{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Set up environment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading tidyverse: ggplot2\n", "Loading tidyverse: tibble\n", "Loading tidyverse: tidyr\n", "Loading tidyverse: readr\n", "Loading tidyverse: purrr\n", "Loading tidyverse: dplyr\n", "Conflicts with tidy packages ---------------------------------------------------\n", "filter(): dplyr, stats\n", "lag(): dplyr, stats\n", "\n", "Attaching package: ‘foreach’\n", "\n", "The following objects are masked from ‘package:purrr’:\n", "\n", " accumulate, when\n", "\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, cbind, colMeans, colnames,\n", " colSums, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n", " grepl, intersect, is.unsorted, lapply, lengths, Map, mapply, match,\n", " mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n", " rbind, Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,\n", " table, tapply, 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", "\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:base’:\n", "\n", " apply\n", "\n", "\n", "Attaching package: ‘limma’\n", "\n", "The following object is masked from ‘package:DESeq2’:\n", "\n", " plotMA\n", "\n", "The following object is masked from ‘package:BiocGenerics’:\n", "\n", " plotMA\n", "\n", "\n", "Attaching package: ‘gridExtra’\n", "\n", "The following object is masked from ‘package:Biobase’:\n", "\n", " combine\n", "\n", "The following object is masked from ‘package:BiocGenerics’:\n", "\n", " combine\n", "\n", "The following object is masked from ‘package:dplyr’:\n", "\n", " combine\n", "\n", "\n", "---------------------\n", "Welcome to dendextend version 1.8.0\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", "\n", "Attaching package: ‘plotly’\n", "\n", "The following object is masked from ‘package:IRanges’:\n", "\n", " slice\n", "\n", "The following object is masked from ‘package:S4Vectors’:\n", "\n", " rename\n", "\n", "The following object is masked from ‘package:ggplot2’:\n", "\n", " last_plot\n", "\n", "The following object is masked from ‘package:stats’:\n", "\n", " filter\n", "\n", "The following object is masked from ‘package:graphics’:\n", "\n", " layout\n", "\n" ] } ], "source": [ "source(\"pilot_config.R\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that after the tutorial one, we have created the hts-pilot-2018.RData.\n", "\n", "```\n", "scratch\n", "└── analysis_output \n", " ├── out \n", " │ └── hts-pilot-2018.RData \n", " └── img \n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "'/home/jovyan/work/scratch/analysis_output/out'" ], "text/latex": [ "'/home/jovyan/work/scratch/analysis\\_output/out'" ], "text/markdown": [ "'/home/jovyan/work/scratch/analysis_output/out'" ], "text/plain": [ "[1] \"/home/jovyan/work/scratch/analysis_output/out\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "OUTDIR" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# file directory\n", "cntfile <- file.path(OUTDIR, \"hts-pilot-2018.RData\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read in results" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "/home/jovyan/work/scratch/analysis_output/out/hts-pilot-2018.RData: 'e8d7991f674b7a9284abae8f38656c2e'" ], "text/latex": [ "\\textbf{/home/jovyan/work/scratch/analysis\\textbackslash{}\\_output/out/hts-pilot-2018.RData:} 'e8d7991f674b7a9284abae8f38656c2e'" ], "text/markdown": [ "**/home/jovyan/work/scratch/analysis_output/out/hts-pilot-2018.RData:** 'e8d7991f674b7a9284abae8f38656c2e'" ], "text/plain": [ "/home/jovyan/work/scratch/analysis_output/out/hts-pilot-2018.RData \n", " \"e8d7991f674b7a9284abae8f38656c2e\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Import count data\n", "attach(cntfile)\n", "tools::md5sum(cntfile)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "/home/jovyan/work/HTS2018-notebooks/josh/info/2018_pilot_metadata_anon.tsv: '20b31fdc4487d0cd8cc3c9300584faca'" ], "text/latex": [ "\\textbf{/home/jovyan/work/HTS2018-notebooks/josh/info/2018\\textbackslash{}\\_pilot\\textbackslash{}\\_metadata\\textbackslash{}\\_anon.tsv:} '20b31fdc4487d0cd8cc3c9300584faca'" ], "text/markdown": [ "**/home/jovyan/work/HTS2018-notebooks/josh/info/2018_pilot_metadata_anon.tsv:** '20b31fdc4487d0cd8cc3c9300584faca'" ], "text/plain": [ "/home/jovyan/work/HTS2018-notebooks/josh/info/2018_pilot_metadata_anon.tsv \n", " \"20b31fdc4487d0cd8cc3c9300584faca\" " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Parsed with column specification:\n", "cols(\n", " Label = col_character(),\n", " RNA_sample_num = col_integer(),\n", " Media = col_character(),\n", " Strain = col_character(),\n", " Replicate = col_integer(),\n", " experiment_person = col_character(),\n", " libprep_person = col_character(),\n", " enrichment_method = col_character(),\n", " RIN = col_double(),\n", " concentration_fold_difference = col_double(),\n", " `i7 index` = col_character(),\n", " `i5 index` = col_character(),\n", " `i5 primer` = col_character(),\n", " `i7 primer` = col_character(),\n", " `library#` = col_integer()\n", ")\n" ] } ], "source": [ "### Import metadata\n", "tools::md5sum(METADTFILE)\n", "\n", "mtdf <- readr::read_tsv(METADTFILE) %>%\n", " mutate_at(vars(\n", " `Label`,\n", " `Strain`,\n", " `Media`,\n", " `enrichment_method`,\n", " `library#`,\n", " `libprep_person`,\n", " `experiment_person`\n", " ), factor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that there are 204 samples and 8498 genes" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
LabelRNA_sample_numMediaStrainReplicateexperiment_personlibprep_personenrichment_methodRINconcentration_fold_differencei7 indexi5 indexi5 primeri7 primerlibrary#
2_MA_C 2 YPD H99 2 expA prepA MA 10.0 1.34 ATTACTCGAGGCTATAi501 i701 1
9_MA_C 9 YPD mar1d 3 expA prepA MA 10.0 2.23 ATTACTCGGCCTCTATi502 i701 2
10_MA_C 10 YPD mar1d 4 expA prepA MA 9.9 4.37 ATTACTCGAGGATAGGi503 i701 3
14_MA_C 14 TC H99 2 expA prepA MA 10.0 1.57 ATTACTCGTCAGAGCCi504 i701 4
15_MA_C 15 TC H99 3 expA prepA MA 9.9 2.85 ATTACTCGCTTCGCCTi505 i701 5
21_MA_C 21 TC mar1d 3 expA prepA MA 10.0 1.81 ATTACTCGTAAGATTAi506 i701 6
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllll}\n", " Label & RNA\\_sample\\_num & Media & Strain & Replicate & experiment\\_person & libprep\\_person & enrichment\\_method & RIN & concentration\\_fold\\_difference & i7 index & i5 index & i5 primer & i7 primer & library\\#\\\\\n", "\\hline\n", "\t 2\\_MA\\_C & 2 & YPD & H99 & 2 & expA & prepA & MA & 10.0 & 1.34 & ATTACTCG & AGGCTATA & i501 & i701 & 1 \\\\\n", "\t 9\\_MA\\_C & 9 & YPD & mar1d & 3 & expA & prepA & MA & 10.0 & 2.23 & ATTACTCG & GCCTCTAT & i502 & i701 & 2 \\\\\n", "\t 10\\_MA\\_C & 10 & YPD & mar1d & 4 & expA & prepA & MA & 9.9 & 4.37 & ATTACTCG & AGGATAGG & i503 & i701 & 3 \\\\\n", "\t 14\\_MA\\_C & 14 & TC & H99 & 2 & expA & prepA & MA & 10.0 & 1.57 & ATTACTCG & TCAGAGCC & i504 & i701 & 4 \\\\\n", "\t 15\\_MA\\_C & 15 & TC & H99 & 3 & expA & prepA & MA & 9.9 & 2.85 & ATTACTCG & CTTCGCCT & i505 & i701 & 5 \\\\\n", "\t 21\\_MA\\_C & 21 & TC & mar1d & 3 & expA & prepA & MA & 10.0 & 1.81 & ATTACTCG & TAAGATTA & i506 & i701 & 6 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Label | RNA_sample_num | Media | Strain | Replicate | experiment_person | libprep_person | enrichment_method | RIN | concentration_fold_difference | i7 index | i5 index | i5 primer | i7 primer | library# | \n", "|---|---|---|---|---|---|\n", "| 2_MA_C | 2 | YPD | H99 | 2 | expA | prepA | MA | 10.0 | 1.34 | ATTACTCG | AGGCTATA | i501 | i701 | 1 | \n", "| 9_MA_C | 9 | YPD | mar1d | 3 | expA | prepA | MA | 10.0 | 2.23 | ATTACTCG | GCCTCTAT | i502 | i701 | 2 | \n", "| 10_MA_C | 10 | YPD | mar1d | 4 | expA | prepA | MA | 9.9 | 4.37 | ATTACTCG | AGGATAGG | i503 | i701 | 3 | \n", "| 14_MA_C | 14 | TC | H99 | 2 | expA | prepA | MA | 10.0 | 1.57 | ATTACTCG | TCAGAGCC | i504 | i701 | 4 | \n", "| 15_MA_C | 15 | TC | H99 | 3 | expA | prepA | MA | 9.9 | 2.85 | ATTACTCG | CTTCGCCT | i505 | i701 | 5 | \n", "| 21_MA_C | 21 | TC | mar1d | 3 | expA | prepA | MA | 10.0 | 1.81 | ATTACTCG | TAAGATTA | i506 | i701 | 6 | \n", "\n", "\n" ], "text/plain": [ " Label RNA_sample_num Media Strain Replicate experiment_person\n", "1 2_MA_C 2 YPD H99 2 expA \n", "2 9_MA_C 9 YPD mar1d 3 expA \n", "3 10_MA_C 10 YPD mar1d 4 expA \n", "4 14_MA_C 14 TC H99 2 expA \n", "5 15_MA_C 15 TC H99 3 expA \n", "6 21_MA_C 21 TC mar1d 3 expA \n", " libprep_person enrichment_method RIN concentration_fold_difference i7 index\n", "1 prepA MA 10.0 1.34 ATTACTCG\n", "2 prepA MA 10.0 2.23 ATTACTCG\n", "3 prepA MA 9.9 4.37 ATTACTCG\n", "4 prepA MA 10.0 1.57 ATTACTCG\n", "5 prepA MA 9.9 2.85 ATTACTCG\n", "6 prepA MA 10.0 1.81 ATTACTCG\n", " i5 index i5 primer i7 primer library#\n", "1 AGGCTATA i501 i701 1 \n", "2 GCCTCTAT i502 i701 2 \n", "3 AGGATAGG i503 i701 3 \n", "4 TCAGAGCC i504 i701 4 \n", "5 CTTCGCCT i505 i701 5 \n", "6 TAAGATTA i506 i701 6 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(mtdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check the label between metadata and mapping results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that we got two data frames in the previous tutorial: \n", "- genecounts: gene counts for each **sample**\n", " - Note: We will need to convert it into gene counts for each **library** later\n", "- mapresults: the mapping results \n", " - Note: There are 204 samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The metadata (`mtdf`) contains the information of each sample. Here we need to make sure if the label in the metadata matches the sample names we have in `mapresults` and `genecount`\n", "\n", "The code chunk below allows us to check to see if we can match the labels to the those in the metadata file. There should not be any output from the code chunk." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/latex": [], "text/markdown": [], "text/plain": [ "character(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/latex": [], "text/markdown": [], "text/plain": [ "character(0)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Use setdiff to check to see if we can match the labels to the those in the metadata file\n", "myregex <- \"_[A-Z](100|[1-9][0-9]?)_L00[1-4].*\"\n", "\n", "mapresults$expid %>%\n", " str_replace(myregex, \"\") %>%\n", " setdiff(mtdf$Label)\n", "\n", "mtdf$Label %>%\n", " setdiff(str_replace(mapresults$expid, myregex, \"\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Construct gene count matrix for each library" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add the \"Label\" to the count matrix and mapping results, then merge in phenotype data (by Label)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "### Add \"Label\" to genecounts\n", "genecounts %>%\n", " mutate(Label = str_replace(expid, myregex, \"\")) ->\n", " annogenecnts" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "### Collapse the gene counts within each label\n", "annogenecnts %>%\n", " select(-expid) %>%\n", " group_by(Label) %>%\n", " summarize_all(sum) %>%\n", " gather(gene, value, -Label) %>% \n", " spread(Label, value) ->\n", " annogenecnts0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the resulting data frame in each step" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
expidCNAG_00001CNAG_00002CNAG_00003CNAG_00004CNAG_00005
1_MA_J_S18_L001_ReadsPerGene.out.tab0 66 38 74 33
1_MA_J_S18_L002_ReadsPerGene.out.tab0 59 25 79 25
1_MA_J_S18_L003_ReadsPerGene.out.tab0 74 27 79 32
1_MA_J_S18_L004_ReadsPerGene.out.tab0 66 22 69 24
1_RZ_J_S26_L001_ReadsPerGene.out.tab0 50 16 51 26
1_RZ_J_S26_L002_ReadsPerGene.out.tab0 45 7 51 31
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " expid & CNAG\\_00001 & CNAG\\_00002 & CNAG\\_00003 & CNAG\\_00004 & CNAG\\_00005\\\\\n", "\\hline\n", "\t 1\\_MA\\_J\\_S18\\_L001\\_ReadsPerGene.out.tab & 0 & 66 & 38 & 74 & 33 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L002\\_ReadsPerGene.out.tab & 0 & 59 & 25 & 79 & 25 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L003\\_ReadsPerGene.out.tab & 0 & 74 & 27 & 79 & 32 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L004\\_ReadsPerGene.out.tab & 0 & 66 & 22 & 69 & 24 \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L001\\_ReadsPerGene.out.tab & 0 & 50 & 16 & 51 & 26 \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L002\\_ReadsPerGene.out.tab & 0 & 45 & 7 & 51 & 31 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "expid | CNAG_00001 | CNAG_00002 | CNAG_00003 | CNAG_00004 | CNAG_00005 | \n", "|---|---|---|---|---|---|\n", "| 1_MA_J_S18_L001_ReadsPerGene.out.tab | 0 | 66 | 38 | 74 | 33 | \n", "| 1_MA_J_S18_L002_ReadsPerGene.out.tab | 0 | 59 | 25 | 79 | 25 | \n", "| 1_MA_J_S18_L003_ReadsPerGene.out.tab | 0 | 74 | 27 | 79 | 32 | \n", "| 1_MA_J_S18_L004_ReadsPerGene.out.tab | 0 | 66 | 22 | 69 | 24 | \n", "| 1_RZ_J_S26_L001_ReadsPerGene.out.tab | 0 | 50 | 16 | 51 | 26 | \n", "| 1_RZ_J_S26_L002_ReadsPerGene.out.tab | 0 | 45 | 7 | 51 | 31 | \n", "\n", "\n" ], "text/plain": [ " expid CNAG_00001 CNAG_00002 CNAG_00003\n", "1 1_MA_J_S18_L001_ReadsPerGene.out.tab 0 66 38 \n", "2 1_MA_J_S18_L002_ReadsPerGene.out.tab 0 59 25 \n", "3 1_MA_J_S18_L003_ReadsPerGene.out.tab 0 74 27 \n", "4 1_MA_J_S18_L004_ReadsPerGene.out.tab 0 66 22 \n", "5 1_RZ_J_S26_L001_ReadsPerGene.out.tab 0 50 16 \n", "6 1_RZ_J_S26_L002_ReadsPerGene.out.tab 0 45 7 \n", " CNAG_00004 CNAG_00005\n", "1 74 33 \n", "2 79 25 \n", "3 79 32 \n", "4 69 24 \n", "5 51 26 \n", "6 51 31 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "genecounts[1:6, 1:6]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
expidCNAG_00001CNAG_00002CNAG_00003CNAG_00004CNAG_00005Label
1_MA_J_S18_L001_ReadsPerGene.out.tab0 66 38 74 33 1_MA_J
1_MA_J_S18_L002_ReadsPerGene.out.tab0 59 25 79 25 1_MA_J
1_MA_J_S18_L003_ReadsPerGene.out.tab0 74 27 79 32 1_MA_J
1_MA_J_S18_L004_ReadsPerGene.out.tab0 66 22 69 24 1_MA_J
1_RZ_J_S26_L001_ReadsPerGene.out.tab0 50 16 51 26 1_RZ_J
1_RZ_J_S26_L002_ReadsPerGene.out.tab0 45 7 51 31 1_RZ_J
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " expid & CNAG\\_00001 & CNAG\\_00002 & CNAG\\_00003 & CNAG\\_00004 & CNAG\\_00005 & Label\\\\\n", "\\hline\n", "\t 1\\_MA\\_J\\_S18\\_L001\\_ReadsPerGene.out.tab & 0 & 66 & 38 & 74 & 33 & 1\\_MA\\_J \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L002\\_ReadsPerGene.out.tab & 0 & 59 & 25 & 79 & 25 & 1\\_MA\\_J \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L003\\_ReadsPerGene.out.tab & 0 & 74 & 27 & 79 & 32 & 1\\_MA\\_J \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L004\\_ReadsPerGene.out.tab & 0 & 66 & 22 & 69 & 24 & 1\\_MA\\_J \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L001\\_ReadsPerGene.out.tab & 0 & 50 & 16 & 51 & 26 & 1\\_RZ\\_J \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L002\\_ReadsPerGene.out.tab & 0 & 45 & 7 & 51 & 31 & 1\\_RZ\\_J \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "expid | CNAG_00001 | CNAG_00002 | CNAG_00003 | CNAG_00004 | CNAG_00005 | Label | \n", "|---|---|---|---|---|---|\n", "| 1_MA_J_S18_L001_ReadsPerGene.out.tab | 0 | 66 | 38 | 74 | 33 | 1_MA_J | \n", "| 1_MA_J_S18_L002_ReadsPerGene.out.tab | 0 | 59 | 25 | 79 | 25 | 1_MA_J | \n", "| 1_MA_J_S18_L003_ReadsPerGene.out.tab | 0 | 74 | 27 | 79 | 32 | 1_MA_J | \n", "| 1_MA_J_S18_L004_ReadsPerGene.out.tab | 0 | 66 | 22 | 69 | 24 | 1_MA_J | \n", "| 1_RZ_J_S26_L001_ReadsPerGene.out.tab | 0 | 50 | 16 | 51 | 26 | 1_RZ_J | \n", "| 1_RZ_J_S26_L002_ReadsPerGene.out.tab | 0 | 45 | 7 | 51 | 31 | 1_RZ_J | \n", "\n", "\n" ], "text/plain": [ " expid CNAG_00001 CNAG_00002 CNAG_00003\n", "1 1_MA_J_S18_L001_ReadsPerGene.out.tab 0 66 38 \n", "2 1_MA_J_S18_L002_ReadsPerGene.out.tab 0 59 25 \n", "3 1_MA_J_S18_L003_ReadsPerGene.out.tab 0 74 27 \n", "4 1_MA_J_S18_L004_ReadsPerGene.out.tab 0 66 22 \n", "5 1_RZ_J_S26_L001_ReadsPerGene.out.tab 0 50 16 \n", "6 1_RZ_J_S26_L002_ReadsPerGene.out.tab 0 45 7 \n", " CNAG_00004 CNAG_00005 Label \n", "1 74 33 1_MA_J\n", "2 79 25 1_MA_J\n", "3 79 32 1_MA_J\n", "4 69 24 1_MA_J\n", "5 51 26 1_RZ_J\n", "6 51 31 1_RZ_J" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "annogenecnts[1:6, c(colnames(annogenecnts)[1:6], \"Label\")]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene1_MA_J1_RZ_J10_MA_C10_RZ_C11_MA_J
CNAG_00001 0 0 0 0 0
CNAG_00002 265 204 269 76 130
CNAG_00003 112 40 171 24 124
CNAG_00004 301 207 407 141 272
CNAG_00005 114 125 50 25 38
CNAG_000061904 1295 3571 1015 2073
\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " gene & 1\\_MA\\_J & 1\\_RZ\\_J & 10\\_MA\\_C & 10\\_RZ\\_C & 11\\_MA\\_J\\\\\n", "\\hline\n", "\t CNAG\\_00001 & 0 & 0 & 0 & 0 & 0 \\\\\n", "\t CNAG\\_00002 & 265 & 204 & 269 & 76 & 130 \\\\\n", "\t CNAG\\_00003 & 112 & 40 & 171 & 24 & 124 \\\\\n", "\t CNAG\\_00004 & 301 & 207 & 407 & 141 & 272 \\\\\n", "\t CNAG\\_00005 & 114 & 125 & 50 & 25 & 38 \\\\\n", "\t CNAG\\_00006 & 1904 & 1295 & 3571 & 1015 & 2073 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "gene | 1_MA_J | 1_RZ_J | 10_MA_C | 10_RZ_C | 11_MA_J | \n", "|---|---|---|---|---|---|\n", "| CNAG_00001 | 0 | 0 | 0 | 0 | 0 | \n", "| CNAG_00002 | 265 | 204 | 269 | 76 | 130 | \n", "| CNAG_00003 | 112 | 40 | 171 | 24 | 124 | \n", "| CNAG_00004 | 301 | 207 | 407 | 141 | 272 | \n", "| CNAG_00005 | 114 | 125 | 50 | 25 | 38 | \n", "| CNAG_00006 | 1904 | 1295 | 3571 | 1015 | 2073 | \n", "\n", "\n" ], "text/plain": [ " gene 1_MA_J 1_RZ_J 10_MA_C 10_RZ_C 11_MA_J\n", "1 CNAG_00001 0 0 0 0 0 \n", "2 CNAG_00002 265 204 269 76 130 \n", "3 CNAG_00003 112 40 171 24 124 \n", "4 CNAG_00004 301 207 407 141 272 \n", "5 CNAG_00005 114 125 50 25 38 \n", "6 CNAG_00006 1904 1295 3571 1015 2073 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "annogenecnts0[1:6, 1:6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add \"Label\" to read map results and merge in phenotype data (-> annomapres)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Column `Label` joining character vector and factor, coercing into character vector”" ] } ], "source": [ "mapresults %>%\n", " mutate(Label = str_replace(expid, myregex, \"\")) %>%\n", " full_join(mtdf, by = \"Label\") ->\n", " annomapres" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "grpvars <- vars(Label, Strain, Media, experiment_person, libprep_person, enrichment_method)\n", "sumvars <- vars(prop.gene, prop.nofeat, prop.unique, depth)\n", "\n", "annomapres %>%\n", " group_by_at(grpvars) %>%\n", " summarize_at(sumvars, mean) -> \n", " annomapres0" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
expidngenemapnambnmultinnofeatnunmapdepthprop.geneprop.nofeatprop.unique⋯experiment_personlibprep_personenrichment_methodRINconcentration_fold_differencei7 indexi5 indexi5 primeri7 primerlibrary#
1_MA_J_S18_L001_ReadsPerGene.out.tab2399781 647 66100 20347 2690 2489565 0.9639359 0.008172914 0.9721088 ⋯ expA prepB MA 10 3.64 CGCTCATT AGGCTATA i501 i703 18
1_MA_J_S18_L002_ReadsPerGene.out.tab2362228 652 65234 20004 2684 2450802 0.9638592 0.008162226 0.9720214 ⋯ expA prepB MA 10 3.64 CGCTCATT AGGCTATA i501 i703 18
1_MA_J_S18_L003_ReadsPerGene.out.tab2436776 697 66538 20549 2672 2527232 0.9642075 0.008131030 0.9723385 ⋯ expA prepB MA 10 3.64 CGCTCATT AGGCTATA i501 i703 18
1_MA_J_S18_L004_ReadsPerGene.out.tab2417485 616 65066 20505 2585 2506257 0.9645798 0.008181523 0.9727614 ⋯ expA prepB MA 10 3.64 CGCTCATT AGGCTATA i501 i703 18
1_RZ_J_S26_L001_ReadsPerGene.out.tab2366742 1431 395848 768146 7218 3539385 0.6686874 0.217028100 0.8857155 ⋯ expA prepB RZ 10 3.64 GAGATTCC AGGCTATA i501 i704 26
1_RZ_J_S26_L002_ReadsPerGene.out.tab2331658 1337 388079 755654 7022 3483750 0.6692954 0.216908217 0.8862037 ⋯ expA prepB RZ 10 3.64 GAGATTCC AGGCTATA i501 i704 26
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllll}\n", " expid & ngenemap & namb & nmulti & nnofeat & nunmap & depth & prop.gene & prop.nofeat & prop.unique & ⋯ & experiment\\_person & libprep\\_person & enrichment\\_method & RIN & concentration\\_fold\\_difference & i7 index & i5 index & i5 primer & i7 primer & library\\#\\\\\n", "\\hline\n", "\t 1\\_MA\\_J\\_S18\\_L001\\_ReadsPerGene.out.tab & 2399781 & 647 & 66100 & 20347 & 2690 & 2489565 & 0.9639359 & 0.008172914 & 0.9721088 & ⋯ & expA & prepB & MA & 10 & 3.64 & CGCTCATT & AGGCTATA & i501 & i703 & 18 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L002\\_ReadsPerGene.out.tab & 2362228 & 652 & 65234 & 20004 & 2684 & 2450802 & 0.9638592 & 0.008162226 & 0.9720214 & ⋯ & expA & prepB & MA & 10 & 3.64 & CGCTCATT & AGGCTATA & i501 & i703 & 18 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L003\\_ReadsPerGene.out.tab & 2436776 & 697 & 66538 & 20549 & 2672 & 2527232 & 0.9642075 & 0.008131030 & 0.9723385 & ⋯ & expA & prepB & MA & 10 & 3.64 & CGCTCATT & AGGCTATA & i501 & i703 & 18 \\\\\n", "\t 1\\_MA\\_J\\_S18\\_L004\\_ReadsPerGene.out.tab & 2417485 & 616 & 65066 & 20505 & 2585 & 2506257 & 0.9645798 & 0.008181523 & 0.9727614 & ⋯ & expA & prepB & MA & 10 & 3.64 & CGCTCATT & AGGCTATA & i501 & i703 & 18 \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L001\\_ReadsPerGene.out.tab & 2366742 & 1431 & 395848 & 768146 & 7218 & 3539385 & 0.6686874 & 0.217028100 & 0.8857155 & ⋯ & expA & prepB & RZ & 10 & 3.64 & GAGATTCC & AGGCTATA & i501 & i704 & 26 \\\\\n", "\t 1\\_RZ\\_J\\_S26\\_L002\\_ReadsPerGene.out.tab & 2331658 & 1337 & 388079 & 755654 & 7022 & 3483750 & 0.6692954 & 0.216908217 & 0.8862037 & ⋯ & expA & prepB & RZ & 10 & 3.64 & GAGATTCC & AGGCTATA & i501 & i704 & 26 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "expid | ngenemap | namb | nmulti | nnofeat | nunmap | depth | prop.gene | prop.nofeat | prop.unique | ⋯ | experiment_person | libprep_person | enrichment_method | RIN | concentration_fold_difference | i7 index | i5 index | i5 primer | i7 primer | library# | \n", "|---|---|---|---|---|---|\n", "| 1_MA_J_S18_L001_ReadsPerGene.out.tab | 2399781 | 647 | 66100 | 20347 | 2690 | 2489565 | 0.9639359 | 0.008172914 | 0.9721088 | ⋯ | expA | prepB | MA | 10 | 3.64 | CGCTCATT | AGGCTATA | i501 | i703 | 18 | \n", "| 1_MA_J_S18_L002_ReadsPerGene.out.tab | 2362228 | 652 | 65234 | 20004 | 2684 | 2450802 | 0.9638592 | 0.008162226 | 0.9720214 | ⋯ | expA | prepB | MA | 10 | 3.64 | CGCTCATT | AGGCTATA | i501 | i703 | 18 | \n", "| 1_MA_J_S18_L003_ReadsPerGene.out.tab | 2436776 | 697 | 66538 | 20549 | 2672 | 2527232 | 0.9642075 | 0.008131030 | 0.9723385 | ⋯ | expA | prepB | MA | 10 | 3.64 | CGCTCATT | AGGCTATA | i501 | i703 | 18 | \n", "| 1_MA_J_S18_L004_ReadsPerGene.out.tab | 2417485 | 616 | 65066 | 20505 | 2585 | 2506257 | 0.9645798 | 0.008181523 | 0.9727614 | ⋯ | expA | prepB | MA | 10 | 3.64 | CGCTCATT | AGGCTATA | i501 | i703 | 18 | \n", "| 1_RZ_J_S26_L001_ReadsPerGene.out.tab | 2366742 | 1431 | 395848 | 768146 | 7218 | 3539385 | 0.6686874 | 0.217028100 | 0.8857155 | ⋯ | expA | prepB | RZ | 10 | 3.64 | GAGATTCC | AGGCTATA | i501 | i704 | 26 | \n", "| 1_RZ_J_S26_L002_ReadsPerGene.out.tab | 2331658 | 1337 | 388079 | 755654 | 7022 | 3483750 | 0.6692954 | 0.216908217 | 0.8862037 | ⋯ | expA | prepB | RZ | 10 | 3.64 | GAGATTCC | AGGCTATA | i501 | i704 | 26 | \n", "\n", "\n" ], "text/plain": [ " expid ngenemap namb nmulti nnofeat nunmap\n", "1 1_MA_J_S18_L001_ReadsPerGene.out.tab 2399781 647 66100 20347 2690 \n", "2 1_MA_J_S18_L002_ReadsPerGene.out.tab 2362228 652 65234 20004 2684 \n", "3 1_MA_J_S18_L003_ReadsPerGene.out.tab 2436776 697 66538 20549 2672 \n", "4 1_MA_J_S18_L004_ReadsPerGene.out.tab 2417485 616 65066 20505 2585 \n", "5 1_RZ_J_S26_L001_ReadsPerGene.out.tab 2366742 1431 395848 768146 7218 \n", "6 1_RZ_J_S26_L002_ReadsPerGene.out.tab 2331658 1337 388079 755654 7022 \n", " depth prop.gene prop.nofeat prop.unique ⋯ experiment_person libprep_person\n", "1 2489565 0.9639359 0.008172914 0.9721088 ⋯ expA prepB \n", "2 2450802 0.9638592 0.008162226 0.9720214 ⋯ expA prepB \n", "3 2527232 0.9642075 0.008131030 0.9723385 ⋯ expA prepB \n", "4 2506257 0.9645798 0.008181523 0.9727614 ⋯ expA prepB \n", "5 3539385 0.6686874 0.217028100 0.8857155 ⋯ expA prepB \n", "6 3483750 0.6692954 0.216908217 0.8862037 ⋯ expA prepB \n", " enrichment_method RIN concentration_fold_difference i7 index i5 index\n", "1 MA 10 3.64 CGCTCATT AGGCTATA\n", "2 MA 10 3.64 CGCTCATT AGGCTATA\n", "3 MA 10 3.64 CGCTCATT AGGCTATA\n", "4 MA 10 3.64 CGCTCATT AGGCTATA\n", "5 RZ 10 3.64 GAGATTCC AGGCTATA\n", "6 RZ 10 3.64 GAGATTCC AGGCTATA\n", " i5 primer i7 primer library#\n", "1 i501 i703 18 \n", "2 i501 i703 18 \n", "3 i501 i703 18 \n", "4 i501 i703 18 \n", "5 i501 i704 26 \n", "6 i501 i704 26 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(annomapres)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
LabelStrainMediaexperiment_personlibprep_personenrichment_methodprop.geneprop.nofeatprop.uniquedepth
1_MA_J H99 YPD expA prepB MA 0.9641456 0.0081619230.9723075 2493464
1_RZ_J H99 YPD expA prepB RZ 0.6689001 0.2170956210.8859957 3541358
10_MA_C mar1d YPD expA prepA MA 0.9618651 0.0098185730.9716837 3282785
10_RZ_C mar1d YPD expA prepA RZ 0.7497438 0.2006516860.9503955 1742594
11_MA_J mar1d YPD expA prepB MA 0.9669597 0.0087178980.9756776 2062181
11_RZ_J mar1d YPD expA prepB RZ 0.7030020 0.1955471510.8985491 2621913
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllll}\n", " Label & Strain & Media & experiment\\_person & libprep\\_person & enrichment\\_method & prop.gene & prop.nofeat & prop.unique & depth\\\\\n", "\\hline\n", "\t 1\\_MA\\_J & H99 & YPD & expA & prepB & MA & 0.9641456 & 0.008161923 & 0.9723075 & 2493464 \\\\\n", "\t 1\\_RZ\\_J & H99 & YPD & expA & prepB & RZ & 0.6689001 & 0.217095621 & 0.8859957 & 3541358 \\\\\n", "\t 10\\_MA\\_C & mar1d & YPD & expA & prepA & MA & 0.9618651 & 0.009818573 & 0.9716837 & 3282785 \\\\\n", "\t 10\\_RZ\\_C & mar1d & YPD & expA & prepA & RZ & 0.7497438 & 0.200651686 & 0.9503955 & 1742594 \\\\\n", "\t 11\\_MA\\_J & mar1d & YPD & expA & prepB & MA & 0.9669597 & 0.008717898 & 0.9756776 & 2062181 \\\\\n", "\t 11\\_RZ\\_J & mar1d & YPD & expA & prepB & RZ & 0.7030020 & 0.195547151 & 0.8985491 & 2621913 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Label | Strain | Media | experiment_person | libprep_person | enrichment_method | prop.gene | prop.nofeat | prop.unique | depth | \n", "|---|---|---|---|---|---|\n", "| 1_MA_J | H99 | YPD | expA | prepB | MA | 0.9641456 | 0.008161923 | 0.9723075 | 2493464 | \n", "| 1_RZ_J | H99 | YPD | expA | prepB | RZ | 0.6689001 | 0.217095621 | 0.8859957 | 3541358 | \n", "| 10_MA_C | mar1d | YPD | expA | prepA | MA | 0.9618651 | 0.009818573 | 0.9716837 | 3282785 | \n", "| 10_RZ_C | mar1d | YPD | expA | prepA | RZ | 0.7497438 | 0.200651686 | 0.9503955 | 1742594 | \n", "| 11_MA_J | mar1d | YPD | expA | prepB | MA | 0.9669597 | 0.008717898 | 0.9756776 | 2062181 | \n", "| 11_RZ_J | mar1d | YPD | expA | prepB | RZ | 0.7030020 | 0.195547151 | 0.8985491 | 2621913 | \n", "\n", "\n" ], "text/plain": [ " Label Strain Media experiment_person libprep_person enrichment_method\n", "1 1_MA_J H99 YPD expA prepB MA \n", "2 1_RZ_J H99 YPD expA prepB RZ \n", "3 10_MA_C mar1d YPD expA prepA MA \n", "4 10_RZ_C mar1d YPD expA prepA RZ \n", "5 11_MA_J mar1d YPD expA prepB MA \n", "6 11_RZ_J mar1d YPD expA prepB RZ \n", " prop.gene prop.nofeat prop.unique depth \n", "1 0.9641456 0.008161923 0.9723075 2493464\n", "2 0.6689001 0.217095621 0.8859957 3541358\n", "3 0.9618651 0.009818573 0.9716837 3282785\n", "4 0.7497438 0.200651686 0.9503955 1742594\n", "5 0.9669597 0.008717898 0.9756776 2062181\n", "6 0.7030020 0.195547151 0.8985491 2621913" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(annomapres0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Store the results" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "/home/jovyan/work/scratch/analysis_output/out/HTS-Pilot-Annotated-STAR-counts.RData: '79338a9ad2efa4bcd1e6e9fd2a2b0ef7'" ], "text/latex": [ "\\textbf{/home/jovyan/work/scratch/analysis\\textbackslash{}\\_output/out/HTS-Pilot-Annotated-STAR-counts.RData:} '79338a9ad2efa4bcd1e6e9fd2a2b0ef7'" ], "text/markdown": [ "**/home/jovyan/work/scratch/analysis_output/out/HTS-Pilot-Annotated-STAR-counts.RData:** '79338a9ad2efa4bcd1e6e9fd2a2b0ef7'" ], "text/plain": [ "/home/jovyan/work/scratch/analysis_output/out/HTS-Pilot-Annotated-STAR-counts.RData \n", " \"79338a9ad2efa4bcd1e6e9fd2a2b0ef7\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "outfile <- file.path(OUTDIR, \"HTS-Pilot-Annotated-STAR-counts.RData\")\n", "save(mtdf, annogenecnts0, annomapres0, annogenecnts, annomapres, file = outfile)\n", "tools::md5sum(outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualize the mapping results" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "### Figures for mapping results\n", "mygeom <- geom_point(position = position_jitter(w = 0.3, h = 0))\n", "mypal <- scale_colour_manual(name=\"\", values =brewer.pal(3,\"Set1\"))\n", "mytheme <- theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme_bw()\n", "myfacet <- facet_grid(Strain~ Media, drop=TRUE, scales=\"free_x\", space=\"free\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show the fraction of unique mapped reads among all reads (prop.unique)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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19/1VVX+Xy+F198saKiYsaMGd1bLNIi4wsVTdPC4XBra2tXDRRF0e/5ICMcDgsh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\nzFMlNputpqampqYmemR0y3HjxkX/GXM5jzzyyCOPPCITfxpxjQo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+j3SVG6FQyNBykOmkCpULLrhACFFfX//+++93nprY6ra5ufn++++f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\nKnnUQRBRPt6nAgxiSexQruTk5AULFsyfP3/KlClvv/22fDdS7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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 6)\n", "\n", "p1 <- ggplot(annomapres, \n", " aes(x = factor(Label), \n", " y = prop.unique, \n", " shape = Strain, \n", " color = Media)) +\n", " myfacet + \n", " mygeom + \n", " mytheme + \n", " mypal\n", "\n", "print(p1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show the fraction of reads mapped to genes (prop.gene)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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oZEUahAVKQmsdYe8nq9JSUlOp0usyEBAAC00q9fvzVr1nz33Xf19fWyLHfp0qV7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PULDUVgllLWKTe96Li8vE3R8Wg0CjC6WscHQ+bvjlZL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27ceOHauwSWpj46efflq2bJmq2Jg2bVpW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\nMvKNa7U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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 6)\n", "\n", "p2 <- ggplot(annomapres, \n", " aes(x = Label, \n", " y = prop.gene, \n", " shape = Strain, \n", " color = Media)) +\n", " myfacet +\n", " mygeom +\n", " mytheme + \n", " mypal\n", " \n", "print(p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show the fraction of reads categorized as \"no feature\" (prop.nofeat)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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eqpp65Zs+bw4cMVFRWGYZxwwgk9evRwuVytHRcso1BJu4Z1gmEY35/cQ6SueGha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189d+7cjh07jEZjQtqT9ImKz+cTBKGuri7snwghHo8n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F6/VWVVWZTKaMjIxEtygpyXTwgWMaULFe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\ne6bK6XSWlpZWVlYSQpo0aZKTkxP4yPzAXqjsqQacBcVDUuw4o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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 6)\n", "\n", "p3 <- ggplot(annomapres, \n", " aes(x = Label, \n", " y = prop.nofeat, \n", " shape = Strain, \n", " color = Media))+\n", " myfacet +\n", " mygeom + \n", " mytheme + \n", " mypal\n", "\n", "print(p3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show the number of all the reads in each sample \n", "(Note: depth = ngenemap + namb + nmulti + nnofeat + nunmap)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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u3aSy65xOl0CiHGjBnj8/lqamr27du3c+fOZcuW9evXb8CAAen6nAAAAMic7du3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CoAAAAAAMAcJCoAAAAAAMAcJCoAAAAAAMAcJCoAAAAA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\n9YSQX375Zd++fUJAWlpa//7927Vr57OcIJaXnYWVYWVNnz6d/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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width = 9, repr.plot.height = 6)\n", "\n", "p4 <- ggplot(annomapres, \n", " aes(x = Label, \n", " y = depth, \n", " shape = Strain, \n", " color = Media))+\n", " myfacet +\n", " mygeom +\n", " mytheme +\n", " mypal\n", "\n", "print(p4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Store the plots" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "png(file.path(IMGDIR, \"p1.png\"), height = 480 * 2, width = 480 * 2)\n", "plot(p1)\n", "graphics.off()\n", "\n", "png(file.path(IMGDIR, \"p2.png\"), height = 480 * 2, width = 480 * 2)\n", "plot(p2)\n", "graphics.off()\n", "\n", "png(file.path(IMGDIR, \"p3.png\"), height = 480 * 2, width = 480 * 2)\n", "plot(p3)\n", "graphics.off()\n", "\n", "png(file.path(IMGDIR, \"p4.png\"), height = 480 * 2, width = 480 * 2)\n", "plot(p4)\n", "graphics.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The End" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "R version 3.4.1 (2017-06-30)\n", "Platform: x86_64-pc-linux-gnu (64-bit)\n", "Running under: Debian GNU/Linux 8 (jessie)\n", "\n", "Matrix products: default\n", "BLAS: /opt/conda/lib/R/lib/libRblas.so\n", "LAPACK: /opt/conda/lib/R/lib/libRlapack.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] tools parallel stats4 stats graphics grDevices utils \n", " [8] datasets methods base \n", "\n", "other attached packages:\n", " [1] bindrcpp_0.2 plotly_4.8.0 \n", " [3] dendextend_1.8.0 gridExtra_2.3 \n", " [5] RColorBrewer_1.1-2 qvalue_2.10.0 \n", " [7] limma_3.34.9 DESeq2_1.18.1 \n", " [9] SummarizedExperiment_1.8.0 DelayedArray_0.4.1 \n", "[11] matrixStats_0.53.1 Biobase_2.38.0 \n", "[13] GenomicRanges_1.30.3 GenomeInfoDb_1.14.0 \n", "[15] IRanges_2.12.0 S4Vectors_0.16.0 \n", "[17] BiocGenerics_0.24.0 haven_1.1.1 \n", "[19] stringr_1.3.0 foreach_1.4.4 \n", "[21] dplyr_0.7.4 purrr_0.2.4 \n", "[23] readr_1.1.1 tidyr_0.8.1 \n", "[25] tibble_1.4.2 ggplot2_3.0.0 \n", "[27] tidyverse_1.1.1 \n", "\n", "loaded via a namespace (and not attached):\n", " [1] colorspace_1.3-2 class_7.3-14 modeltools_0.2-22 \n", " [4] mclust_5.4.1 IRdisplay_0.4.4 htmlTable_1.9 \n", " [7] XVector_0.18.0 base64enc_0.1-3 flexmix_2.3-14 \n", " [10] bit64_0.9-5 mvtnorm_1.0-8 AnnotationDbi_1.40.0 \n", " [13] lubridate_1.7.4 xml2_1.2.0 codetools_0.2-15 \n", " [16] splines_3.4.1 mnormt_1.5-5 robustbase_0.92-7 \n", " [19] geneplotter_1.56.0 knitr_1.20 IRkernel_0.8.11 \n", " [22] Formula_1.2-1 jsonlite_1.5 broom_0.4.4 \n", " [25] annotate_1.56.0 kernlab_0.9-25 cluster_2.0.6 \n", " [28] compiler_3.4.1 httr_1.3.1 backports_1.1.2 \n", " [31] assertthat_0.2.0 Matrix_1.2-14 lazyeval_0.2.1 \n", " [34] acepack_1.4.1 htmltools_0.3.6 gtable_0.2.0 \n", " [37] glue_1.2.0 GenomeInfoDbData_0.99.0 reshape2_1.4.3 \n", " [40] Rcpp_0.12.15 trimcluster_0.1-2.1 cellranger_1.1.0 \n", " [43] nlme_3.1-131 fpc_2.1-11.1 iterators_1.0.9 \n", " [46] psych_1.8.4 rvest_0.3.2 XML_3.98-1.11 \n", " [49] DEoptimR_1.0-8 MASS_7.3-48 zlibbioc_1.24.0 \n", " [52] scales_0.5.0 hms_0.3 memoise_1.1.0 \n", " [55] rpart_4.1-13 latticeExtra_0.6-28 stringi_1.1.7 \n", " [58] RSQLite_2.0 genefilter_1.60.0 checkmate_1.8.5 \n", " [61] BiocParallel_1.12.0 repr_0.15.0 prabclus_2.2-6 \n", " [64] rlang_0.2.1 pkgconfig_2.0.1 bitops_1.0-6 \n", " [67] evaluate_0.10.1 lattice_0.20-34 bindr_0.1.1 \n", " [70] labeling_0.3 htmlwidgets_1.2 tidyselect_0.2.4 \n", " [73] bit_1.1-12 plyr_1.8.4 magrittr_1.5 \n", " [76] R6_2.2.2 Hmisc_4.0-3 pbdZMQ_0.3-2 \n", " [79] DBI_1.0.0 pillar_1.2.2 whisker_0.3-2 \n", " [82] foreign_0.8-67 withr_2.1.1 survival_2.40-1 \n", " [85] RCurl_1.95-4.8 nnet_7.3-12 modelr_0.1.2 \n", " [88] crayon_1.3.4 uuid_0.1-2 viridis_0.5.1 \n", " [91] locfit_1.5-9.1 grid_3.4.1 readxl_1.1.0 \n", " [94] data.table_1.10.4 blob_1.1.1 forcats_0.3.0 \n", " [97] diptest_0.75-7 digest_0.6.15 xtable_1.8-2 \n", "[100] munsell_0.5.0 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": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 2 }