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2
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1 ---
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2 title: 'DESeq2: Results'
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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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10
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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 )
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15
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16 library(DESeq2)
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17 library(pheatmap)
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18 library(genefilter)
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19 ```
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20
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21 # Import workspace
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22
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23 ```{r eval=TRUE}
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24 fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
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25 load("deseq.RData")
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26 ```
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27
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28 # Results {.tabset}
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29
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30 ## Result table
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31
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32 ```{r}
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33 group = colnames(sample_table)[CONTRAST_GROUP]
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34 res <- results(dds, contrast = c(group, 'TREATMENT_LEVEL', 'CONDITION_LEVEL'))
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35 datatable(as.data.frame(res), style="bootstrap", filter = 'top',
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36 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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37 ```
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38
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39 ## Result summary
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40
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41 ```{r}
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42 summary(res)
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43 ```
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44
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45
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46 # MA-plot {.tabset}
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47
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48 ## Shrinked with `lfcShrink()` function
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49
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50 ```{r eval=FALSE}
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51 shrink_res = DESeq2::lfcShrink(dds, contrast = c(group, 'TREATMENT_LEVEL', 'CONDITION_LEVEL'), res=res)
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52 plotMA(shrink_res)
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53 ```
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54
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55 ## Shrinked with Bayesian procedure
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56
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57 ```{r}
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58 plotMA(res)
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59 ```
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60
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61
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62 # Histogram of p values
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63
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64 ```{r}
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65 hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,
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66 col = "grey50", border = "white", main = "",
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67 xlab = "Mean normalized count larger than 1")
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68 ```
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69
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70
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71 # Gene clustering
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72
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73 ```{r}
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74 group_index = as.numeric(strsplit("CLUSTERING_GROUPS", ',')[[1]])
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75 clustering_groups = colnames(sample_table)[group_index]
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76
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77 topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)
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78 mat <- assay(rld)[ topVarGenes, ]
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79 mat <- mat - rowMeans(mat)
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80 annotation_col <- as.data.frame(colData(rld)[, clustering_groups])
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81 colnames(annotation_col) = clustering_groups
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82 rownames(annotation_col) = colnames(mat)
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83 pheatmap(mat, annotation_col = annotation_col)
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84 ```
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85
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