Mercurial > repos > mingchen0919 > rmarkdown_deseq2_test

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planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_deseq2
author mingchen0919
date Tue, 07 Nov 2017 10:02:57 -0500
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1 ---
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2 title: 'DESeq2: Perform DESeq analysis'
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3 output:
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4 html_document:
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5 number_sections: true
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6 toc: true
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7 theme: cosmo
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8 highlight: tango
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9 ---
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11 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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12 knitr::opts_chunk$set(
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13 echo = ECHO,
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14 error = TRUE
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15 )
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16 ```
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18 ```{r}
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19 str(opt)
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20 ```
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22 # `DESeqDataSet` object
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24 ```{r eval=FALSE}
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25 count_files = strsplit(opt$count_files, ',')[[1]]
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26 sample_table = read.table(opt$sample_table, header = TRUE)
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28 ## copy count files into working directory
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29 file_copy = file.copy(count_files, sample_table$fileName, overwrite = TRUE)
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31 ## DESeqDataSet object
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32 dds = DESeqDataSetFromHTSeqCount(sampleTable = sample_table,
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33 directory = './',
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34 design = DESIGN_FORMULA)
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35 dds
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36 ```
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37
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38 # Pre-filtering the dataset.
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40 We can remove the rows that have 0 or 1 count to reduce object size and increase the calculation speed.
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42 * Number of rows before pre-filtering
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43 ```{r eval=FALSE}
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44 nrow(dds)
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45 ```
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47 * Number of rows after pre-filtering
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48 ```{r eval=FALSE}
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49 dds = dds[rowSums(counts(dds)) > 1, ]
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50 nrow(dds)
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51 ```
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52
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53 # Peek at data {.tabset}
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55 ## Count Data
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57 ```{r eval=FALSE}
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58 datatable(head(counts(dds), 100), style="bootstrap",
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59 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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60 ```
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61
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62 ## Sample Table
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63
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64 ```{r eval=FALSE}
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65 datatable(sample_table, style="bootstrap",
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66 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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67 ```
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68
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69 # Sample distance on variance stabilized data {.tabset}
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71 ## `rlog` Stabilizing transformation
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73 ```{r eval=FALSE}
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74 rld = rlog(dds, blind = FALSE)
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75 datatable(head(assay(rld), 100), style="bootstrap",
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76 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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77 ```
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78
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79 ## Sample distance
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81 ```{r eval=FALSE}
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82 sampleDists <- dist(t(assay(rld)))
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83 sampleDists
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84 ```
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85
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86 # Differential expression analysis
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87
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88 ```{r eval=FALSE}
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89 dds <- DESeq(dds)
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90 ```
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91
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92 ```{r eval=FALSE}
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93 rm("opt")
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94 save(list=ls(all.names = TRUE), file='DESEQ_WORKSPACE')
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95 ```
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96
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