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author | mingchen0919 |
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date | Mon, 07 Aug 2017 18:26:39 -0400 |
parents | a5fdd120b2c7 |
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--- title: 'DESeq2: Perform DESeq analysis' output: html_document: number_sections: true toc: true theme: cosmo highlight: tango --- ```{r setup, include=FALSE, warning=FALSE, message=FALSE} knitr::opts_chunk$set( echo = ECHO ) library(stringi) library(DESeq2) library(pheatmap) library(PoiClaClu) library(RColorBrewer) ``` # `DESeqDataSet` object ```{r} count_files = strsplit(opt$count_files, ',')[[1]] sample_table = read.table(opt$sample_table, header = TRUE) ## copy count files into working directory file_copy = file.copy(count_files, sample_table$fileName, overwrite = TRUE) ## DESeqDataSet object dds = DESeqDataSetFromHTSeqCount(sampleTable = sample_table, directory = './', design = DESIGN_FORMULA) dds ``` # Pre-filtering the dataset. We can remove the rows that have 0 or 1 count to reduce object size and increase the calculation speed. * Number of rows before pre-filtering ```{r} nrow(dds) ``` * Number of rows after pre-filtering ```{r} dds = dds[rowSums(counts(dds)) > 1, ] nrow(dds) ``` # Peek at data {.tabset} ## Count Data ```{r} datatable(head(counts(dds), 100), style="bootstrap", class="table-condensed", options = list(dom = 'tp', scrollX = TRUE)) ``` ## Sample Table ```{r} datatable(sample_table, style="bootstrap", class="table-condensed", options = list(dom = 'tp', scrollX = TRUE)) ``` # Sample distance on variance stabilized data {.tabset} ## `rlog` Stabilizing transformation ```{r} rld = rlog(dds, blind = FALSE) datatable(head(assay(rld), 100), style="bootstrap", class="table-condensed", options = list(dom = 'tp', scrollX = TRUE)) ``` ## Sample distance ```{r} sampleDists <- dist(t(assay(rld))) sampleDists ``` # Differential expression analysis ```{r} dds <- DESeq(dds) ``` ```{r} rm("opt") save(list=ls(all.names = TRUE), file='DESEQ_WORKSPACE') ```