \n", "8494 NA NA \n", "8495 NA NA \n", "8496 NA NA \n", "8497 NA NA \n", "8498 0.003175301 0.00896808 \n", "8499 0.139411199 0.22171458 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results(ddsDE, tidy = TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get the results for the differential expression analysis using results(). Here, we can compare two group of samples specified by the contrast. (If not, the default contrast would be the last term in your additive model `design(dds)`)." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "log2 fold change (MLE): condition pH4 vs pH8 \n", "Wald test p-value: condition pH4 vs pH8 \n", "DataFrame with 8499 rows and 6 columns\n", " baseMean log2FoldChange lfcSE\n", " \n", "CNAG_00001 0 NA NA\n", "CNAG_00002 192.456384076694 -1.34351851614176 0.0813854454817818\n", "CNAG_00003 164.990819436254 -0.831905577724638 0.101018400723969\n", "CNAG_00004 571.054651467718 -0.0871138906737344 0.0465166816365353\n", "CNAG_00005 28.7106195205257 -1.29275886849841 0.192729463777133\n", "... ... ... ...\n", "ENSRNA049551942 0 NA NA\n", "ENSRNA049551964 0 NA NA\n", "ENSRNA049551993 0 NA NA\n", "large_MTrRNA 5333.97356461621 -0.948564278537808 0.134340883270906\n", "small_MTrRNA 1705.75472334291 -0.691472418621256 0.295538979277226\n", " stat pvalue padj\n", " \n", "CNAG_00001 NA NA NA\n", "CNAG_00002 -16.5080931631996 3.20862403991611e-61 2.87085560366149e-60\n", "CNAG_00003 -8.23518855735805 1.79275046151211e-16 4.79576928512805e-16\n", "CNAG_00004 -1.87274516601186 0.0611035831980367 0.074895534834165\n", "CNAG_00005 -6.70763485334717 1.97804137561036e-11 4.32020773113613e-11\n", "... ... ... ...\n", "ENSRNA049551942 NA NA NA\n", "ENSRNA049551964 NA NA NA\n", "ENSRNA049551993 NA NA NA\n", "large_MTrRNA -7.06087570248421 1.65456549165796e-12 3.75246614426935e-12\n", "small_MTrRNA -2.33969955608675 0.0192992582107572 0.0250291573072207" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# DE with respect to condition\n", "myres_condition4v8 <- results(ddsDE, contrast = c(\"condition\", \"pH4\", \"pH8\"))\n", "myres_condition4v8" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "log2 fold change (MLE): condition pH8 vs pH4 \n", "Wald test p-value: condition pH8 vs pH4 \n", "DataFrame with 8499 rows and 6 columns\n", " baseMean log2FoldChange lfcSE\n", " \n", "CNAG_00001 0 NA NA\n", "CNAG_00002 192.456384076694 1.34351851614176 0.0813854454817818\n", "CNAG_00003 164.990819436254 0.831905577724638 0.101018400723969\n", "CNAG_00004 571.054651467718 0.0871138906737344 0.0465166816365353\n", "CNAG_00005 28.7106195205257 1.29275886849841 0.192729463777133\n", "... ... ... ...\n", "ENSRNA049551942 0 NA NA\n", "ENSRNA049551964 0 NA NA\n", "ENSRNA049551993 0 NA NA\n", "large_MTrRNA 5333.97356461621 0.948564278537808 0.134340883270906\n", "small_MTrRNA 1705.75472334291 0.691472418621256 0.295538979277226\n", " stat pvalue padj\n", " \n", "CNAG_00001 NA NA NA\n", "CNAG_00002 16.5080931631996 3.20862403991611e-61 2.87085560366149e-60\n", "CNAG_00003 8.23518855735805 1.79275046151211e-16 4.79576928512805e-16\n", "CNAG_00004 1.87274516601186 0.0611035831980367 0.074895534834165\n", "CNAG_00005 6.70763485334717 1.97804137561036e-11 4.32020773113613e-11\n", "... ... ... ...\n", "ENSRNA049551942 NA NA NA\n", "ENSRNA049551964 NA NA NA\n", "ENSRNA049551993 NA NA NA\n", "large_MTrRNA 7.06087570248421 1.65456549165796e-12 3.75246614426935e-12\n", "small_MTrRNA 2.33969955608675 0.0192992582107572 0.0250291573072207" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# DE with respect to condition (flip order)\n", "myres_condition8v4 <- results(ddsDE, contrast = c(\"condition\", \"pH8\", \"pH4\"))\n", "myres_condition8v4" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "log2 fold change (MLE): genotype sre1d vs WT \n", "Wald test p-value: genotype sre1d vs WT \n", "DataFrame with 8499 rows and 6 columns\n", " baseMean log2FoldChange lfcSE\n", " \n", "CNAG_00001 0 NA NA\n", "CNAG_00002 192.456384076694 -0.0459290105698189 0.0812849125012254\n", "CNAG_00003 164.990819436254 -0.0432979308487266 0.101045100070341\n", "CNAG_00004 571.054651467718 -0.0347715410374543 0.0464312203030827\n", "CNAG_00005 28.7106195205257 0.377056805602812 0.192347436642309\n", "... ... ... ...\n", "ENSRNA049551942 0 NA NA\n", "ENSRNA049551964 0 NA NA\n", "ENSRNA049551993 0 NA NA\n", "large_MTrRNA 5333.97356461621 0.396337460272526 0.13434088145007\n", "small_MTrRNA 1705.75472334291 0.436802837852531 0.295538981353034\n", " stat pvalue padj\n", " \n", "CNAG_00001 NA NA NA\n", "CNAG_00002 -0.565037337883909 0.572048368119426 0.675469542016723\n", "CNAG_00003 -0.428501043777338 0.668286373790347 0.756450803050288\n", "CNAG_00004 -0.748882773497678 0.453927864041203 0.566263547566551\n", "CNAG_00005 1.96029025488907 0.0499618744055703 0.094333851813714\n", "... ... ... ...\n", "ENSRNA049551942 NA NA NA\n", "ENSRNA049551964 NA NA NA\n", "ENSRNA049551993 NA NA NA\n", "large_MTrRNA 2.95023715785154 0.00317530091247218 0.00896807960414441\n", "small_MTrRNA 1.4779872213566 0.139411198621898 0.221714577729148" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### DE with respect to genotype\n", "myres_strainvWT <- results(ddsDE, contrast = c(\"genotype\", \"sre1d\", \"WT\"))\n", "myres_strainvWT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the results for the first four genes" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 8499 × 7
rowbaseMeanlog2FoldChangelfcSEstatpvaluepadj
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
CNAG_00001 0.00000 NA NA NA NA NA
CNAG_00002 192.45638 1.3435185160.08138545 16.5080932 3.208624e-61 2.870856e-60
CNAG_00003 164.99082 0.8319055780.10101840 8.2351886 1.792750e-16 4.795769e-16
CNAG_00004 571.05465 0.0871138910.04651668 1.8727452 6.110358e-02 7.489553e-02
CNAG_00005 28.71062 1.2927588680.19272946 6.7076349 1.978041e-11 4.320208e-11
CNAG_000063147.39964-0.1393277750.04956000 -2.8112951 4.934250e-03 6.756102e-03
CNAG_000071484.87545-0.7479203280.08053624 -9.2867544 1.590647e-20 4.927921e-20
CNAG_00008 938.01932-0.0791101960.04765929 -1.6599115 9.693226e-02 1.159517e-01
CNAG_00009 305.19881-0.5588380660.09968395 -5.6060989 2.069377e-08 3.933651e-08
CNAG_00010 989.04227 0.5378936680.06321490 8.5089694 1.754861e-17 4.868574e-17
CNAG_000111001.75238-2.1488943670.08634115-24.88841429.932146e-1372.998405e-135
CNAG_00012 773.99385 0.1614128170.05766095 2.7993437 5.120661e-03 6.994889e-03
CNAG_00013 485.75319-0.7229664970.07965037 -9.0767500 1.118652e-19 3.374959e-19
CNAG_00014 180.53988-0.2958865600.06870554 -4.3065895 1.657908e-05 2.684468e-05
CNAG_00015 318.87538-0.3285995880.05019105 -6.5469752 5.871409e-11 1.252823e-10
CNAG_00016 901.25567 0.3127175960.04460378 7.0110118 2.366008e-12 5.340717e-12
CNAG_00017 184.65031 0.3166832430.08573971 3.6935425 2.211516e-04 3.341873e-04
CNAG_00018 876.12980 0.1552110010.03742605 4.1471376 3.366577e-05 5.348074e-05
CNAG_000191208.90542 0.7471236790.05412045 13.8048310 2.383308e-43 1.414883e-42
CNAG_00020 968.39030-0.4701641250.06206999 -7.5747418 3.598425e-14 8.737196e-14
CNAG_00021 539.92676 0.0079220360.05395599 0.1468240 8.832709e-01 8.975865e-01
CNAG_00022 746.84601 0.0202990740.06152583 0.3299277 7.414546e-01 7.689054e-01
CNAG_00023 278.87826 0.2320639630.26610452 0.8720782 3.831657e-01 4.210277e-01
CNAG_000247703.04647 0.9970433840.04323929 23.05873811.202071e-1172.760023e-116
CNAG_000252021.99612 0.8810554030.09609371 9.1687104 4.787115e-20 1.463607e-19
CNAG_000262237.77134 0.1674335120.06091140 2.7488043 5.981308e-03 8.132384e-03
CNAG_00027 425.60765-0.9296469980.06112216-15.2096555 3.051445e-52 2.273522e-51
CNAG_00028 381.48552-0.8174229400.05910049-13.8310684 1.655433e-43 9.892547e-43
CNAG_00029 52.66523-3.3160308810.16631275-19.9385244 1.885448e-88 2.729713e-87
CNAG_00030 76.57328-1.1036474980.12592110 -8.7645955 1.874484e-18 5.414216e-18
⋮⋮⋮⋮⋮⋮⋮
ENSRNA049550980 0.000 NA NA NA NA NA
ENSRNA049551037 0.000 NA NA NA NA NA
ENSRNA049551063 0.000 NA NA NA NA NA
ENSRNA049551074 0.000 NA NA NA NA NA
ENSRNA049551126 0.000 NA NA NA NA NA
ENSRNA049551197 0.000 NA NA NA NA NA
ENSRNA049551244 0.000 NA NA NA NA NA
ENSRNA049551269 0.000 NA NA NA NA NA
ENSRNA049551298 0.000 NA NA NA NA NA
ENSRNA049551337 0.000 NA NA NA NA NA
ENSRNA049551365 0.000 NA NA NA NA NA
ENSRNA049551391 0.000 NA NA NA NA NA
ENSRNA049551484 0.000 NA NA NA NA NA
ENSRNA049551550 0.000 NA NA NA NA NA
ENSRNA049551574 0.000 NA NA NA NA NA
ENSRNA049551598 0.000 NA NA NA NA NA
ENSRNA049551636 0.000 NA NA NA NA NA
ENSRNA049551673 0.000 NA NA NA NA NA
ENSRNA049551717 0.000 NA NA NA NA NA
ENSRNA049551745 0.000 NA NA NA NA NA
ENSRNA049551774 0.000 NA NA NA NA NA
ENSRNA049551798 0.000 NA NA NA NA NA
ENSRNA049551814 0.000 NA NA NA NA NA
ENSRNA049551862 0.000 NA NA NA NA NA
ENSRNA049551899 0.000 NA NA NA NA NA
ENSRNA049551942 0.000 NA NA NA NA NA
ENSRNA049551964 0.000 NA NA NA NA NA
ENSRNA049551993 0.000 NA NA NA NA NA
large_MTrRNA 5333.9740.94856430.13434097.0608761.654565e-123.752466e-12
small_MTrRNA 1705.7550.69147240.29553902.3397001.929926e-022.502916e-02
\n" ], "text/latex": [ "A data.frame: 8499 × 7\n", "\\begin{tabular}{r|lllllll}\n", " row & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t CNAG\\_00001 & 0.00000 & NA & NA & NA & NA & NA\\\\\n", "\t CNAG\\_00002 & 192.45638 & 1.343518516 & 0.08138545 & 16.5080932 & 3.208624e-61 & 2.870856e-60\\\\\n", "\t CNAG\\_00003 & 164.99082 & 0.831905578 & 0.10101840 & 8.2351886 & 1.792750e-16 & 4.795769e-16\\\\\n", "\t CNAG\\_00004 & 571.05465 & 0.087113891 & 0.04651668 & 1.8727452 & 6.110358e-02 & 7.489553e-02\\\\\n", "\t CNAG\\_00005 & 28.71062 & 1.292758868 & 0.19272946 & 6.7076349 & 1.978041e-11 & 4.320208e-11\\\\\n", "\t CNAG\\_00006 & 3147.39964 & -0.139327775 & 0.04956000 & -2.8112951 & 4.934250e-03 & 6.756102e-03\\\\\n", "\t CNAG\\_00007 & 1484.87545 & -0.747920328 & 0.08053624 & -9.2867544 & 1.590647e-20 & 4.927921e-20\\\\\n", "\t CNAG\\_00008 & 938.01932 & -0.079110196 & 0.04765929 & -1.6599115 & 9.693226e-02 & 1.159517e-01\\\\\n", "\t CNAG\\_00009 & 305.19881 & -0.558838066 & 0.09968395 & -5.6060989 & 2.069377e-08 & 3.933651e-08\\\\\n", "\t CNAG\\_00010 & 989.04227 & 0.537893668 & 0.06321490 & 8.5089694 & 1.754861e-17 & 4.868574e-17\\\\\n", "\t CNAG\\_00011 & 1001.75238 & -2.148894367 & 0.08634115 & -24.8884142 & 9.932146e-137 & 2.998405e-135\\\\\n", "\t CNAG\\_00012 & 773.99385 & 0.161412817 & 0.05766095 & 2.7993437 & 5.120661e-03 & 6.994889e-03\\\\\n", "\t CNAG\\_00013 & 485.75319 & -0.722966497 & 0.07965037 & -9.0767500 & 1.118652e-19 & 3.374959e-19\\\\\n", "\t CNAG\\_00014 & 180.53988 & -0.295886560 & 0.06870554 & -4.3065895 & 1.657908e-05 & 2.684468e-05\\\\\n", "\t CNAG\\_00015 & 318.87538 & -0.328599588 & 0.05019105 & -6.5469752 & 5.871409e-11 & 1.252823e-10\\\\\n", "\t CNAG\\_00016 & 901.25567 & 0.312717596 & 0.04460378 & 7.0110118 & 2.366008e-12 & 5.340717e-12\\\\\n", "\t CNAG\\_00017 & 184.65031 & 0.316683243 & 0.08573971 & 3.6935425 & 2.211516e-04 & 3.341873e-04\\\\\n", "\t CNAG\\_00018 & 876.12980 & 0.155211001 & 0.03742605 & 4.1471376 & 3.366577e-05 & 5.348074e-05\\\\\n", "\t CNAG\\_00019 & 1208.90542 & 0.747123679 & 0.05412045 & 13.8048310 & 2.383308e-43 & 1.414883e-42\\\\\n", "\t CNAG\\_00020 & 968.39030 & -0.470164125 & 0.06206999 & -7.5747418 & 3.598425e-14 & 8.737196e-14\\\\\n", "\t CNAG\\_00021 & 539.92676 & 0.007922036 & 0.05395599 & 0.1468240 & 8.832709e-01 & 8.975865e-01\\\\\n", "\t CNAG\\_00022 & 746.84601 & 0.020299074 & 0.06152583 & 0.3299277 & 7.414546e-01 & 7.689054e-01\\\\\n", "\t CNAG\\_00023 & 278.87826 & 0.232063963 & 0.26610452 & 0.8720782 & 3.831657e-01 & 4.210277e-01\\\\\n", "\t CNAG\\_00024 & 7703.04647 & 0.997043384 & 0.04323929 & 23.0587381 & 1.202071e-117 & 2.760023e-116\\\\\n", "\t CNAG\\_00025 & 2021.99612 & 0.881055403 & 0.09609371 & 9.1687104 & 4.787115e-20 & 1.463607e-19\\\\\n", "\t CNAG\\_00026 & 2237.77134 & 0.167433512 & 0.06091140 & 2.7488043 & 5.981308e-03 & 8.132384e-03\\\\\n", "\t CNAG\\_00027 & 425.60765 & -0.929646998 & 0.06112216 & -15.2096555 & 3.051445e-52 & 2.273522e-51\\\\\n", "\t CNAG\\_00028 & 381.48552 & -0.817422940 & 0.05910049 & -13.8310684 & 1.655433e-43 & 9.892547e-43\\\\\n", "\t CNAG\\_00029 & 52.66523 & -3.316030881 & 0.16631275 & -19.9385244 & 1.885448e-88 & 2.729713e-87\\\\\n", "\t CNAG\\_00030 & 76.57328 & -1.103647498 & 0.12592110 & -8.7645955 & 1.874484e-18 & 5.414216e-18\\\\\n", "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", "\t ENSRNA049550980 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551037 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551063 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551074 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551126 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551197 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551244 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551269 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551298 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551337 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551365 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551391 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551484 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551550 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551574 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551598 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551636 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551673 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551717 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551745 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551774 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551798 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551814 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551862 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551899 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551942 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551964 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t ENSRNA049551993 & 0.000 & NA & NA & NA & NA & NA\\\\\n", "\t large\\_MTrRNA & 5333.974 & 0.9485643 & 0.1343409 & 7.060876 & 1.654565e-12 & 3.752466e-12\\\\\n", "\t small\\_MTrRNA & 1705.755 & 0.6914724 & 0.2955390 & 2.339700 & 1.929926e-02 & 2.502916e-02\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 8499 × 7\n", "\n", "| row <chr> | baseMean <dbl> | log2FoldChange <dbl> | lfcSE <dbl> | stat <dbl> | pvalue <dbl> | padj <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| CNAG_00001 | 0.00000 | NA | NA | NA | NA | NA |\n", "| CNAG_00002 | 192.45638 | 1.343518516 | 0.08138545 | 16.5080932 | 3.208624e-61 | 2.870856e-60 |\n", "| CNAG_00003 | 164.99082 | 0.831905578 | 0.10101840 | 8.2351886 | 1.792750e-16 | 4.795769e-16 |\n", "| CNAG_00004 | 571.05465 | 0.087113891 | 0.04651668 | 1.8727452 | 6.110358e-02 | 7.489553e-02 |\n", "| CNAG_00005 | 28.71062 | 1.292758868 | 0.19272946 | 6.7076349 | 1.978041e-11 | 4.320208e-11 |\n", "| CNAG_00006 | 3147.39964 | -0.139327775 | 0.04956000 | -2.8112951 | 4.934250e-03 | 6.756102e-03 |\n", "| CNAG_00007 | 1484.87545 | -0.747920328 | 0.08053624 | -9.2867544 | 1.590647e-20 | 4.927921e-20 |\n", "| CNAG_00008 | 938.01932 | -0.079110196 | 0.04765929 | -1.6599115 | 9.693226e-02 | 1.159517e-01 |\n", "| CNAG_00009 | 305.19881 | -0.558838066 | 0.09968395 | -5.6060989 | 2.069377e-08 | 3.933651e-08 |\n", "| CNAG_00010 | 989.04227 | 0.537893668 | 0.06321490 | 8.5089694 | 1.754861e-17 | 4.868574e-17 |\n", "| CNAG_00011 | 1001.75238 | -2.148894367 | 0.08634115 | -24.8884142 | 9.932146e-137 | 2.998405e-135 |\n", "| CNAG_00012 | 773.99385 | 0.161412817 | 0.05766095 | 2.7993437 | 5.120661e-03 | 6.994889e-03 |\n", "| CNAG_00013 | 485.75319 | -0.722966497 | 0.07965037 | -9.0767500 | 1.118652e-19 | 3.374959e-19 |\n", "| CNAG_00014 | 180.53988 | -0.295886560 | 0.06870554 | -4.3065895 | 1.657908e-05 | 2.684468e-05 |\n", "| CNAG_00015 | 318.87538 | -0.328599588 | 0.05019105 | -6.5469752 | 5.871409e-11 | 1.252823e-10 |\n", "| CNAG_00016 | 901.25567 | 0.312717596 | 0.04460378 | 7.0110118 | 2.366008e-12 | 5.340717e-12 |\n", "| CNAG_00017 | 184.65031 | 0.316683243 | 0.08573971 | 3.6935425 | 2.211516e-04 | 3.341873e-04 |\n", "| CNAG_00018 | 876.12980 | 0.155211001 | 0.03742605 | 4.1471376 | 3.366577e-05 | 5.348074e-05 |\n", "| CNAG_00019 | 1208.90542 | 0.747123679 | 0.05412045 | 13.8048310 | 2.383308e-43 | 1.414883e-42 |\n", "| CNAG_00020 | 968.39030 | -0.470164125 | 0.06206999 | -7.5747418 | 3.598425e-14 | 8.737196e-14 |\n", "| CNAG_00021 | 539.92676 | 0.007922036 | 0.05395599 | 0.1468240 | 8.832709e-01 | 8.975865e-01 |\n", "| CNAG_00022 | 746.84601 | 0.020299074 | 0.06152583 | 0.3299277 | 7.414546e-01 | 7.689054e-01 |\n", "| CNAG_00023 | 278.87826 | 0.232063963 | 0.26610452 | 0.8720782 | 3.831657e-01 | 4.210277e-01 |\n", "| CNAG_00024 | 7703.04647 | 0.997043384 | 0.04323929 | 23.0587381 | 1.202071e-117 | 2.760023e-116 |\n", "| CNAG_00025 | 2021.99612 | 0.881055403 | 0.09609371 | 9.1687104 | 4.787115e-20 | 1.463607e-19 |\n", "| CNAG_00026 | 2237.77134 | 0.167433512 | 0.06091140 | 2.7488043 | 5.981308e-03 | 8.132384e-03 |\n", "| CNAG_00027 | 425.60765 | -0.929646998 | 0.06112216 | -15.2096555 | 3.051445e-52 | 2.273522e-51 |\n", "| CNAG_00028 | 381.48552 | -0.817422940 | 0.05910049 | -13.8310684 | 1.655433e-43 | 9.892547e-43 |\n", "| CNAG_00029 | 52.66523 | -3.316030881 | 0.16631275 | -19.9385244 | 1.885448e-88 | 2.729713e-87 |\n", "| CNAG_00030 | 76.57328 | -1.103647498 | 0.12592110 | -8.7645955 | 1.874484e-18 | 5.414216e-18 |\n", "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n", "| ENSRNA049550980 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551037 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551063 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551074 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551126 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551197 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551244 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551269 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551298 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551337 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551365 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551391 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551484 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551550 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551574 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551598 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551636 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551673 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551717 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551745 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551774 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551798 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551814 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551862 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551899 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551942 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551964 | 0.000 | NA | NA | NA | NA | NA |\n", "| ENSRNA049551993 | 0.000 | NA | NA | NA | NA | NA |\n", "| large_MTrRNA | 5333.974 | 0.9485643 | 0.1343409 | 7.060876 | 1.654565e-12 | 3.752466e-12 |\n", "| small_MTrRNA | 1705.755 | 0.6914724 | 0.2955390 | 2.339700 | 1.929926e-02 | 2.502916e-02 |\n", "\n" ], "text/plain": [ " row baseMean log2FoldChange lfcSE stat \n", "1 CNAG_00001 0.00000 NA NA NA\n", "2 CNAG_00002 192.45638 1.343518516 0.08138545 16.5080932\n", "3 CNAG_00003 164.99082 0.831905578 0.10101840 8.2351886\n", "4 CNAG_00004 571.05465 0.087113891 0.04651668 1.8727452\n", "5 CNAG_00005 28.71062 1.292758868 0.19272946 6.7076349\n", "6 CNAG_00006 3147.39964 -0.139327775 0.04956000 -2.8112951\n", "7 CNAG_00007 1484.87545 -0.747920328 0.08053624 -9.2867544\n", "8 CNAG_00008 938.01932 -0.079110196 0.04765929 -1.6599115\n", "9 CNAG_00009 305.19881 -0.558838066 0.09968395 -5.6060989\n", "10 CNAG_00010 989.04227 0.537893668 0.06321490 8.5089694\n", "11 CNAG_00011 1001.75238 -2.148894367 0.08634115 -24.8884142\n", "12 CNAG_00012 773.99385 0.161412817 0.05766095 2.7993437\n", "13 CNAG_00013 485.75319 -0.722966497 0.07965037 -9.0767500\n", "14 CNAG_00014 180.53988 -0.295886560 0.06870554 -4.3065895\n", "15 CNAG_00015 318.87538 -0.328599588 0.05019105 -6.5469752\n", "16 CNAG_00016 901.25567 0.312717596 0.04460378 7.0110118\n", "17 CNAG_00017 184.65031 0.316683243 0.08573971 3.6935425\n", "18 CNAG_00018 876.12980 0.155211001 0.03742605 4.1471376\n", "19 CNAG_00019 1208.90542 0.747123679 0.05412045 13.8048310\n", "20 CNAG_00020 968.39030 -0.470164125 0.06206999 -7.5747418\n", "21 CNAG_00021 539.92676 0.007922036 0.05395599 0.1468240\n", "22 CNAG_00022 746.84601 0.020299074 0.06152583 0.3299277\n", "23 CNAG_00023 278.87826 0.232063963 0.26610452 0.8720782\n", "24 CNAG_00024 7703.04647 0.997043384 0.04323929 23.0587381\n", "25 CNAG_00025 2021.99612 0.881055403 0.09609371 9.1687104\n", "26 CNAG_00026 2237.77134 0.167433512 0.06091140 2.7488043\n", "27 CNAG_00027 425.60765 -0.929646998 0.06112216 -15.2096555\n", "28 CNAG_00028 381.48552 -0.817422940 0.05910049 -13.8310684\n", "29 CNAG_00029 52.66523 -3.316030881 0.16631275 -19.9385244\n", "30 CNAG_00030 76.57328 -1.103647498 0.12592110 -8.7645955\n", "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", "8470 ENSRNA049550980 0.000 NA NA NA \n", "8471 ENSRNA049551037 0.000 NA NA NA \n", "8472 ENSRNA049551063 0.000 NA NA NA \n", "8473 ENSRNA049551074 0.000 NA NA NA \n", "8474 ENSRNA049551126 0.000 NA NA NA \n", "8475 ENSRNA049551197 0.000 NA NA NA \n", "8476 ENSRNA049551244 0.000 NA NA NA \n", "8477 ENSRNA049551269 0.000 NA NA NA \n", "8478 ENSRNA049551298 0.000 NA NA NA \n", "8479 ENSRNA049551337 0.000 NA NA NA \n", "8480 ENSRNA049551365 0.000 NA NA NA \n", "8481 ENSRNA049551391 0.000 NA NA NA \n", "8482 ENSRNA049551484 0.000 NA NA NA \n", "8483 ENSRNA049551550 0.000 NA NA NA \n", "8484 ENSRNA049551574 0.000 NA NA NA \n", "8485 ENSRNA049551598 0.000 NA NA NA \n", "8486 ENSRNA049551636 0.000 NA NA NA \n", "8487 ENSRNA049551673 0.000 NA NA NA \n", "8488 ENSRNA049551717 0.000 NA NA NA \n", "8489 ENSRNA049551745 0.000 NA NA NA \n", "8490 ENSRNA049551774 0.000 NA NA NA \n", "8491 ENSRNA049551798 0.000 NA NA NA \n", "8492 ENSRNA049551814 0.000 NA NA NA \n", "8493 ENSRNA049551862 0.000 NA NA NA \n", "8494 ENSRNA049551899 0.000 NA NA NA \n", "8495 ENSRNA049551942 0.000 NA NA NA \n", "8496 ENSRNA049551964 0.000 NA NA NA \n", "8497 ENSRNA049551993 0.000 NA NA NA \n", "8498 large_MTrRNA 5333.974 0.9485643 0.1343409 7.060876 \n", "8499 small_MTrRNA 1705.755 0.6914724 0.2955390 2.339700 \n", " pvalue padj \n", "1 NA NA\n", "2 3.208624e-61 2.870856e-60\n", "3 1.792750e-16 4.795769e-16\n", "4 6.110358e-02 7.489553e-02\n", "5 1.978041e-11 4.320208e-11\n", "6 4.934250e-03 6.756102e-03\n", "7 1.590647e-20 4.927921e-20\n", "8 9.693226e-02 1.159517e-01\n", "9 2.069377e-08 3.933651e-08\n", "10 1.754861e-17 4.868574e-17\n", "11 9.932146e-137 2.998405e-135\n", "12 5.120661e-03 6.994889e-03\n", "13 1.118652e-19 3.374959e-19\n", "14 1.657908e-05 2.684468e-05\n", "15 5.871409e-11 1.252823e-10\n", "16 2.366008e-12 5.340717e-12\n", "17 2.211516e-04 3.341873e-04\n", "18 3.366577e-05 5.348074e-05\n", "19 2.383308e-43 1.414883e-42\n", "20 3.598425e-14 8.737196e-14\n", "21 8.832709e-01 8.975865e-01\n", "22 7.414546e-01 7.689054e-01\n", "23 3.831657e-01 4.210277e-01\n", "24 1.202071e-117 2.760023e-116\n", "25 4.787115e-20 1.463607e-19\n", "26 5.981308e-03 8.132384e-03\n", "27 3.051445e-52 2.273522e-51\n", "28 1.655433e-43 9.892547e-43\n", "29 1.885448e-88 2.729713e-87\n", "30 1.874484e-18 5.414216e-18\n", "⋮ ⋮ ⋮ \n", "8470 NA NA \n", "8471 NA NA \n", "8472 NA NA \n", "8473 NA NA \n", "8474 NA NA \n", "8475 NA NA \n", "8476 NA NA \n", "8477 NA NA \n", "8478 NA NA \n", "8479 NA NA \n", "8480 NA NA \n", "8481 NA NA \n", "8482 NA NA \n", "8483 NA NA \n", "8484 NA NA \n", "8485 NA NA \n", "8486 NA NA \n", "8487 NA NA \n", "8488 NA NA \n", "8489 NA NA \n", "8490 NA NA \n", "8491 NA NA \n", "8492 NA NA \n", "8493 NA NA \n", "8494 NA NA \n", "8495 NA NA \n", "8496 NA NA \n", "8497 NA NA \n", "8498 1.654565e-12 3.752466e-12 \n", "8499 1.929926e-02 2.502916e-02 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Tidy the results\n", "myres_condition8v4 <- results(ddsDE, contrast = c(\"condition\", \"pH8\", \"pH4\"), tidy = TRUE)\n", "myres_condition8v4" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 10 × 7
rowbaseMeanlog2FoldChangelfcSEstatpvaluepadj
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
CNAG_00275 1072.9219-4.0481060.10667356-37.9485400
CNAG_00309 539.1312-3.3315240.08444635-39.4513700
CNAG_00409 1093.4775-5.0306550.07452918-67.4991300
CNAG_0053115371.8856 3.5636910.05663035 62.9290000
CNAG_00601 506.4752 4.6615770.09625726 48.4283200
CNAG_0089712255.8281 4.8569240.07970883 60.9333200
CNAG_0127249334.5209 1.9610090.04521777 43.3681200
CNAG_01275 2534.1015 2.2730880.04888985 46.4940600
CNAG_01344 3528.6160-1.5821220.04028591-39.2723400
CNAG_01713 9652.4535 2.0164860.05310869 37.9690400
\n" ], "text/latex": [ "A data.frame: 10 × 7\n", "\\begin{tabular}{r|lllllll}\n", " row & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t CNAG\\_00275 & 1072.9219 & -4.048106 & 0.10667356 & -37.94854 & 0 & 0\\\\\n", "\t CNAG\\_00309 & 539.1312 & -3.331524 & 0.08444635 & -39.45137 & 0 & 0\\\\\n", "\t CNAG\\_00409 & 1093.4775 & -5.030655 & 0.07452918 & -67.49913 & 0 & 0\\\\\n", "\t CNAG\\_00531 & 15371.8856 & 3.563691 & 0.05663035 & 62.92900 & 0 & 0\\\\\n", "\t CNAG\\_00601 & 506.4752 & 4.661577 & 0.09625726 & 48.42832 & 0 & 0\\\\\n", "\t CNAG\\_00897 & 12255.8281 & 4.856924 & 0.07970883 & 60.93332 & 0 & 0\\\\\n", "\t CNAG\\_01272 & 49334.5209 & 1.961009 & 0.04521777 & 43.36812 & 0 & 0\\\\\n", "\t CNAG\\_01275 & 2534.1015 & 2.273088 & 0.04888985 & 46.49406 & 0 & 0\\\\\n", "\t CNAG\\_01344 & 3528.6160 & -1.582122 & 0.04028591 & -39.27234 & 0 & 0\\\\\n", "\t CNAG\\_01713 & 9652.4535 & 2.016486 & 0.05310869 & 37.96904 & 0 & 0\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 10 × 7\n", "\n", "| row <chr> | baseMean <dbl> | log2FoldChange <dbl> | lfcSE <dbl> | stat <dbl> | pvalue <dbl> | padj <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| CNAG_00275 | 1072.9219 | -4.048106 | 0.10667356 | -37.94854 | 0 | 0 |\n", "| CNAG_00309 | 539.1312 | -3.331524 | 0.08444635 | -39.45137 | 0 | 0 |\n", "| CNAG_00409 | 1093.4775 | -5.030655 | 0.07452918 | -67.49913 | 0 | 0 |\n", "| CNAG_00531 | 15371.8856 | 3.563691 | 0.05663035 | 62.92900 | 0 | 0 |\n", "| CNAG_00601 | 506.4752 | 4.661577 | 0.09625726 | 48.42832 | 0 | 0 |\n", "| CNAG_00897 | 12255.8281 | 4.856924 | 0.07970883 | 60.93332 | 0 | 0 |\n", "| CNAG_01272 | 49334.5209 | 1.961009 | 0.04521777 | 43.36812 | 0 | 0 |\n", "| CNAG_01275 | 2534.1015 | 2.273088 | 0.04888985 | 46.49406 | 0 | 0 |\n", "| CNAG_01344 | 3528.6160 | -1.582122 | 0.04028591 | -39.27234 | 0 | 0 |\n", "| CNAG_01713 | 9652.4535 | 2.016486 | 0.05310869 | 37.96904 | 0 | 0 |\n", "\n" ], "text/plain": [ " row baseMean log2FoldChange lfcSE stat pvalue padj\n", "1 CNAG_00275 1072.9219 -4.048106 0.10667356 -37.94854 0 0 \n", "2 CNAG_00309 539.1312 -3.331524 0.08444635 -39.45137 0 0 \n", "3 CNAG_00409 1093.4775 -5.030655 0.07452918 -67.49913 0 0 \n", "4 CNAG_00531 15371.8856 3.563691 0.05663035 62.92900 0 0 \n", "5 CNAG_00601 506.4752 4.661577 0.09625726 48.42832 0 0 \n", "6 CNAG_00897 12255.8281 4.856924 0.07970883 60.93332 0 0 \n", "7 CNAG_01272 49334.5209 1.961009 0.04521777 43.36812 0 0 \n", "8 CNAG_01275 2534.1015 2.273088 0.04888985 46.49406 0 0 \n", "9 CNAG_01344 3528.6160 -1.582122 0.04028591 -39.27234 0 0 \n", "10 CNAG_01713 9652.4535 2.016486 0.05310869 37.96904 0 0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Tidy the results for DE with respect to condition\n", "### Results are sorted in ascending order by adjusted p-value\n", "### Here ph4 is the reference level\n", "### log2FC > 0 suggests that higher pH (pH8) is associated with increased expression\n", "### log2FC < 0 suggests that higher pH (pH8) is associated with lower expression\n", "myres_condition8v4 <- results(ddsDE, contrast = c(\"condition\", \"pH8\", \"pH4\"), tidy = TRUE)\n", "\n", "myres_condition8v4 %>% \n", " arrange(desc(-padj)) %>% \n", " head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize DE effect" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the results for these two genes: \n", "\n", "* The estimated log2FC for CNAG_00275 is negative. We will verify visually that ph8, compared to pH4, is associated with lower expression\n", "\n", "* The estimated log2FC for CNAG_00531 is positive. We will verify visually that ph8, compared to pH4, is associated with higher expression\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A data.frame: 2 × 7
rowbaseMeanlog2FoldChangelfcSEstatpvaluepadj
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
CNAG_00275 1072.922-0.32684900.10536149-3.1021670.00192109430.005769654
CNAG_0053115371.886 0.19024240.05662427 3.3597330.00078017940.002625616
\n" ], "text/latex": [ "A data.frame: 2 × 7\n", "\\begin{tabular}{r|lllllll}\n", " row & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t CNAG\\_00275 & 1072.922 & -0.3268490 & 0.10536149 & -3.102167 & 0.0019210943 & 0.005769654\\\\\n", "\t CNAG\\_00531 & 15371.886 & 0.1902424 & 0.05662427 & 3.359733 & 0.0007801794 & 0.002625616\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 7\n", "\n", "| row <chr> | baseMean <dbl> | log2FoldChange <dbl> | lfcSE <dbl> | stat <dbl> | pvalue <dbl> | padj <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| CNAG_00275 | 1072.922 | -0.3268490 | 0.10536149 | -3.102167 | 0.0019210943 | 0.005769654 |\n", "| CNAG_00531 | 15371.886 | 0.1902424 | 0.05662427 | 3.359733 | 0.0007801794 | 0.002625616 |\n", "\n" ], "text/plain": [ " row baseMean log2FoldChange lfcSE stat pvalue \n", "1 CNAG_00275 1072.922 -0.3268490 0.10536149 -3.102167 0.0019210943\n", "2 CNAG_00531 15371.886 0.1902424 0.05662427 3.359733 0.0007801794\n", " padj \n", "1 0.005769654\n", "2 0.002625616" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results(ddsDE, tidy = TRUE) %>%\n", " filter(row %in% c(\"CNAG_00275\",\"CNAG_00531\"))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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fESTE8u1HSEXE8h1BQizf\nfoQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAA\nAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAA\nAYQECCAkE/z1tEoN/pDru9iyxqhdw6qOsKwNV9VNrDPgK/tjrdOcM1RXy+qn9o7NSGr9F72z\nxihCMsAyX8acRWcNVpmWlaUmZVzxgLWpZvWZL0yvWm1N0ZAuU317Pzy9lnpN87gxiZAMcH7c\nasvKOdUJabJqvdN+z2C1zH77rrqsaEiZqnu+Za1UnbUOG6MIKfodSvydc1gUCGm2vcyr0tj/\nkaYpecVCWuosW8Qf0DRoLCOk6LdZXeoc1gRCet1eblG9/R/ppbYWC2mts+ytNmsaNJYRUvRb\no650DjsCIa20l2tVf/9HLrXTKRrSNmfZP9ATPEVI0W9jIJuvAyGtspdbVS//R3ra6fhDyg2E\ntNF5Z2/1na5JYxghRb8cXzvn8EwopPxqDf0faVgt32qbbC/WBUJyvu2zWiYd0jVpDCMkA5we\nv8GyDnQIhWQNU2/ab5epoc7PSZss69ZASIPsd65WPbQOG6MIyQBPqyYPPn326CIhbamTfs/L\ns9IztlrWfersR8f1rnOuE9K5mYsfqOf7t+55YxEhmeDPzZNOmLNDXVEYkrXpqoyEeiOcH4YO\nZNWtkvlzk45OSBvG1k1q85LeWWMUIRnjMzU2/AmZaoc3k+BohGSA+Wd9Yr+dqkrYRUdIGhGS\nAf5bqd6sx0fFtS1hxwIhaURIJnivT+3ExuN3l3AWIWlESIAAQgIEEBIggJAAAYQECCAkQAAh\nAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAh\nAQIICRBASIAAQgIEEBIg4P8BDw5SjZmRFuIAAAAASUVORK5CYII=", "text/plain": [ "Plot with title “CNAG_00275”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### This dot plot verify visually that exposure to ph8, compared to pH4, is associated with lower expression\n", "plotCounts(dds2019, \"CNAG_00275\", intgroup = \"condition\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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CqY2b/psYP+Ye8fbOCsVj3UfpyfI0WK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\noi5MAAAAAElFTkSuQmCC", "text/plain": [ "Plot with title “CNAG_00531”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### This dot plot verify visually that exposure to ph8, compared to pH4, is associated with higher expression\n", "plotCounts(dds2019, \"CNAG_00531\", intgroup = \"condition\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Volcano plot" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Removed 348 rows containing missing values (geom_point).”" ] }, { "data": { "image/png": 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5A2YIIy4RiWyeYsA0jMApAopR+QEmsh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\nfbOT3lcyUrzq6Z0WIHErU4Mkf36fpaeRSOLiAmA+2zNvoa0ps4pLBYgtwwB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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Volcano plot for con effect\n", "ggplot(results(ddsDE, contrast = c(\"condition\", \"pH4\", \"pH8\"), tidy = TRUE), \n", " aes(x = log2FoldChange, y = -log10(padj))) + geom_point()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Removed 348 rows containing missing values (geom_point).”" ] }, { "data": { "image/png": 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l0GkhKQNCwDSTQgGQ1IsstAUgKShmUg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\nYzaQTAakeveV+/NWSfx/+7hn7EnOI3Vv59yKtbpfhojfpy3XQqr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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Genotype Effect\n", "ggplot(results(ddsDE, contrast = c(\"genotype\", \"sre1d\", \"WT\"), tidy = TRUE), \n", " aes(x = log2FoldChange, y = -log10(padj))) + geom_point()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regularized log transformation\n", "The regularized log transform can be obtained using the [rlog() function](https://rdrr.io/bioc/DESeq2/man/rlog.html). Note that an important argument for this function is blind (TRUE by default). The default \"blinds\" the normalization to the design. This is very important so as to not bias the analyses (e.g. class discovery) " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "rld <- rlog(dds2019, blind = TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dendrogram of samples: showing strain & media of each sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hierarchical clustering using rlog transformation" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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C6O3QOG7W9ajuAYh3g0o3vxWsYnV42grEoW5VOV7f1F+5qps2ZOGV4hBju1aogD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\nXz1kzNpibOeBzztDCvKzJ69+LzwpSm3ScQ/bakeVqBw5ZcaMSSe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"text/plain": [ "Plot with title “Cluster Dendrogram”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 5)\n", "dists <- dist(t(assay(rld)))\n", "plot(hclust(dists)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store the dendrogram of samples using hierarchical clustering" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "assay(rld) %>%\n", " t() %>%\n", " dist %>%\n", " hclust(method = \"complete\") %>%\n", " as.dendrogram ->\n", " mydend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dendrogram of samples: showing strain of each sample" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "\n", "dendplot <- function(mydend, columndata, labvar, colvar, pchvar) {\n", " cols <- factor(columndata[[colvar]][order.dendrogram(mydend)])\n", " collab <- brewer.pal(max(3,nlevels(cols)),\"Set1\")[cols]\n", " pchs <- factor(columndata[[pchvar]][order.dendrogram(mydend)])\n", " pchlab <- seq_len(nlevels(pchs))[pchs]\n", " lablab <- columndata[[labvar]][order.dendrogram(mydend)]\n", " \n", " mydend %>% \n", " set(\"labels_cex\",1) %>% \n", " set(\"labels_col\",collab) %>%\n", " set(\"leaves_pch\",pchlab) %>%\n", " set(\"labels\", lablab)\n", "}\n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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cuXL//nn3+qdcDc3Nzbt2/7+PiodcA1a9bs3LlTraOhcAQVizd//vx79+65urqa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\n09PTExMTjx8/npqa2rx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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 5)\n", "dendplot(mydend, dds2019@colData, \n", " \"genotype\", # variable that show in label\n", " \"genotype\", # variable that define color\n", " \"condition\") %>% # variable that define shape of points\n", " plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dendrogram of samples: showing media of each sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Customize presentation" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 24 × 4
Labelgenotypeconditiongeneexp
<chr><fct><fct><dbl>
1_2019_P_M1 WT pH4126.28489
2_2019_P_M1 WT pH4 94.04893
3_2019_P_M1 WT pH4106.41852
4_2019_P_M1 WT pH4120.45064
5_2019_P_M1 WT pH4112.70469
6_2019_P_M1 WT pH4 83.25923
7_2019_P_M1 sre1dpH4122.72715
8_2019_P_M1 sre1dpH4117.87771
9_2019_P_M1 sre1dpH4149.00707
10_2019_P_M1sre1dpH4121.70596
11_2019_P_M1sre1dpH4116.10471
12_2019_P_M1sre1dpH4155.47163
13_2019_P_M1WT pH8278.86338
14_2019_P_M1WT pH8204.68325
15_2019_P_M1WT pH8274.49575
16_2019_P_M1WT pH8217.18785
17_2019_P_M1WT pH8236.11254
18_2019_P_M1WT pH8215.75364
19_2019_P_M1sre1dpH8157.45180
20_2019_P_M1sre1dpH8171.48644
21_2019_P_M1sre1dpH8186.69737
22_2019_P_M1sre1dpH8178.12113
23_2019_P_M1sre1dpH8203.92325
24_2019_P_M1sre1dpH8208.94213
\n" ], "text/latex": [ "A tibble: 24 × 4\n", "\\begin{tabular}{r|llll}\n", " Label & genotype & condition & geneexp\\\\\n", " & & & \\\\\n", "\\hline\n", "\t 1\\_2019\\_P\\_M1 & WT & pH4 & 126.28489\\\\\n", "\t 2\\_2019\\_P\\_M1 & WT & pH4 & 94.04893\\\\\n", "\t 3\\_2019\\_P\\_M1 & WT & pH4 & 106.41852\\\\\n", "\t 4\\_2019\\_P\\_M1 & WT & pH4 & 120.45064\\\\\n", "\t 5\\_2019\\_P\\_M1 & WT & pH4 & 112.70469\\\\\n", "\t 6\\_2019\\_P\\_M1 & WT & pH4 & 83.25923\\\\\n", "\t 7\\_2019\\_P\\_M1 & sre1d & pH4 & 122.72715\\\\\n", "\t 8\\_2019\\_P\\_M1 & sre1d & pH4 & 117.87771\\\\\n", "\t 9\\_2019\\_P\\_M1 & sre1d & pH4 & 149.00707\\\\\n", "\t 10\\_2019\\_P\\_M1 & sre1d & pH4 & 121.70596\\\\\n", "\t 11\\_2019\\_P\\_M1 & sre1d & pH4 & 116.10471\\\\\n", "\t 12\\_2019\\_P\\_M1 & sre1d & pH4 & 155.47163\\\\\n", "\t 13\\_2019\\_P\\_M1 & WT & pH8 & 278.86338\\\\\n", "\t 14\\_2019\\_P\\_M1 & WT & pH8 & 204.68325\\\\\n", "\t 15\\_2019\\_P\\_M1 & WT & pH8 & 274.49575\\\\\n", "\t 16\\_2019\\_P\\_M1 & WT & pH8 & 217.18785\\\\\n", "\t 17\\_2019\\_P\\_M1 & WT & pH8 & 236.11254\\\\\n", "\t 18\\_2019\\_P\\_M1 & WT & pH8 & 215.75364\\\\\n", "\t 19\\_2019\\_P\\_M1 & sre1d & pH8 & 157.45180\\\\\n", "\t 20\\_2019\\_P\\_M1 & sre1d & pH8 & 171.48644\\\\\n", "\t 21\\_2019\\_P\\_M1 & sre1d & pH8 & 186.69737\\\\\n", "\t 22\\_2019\\_P\\_M1 & sre1d & pH8 & 178.12113\\\\\n", "\t 23\\_2019\\_P\\_M1 & sre1d & pH8 & 203.92325\\\\\n", "\t 24\\_2019\\_P\\_M1 & sre1d & pH8 & 208.94213\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 24 × 4\n", "\n", "| Label <chr> | genotype <fct> | condition <fct> | geneexp <dbl> |\n", "|---|---|---|---|\n", "| 1_2019_P_M1 | WT | pH4 | 126.28489 |\n", "| 2_2019_P_M1 | WT | pH4 | 94.04893 |\n", "| 3_2019_P_M1 | WT | pH4 | 106.41852 |\n", "| 4_2019_P_M1 | WT | pH4 | 120.45064 |\n", "| 5_2019_P_M1 | WT | pH4 | 112.70469 |\n", "| 6_2019_P_M1 | WT | pH4 | 83.25923 |\n", "| 7_2019_P_M1 | sre1d | pH4 | 122.72715 |\n", "| 8_2019_P_M1 | sre1d | pH4 | 117.87771 |\n", "| 9_2019_P_M1 | sre1d | pH4 | 149.00707 |\n", "| 10_2019_P_M1 | sre1d | pH4 | 121.70596 |\n", "| 11_2019_P_M1 | sre1d | pH4 | 116.10471 |\n", "| 12_2019_P_M1 | sre1d | pH4 | 155.47163 |\n", "| 13_2019_P_M1 | WT | pH8 | 278.86338 |\n", "| 14_2019_P_M1 | WT | pH8 | 204.68325 |\n", "| 15_2019_P_M1 | WT | pH8 | 274.49575 |\n", "| 16_2019_P_M1 | WT | pH8 | 217.18785 |\n", "| 17_2019_P_M1 | WT | pH8 | 236.11254 |\n", "| 18_2019_P_M1 | WT | pH8 | 215.75364 |\n", "| 19_2019_P_M1 | sre1d | pH8 | 157.45180 |\n", "| 20_2019_P_M1 | sre1d | pH8 | 171.48644 |\n", "| 21_2019_P_M1 | sre1d | pH8 | 186.69737 |\n", "| 22_2019_P_M1 | sre1d | pH8 | 178.12113 |\n", "| 23_2019_P_M1 | sre1d | pH8 | 203.92325 |\n", "| 24_2019_P_M1 | sre1d | pH8 | 208.94213 |\n", "\n" ], "text/plain": [ " Label genotype condition geneexp \n", "1 1_2019_P_M1 WT pH4 126.28489\n", "2 2_2019_P_M1 WT pH4 94.04893\n", "3 3_2019_P_M1 WT pH4 106.41852\n", "4 4_2019_P_M1 WT pH4 120.45064\n", "5 5_2019_P_M1 WT pH4 112.70469\n", "6 6_2019_P_M1 WT pH4 83.25923\n", "7 7_2019_P_M1 sre1d pH4 122.72715\n", "8 8_2019_P_M1 sre1d pH4 117.87771\n", "9 9_2019_P_M1 sre1d pH4 149.00707\n", "10 10_2019_P_M1 sre1d pH4 121.70596\n", "11 11_2019_P_M1 sre1d pH4 116.10471\n", "12 12_2019_P_M1 sre1d pH4 155.47163\n", "13 13_2019_P_M1 WT pH8 278.86338\n", "14 14_2019_P_M1 WT pH8 204.68325\n", "15 15_2019_P_M1 WT pH8 274.49575\n", "16 16_2019_P_M1 WT pH8 217.18785\n", "17 17_2019_P_M1 WT pH8 236.11254\n", "18 18_2019_P_M1 WT pH8 215.75364\n", "19 19_2019_P_M1 sre1d pH8 157.45180\n", "20 20_2019_P_M1 sre1d pH8 171.48644\n", "21 21_2019_P_M1 sre1d pH8 186.69737\n", "22 22_2019_P_M1 sre1d pH8 178.12113\n", "23 23_2019_P_M1 sre1d pH8 203.92325\n", "24 24_2019_P_M1 sre1d pH8 208.94213" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Merge gene expression with meta data\n", "myDEplotData <- function(mydds, geneid, mergelab) {\n", " counts(mydds, normalize = TRUE) %>%\n", " as_tibble(rownames=\"gene\") %>%\n", " filter(gene == geneid) %>%\n", " gather(Label, geneexp, -gene) %>%\n", " select(-gene) -> genedat\n", "\n", " colData(mydds) %>%\n", " as.data.frame %>%\n", " as_tibble %>%\n", " full_join(genedat, by = mergelab) -> genedat\n", " \n", " return(genedat)\n", "}\n", "\n", "myDEplotData(dds2019, \"CNAG_00003\", \"Label\")[,c(\"Label\", \"genotype\", \"condition\" , \"geneexp\")]\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "### Basic function\n", "\n", "myDEplot <- function(mydds, geneid, mergelab) {\n", " mydat <- myDEplotData(mydds, geneid, mergelab)\n", " ggplot(mydat, aes(x = condition, y = geneexp))+ geom_point()\n", "}\n", "\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Allow for grouping by any factor in dataframe\n", "\n", "myDEplot <- function(mydds, geneid, grpvar, mergelab) {\n", " mydat <- myDEplotData(mydds, geneid, mergelab)\n", " ggplot(mydat, aes_string(x=grpvar, y = \"geneexp\"))+ geom_point()\n", "}\n", "\n", "myDEplot(dds2019, \"CNAG_00003\", \"genotype\", \"Label\")\n", "myDEplot(dds2019, \"CNAG_00003\", \"condition\", \"Label\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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5E3aevWrWEY/vCHP3zHO95RXV195JFHPv7442/yc+fOndvR0fHRj360x19997pf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\nBQAAiB1FBQAAiB1FBQA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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Add color\n", "\n", "myDEplot <- function(mydds, geneid, grpvar, mergelab) {\n", " mydat <- myDEplotData(mydds, geneid, mergelab)\n", " ggplot(mydat, aes_string(x=grpvar, y = \"geneexp\", col = grpvar))+ geom_point()\n", "}\n", "myDEplot(dds2019, \"CNAG_00003\", \"condition\", \"Label\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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dnc/HzzRkUFPlx9fX3bfv5eUlKSTCbb8ISF0vLSr4ceemj69OmNjY0XX3xx07Cq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\nFs2YMWP48OFDhw6dMWP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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Alow for coloring with respect to another factor\n", "myDEplot <- function(mydds, geneid, grpvar, colvar, mergelab) {\n", " mydat <- myDEplotData(mydds, geneid, mergelab)\n", " ggplot(mydat, aes_string(x=grpvar, y = \"geneexp\", col = colvar))+ geom_point()\n", "}\n", "myDEplot(dds2019, \"CNAG_00003\", \"condition\", \"genotype\", \"Label\")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "R version 3.6.0 (2019-04-26)\n", "Platform: x86_64-pc-linux-gnu (64-bit)\n", "Running under: Debian GNU/Linux 9 (stretch)\n", "\n", "Matrix products: default\n", "BLAS: /usr/lib/openblas-base/libblas.so.3\n", "LAPACK: /usr/lib/libopenblasp-r0.2.19.so\n", "\n", "locale:\n", " [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \n", " [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \n", " [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \n", " [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \n", " [9] LC_ADDRESS=C LC_TELEPHONE=C \n", "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \n", "\n", "attached base packages:\n", "[1] parallel stats4 stats graphics grDevices utils datasets \n", "[8] methods base \n", "\n", "other attached packages:\n", " [1] RColorBrewer_1.1-2 dendextend_1.12.0 \n", " [3] DESeq2_1.24.0 SummarizedExperiment_1.14.0\n", " [5] DelayedArray_0.10.0 BiocParallel_1.18.0 \n", " [7] matrixStats_0.54.0 Biobase_2.44.0 \n", " [9] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0 \n", "[11] IRanges_2.18.1 S4Vectors_0.22.0 \n", "[13] BiocGenerics_0.30.0 forcats_0.4.0 \n", "[15] stringr_1.4.0 dplyr_0.8.1 \n", "[17] purrr_0.3.2 readr_1.3.1 \n", "[19] tidyr_0.8.3 tibble_2.1.2 \n", "[21] ggplot2_3.1.1 tidyverse_1.2.1 \n", "\n", "loaded via a namespace (and not attached):\n", " [1] colorspace_1.4-1 IRdisplay_0.7.0 htmlTable_1.13.1 \n", " [4] XVector_0.24.0 base64enc_0.1-3 rstudioapi_0.10 \n", " [7] bit64_0.9-7 AnnotationDbi_1.46.0 lubridate_1.7.4 \n", "[10] xml2_1.2.0 splines_3.6.0 geneplotter_1.62.0 \n", "[13] knitr_1.23 zeallot_0.1.0 IRkernel_1.0.1 \n", "[16] Formula_1.2-3 jsonlite_1.6 broom_0.5.2 \n", "[19] annotate_1.62.0 cluster_2.0.8 compiler_3.6.0 \n", "[22] httr_1.4.0 backports_1.1.4 assertthat_0.2.1 \n", "[25] Matrix_1.2-17 lazyeval_0.2.2 cli_1.1.0 \n", "[28] acepack_1.4.1 htmltools_0.3.6 tools_3.6.0 \n", "[31] gtable_0.3.0 glue_1.3.1 GenomeInfoDbData_1.2.1\n", "[34] Rcpp_1.0.1 cellranger_1.1.0 vctrs_0.1.0 \n", "[37] nlme_3.1-139 xfun_0.7 rvest_0.3.4 \n", "[40] XML_3.98-1.19 zlibbioc_1.30.0 scales_1.0.0 \n", "[43] hms_0.4.2 memoise_1.1.0 gridExtra_2.3 \n", "[46] rpart_4.1-15 latticeExtra_0.6-28 stringi_1.4.3 \n", "[49] RSQLite_2.1.1 genefilter_1.66.0 checkmate_1.9.3 \n", "[52] repr_1.0.1 rlang_0.3.4 pkgconfig_2.0.2 \n", "[55] bitops_1.0-6 evaluate_0.14 lattice_0.20-38 \n", "[58] labeling_0.3 htmlwidgets_1.3 bit_1.1-14 \n", "[61] tidyselect_0.2.5 plyr_1.8.4 magrittr_1.5 \n", "[64] R6_2.4.0 generics_0.0.2 Hmisc_4.2-0 \n", "[67] pbdZMQ_0.3-3 DBI_1.0.0 pillar_1.4.1 \n", "[70] haven_2.1.0 foreign_0.8-71 withr_2.1.2 \n", "[73] survival_2.44-1.1 RCurl_1.95-4.12 nnet_7.3-12 \n", "[76] modelr_0.1.4 crayon_1.3.4 uuid_0.1-2 \n", "[79] viridis_0.5.1 locfit_1.5-9.1 grid_3.6.0 \n", "[82] readxl_1.3.1 data.table_1.12.2 blob_1.1.1 \n", "[85] digest_0.6.19 xtable_1.8-4 munsell_0.5.0 \n", "[88] viridisLite_0.3.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sessionInfo()" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }