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author mingchen0919
date Sun, 30 Dec 2018 13:57:07 -0500
parents 32210899a3dd
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1 ---
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2 title: 'DESeq2 analysis'
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3 output:
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4 html_document:
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5 highlight: pygments
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6 ---
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7
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8 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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9 knitr::opts_chunk$set(error = TRUE, echo = FALSE)
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10 ```
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13 ```{r echo=FALSE}
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14 # import count data
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15 count_data = read.csv(opt$X_A, row.names = 1, header = TRUE)
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16 # import column data
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17 coldata = read.csv(opt$X_B, row.names = 1, header = TRUE)[colnames(count_data),,drop=FALSE]
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18 ```
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19
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20 ```{r echo=FALSE}
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21 dds = DESeqDataSetFromMatrix(countData = count_data,
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22 colData = coldata,
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23 design = formula(opt$X_C))
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24 dds = DESeq(dds, test = opt$X_G, fitType = opt$X_H)
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25 ## Differential expression test results
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26 res = results(dds, contrast = c(opt$X_D, opt$X_E, opt$X_F), alpha = opt$X_I)
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27 res
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28 ```
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31 ```{r echo=FALSE}
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32 # save all padj sorted res to tool output directory
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33 padj_sorted_res = res[order(res$padj), ]
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34 write.table(padj_sorted_res,
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35 file = paste0(opt$X_d, '/padj-sorted-genes.txt'),
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36 quote = FALSE)
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37
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38 # save significant genes to a file in tool output directory
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39 sig_res = res[(res$padj < opt$X_I) & !is.na(res$padj), ]
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40 sig_res_sorted = sig_res[order(sig_res$padj), ]
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41 write.table(sig_res_sorted,
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42 file = paste0(opt$X_d, '/padj-sorted-significant-genes.txt'),
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43 quote = FALSE)
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44 ```