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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$set(
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13 echo = as.logical(opt$X_e),
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14 error = TRUE
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15 )
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16 ```
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17
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18
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19 ```{r eval=TRUE}
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20 # Import workspace
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21 # fcp = file.copy(opt$X_W, "deseq.RData")
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22 load(opt$X_W)
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23 ```
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24
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25 # Results {.tabset}
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26
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27 ## Result table
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28
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29 ```{r}
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30 cat('--- View the top 100 rows of the result table ---')
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31 res <- results(dds, contrast = c(opt$X_C, opt$X_T, opt$X_K))
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32 write.csv(as.data.frame(res), file = opt$X_R)
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33 res_df = as.data.frame(res)[1:100, ]
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34 datatable(res_df, style="bootstrap", filter = 'top',
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35 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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36 ```
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37
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38 ## Result summary
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39
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40 ```{r}
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41 summary(res)
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42 ```
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43
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44
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45 # MA-plot {.tabset}
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46
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47
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48
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49 ```{r}
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50 cat('--- Shrinked with Bayesian procedure ---')
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51 plotMA(res)
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52 ```
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53
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54
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55 # Histogram of p values
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56
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57 ```{r}
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58 hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,
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59 col = "grey50", border = "white", main = "",
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60 xlab = "Mean normalized count larger than 1")
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61 ```
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62
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63
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64 # Visualization {.tabset}
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65 ## Gene clustering
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66
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67 ```{r}
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68 clustering_groups = strsplit(opt$X_M, ',')[[1]]
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69
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70 topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)
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71 mat <- assay(rld)[ topVarGenes, ]
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72 mat <- mat - rowMeans(mat)
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73 annotation_col <- as.data.frame(colData(rld)[, clustering_groups])
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74 colnames(annotation_col) = clustering_groups
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75 rownames(annotation_col) = colnames(mat)
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76 pheatmap(mat, annotation_col = annotation_col)
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77 ```
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78
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79 ## Sample-to-sample distance
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80
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81 ```{r}
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82 sampleDistMatrix <- as.matrix( sampleDists )
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83 colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
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84 pheatmap(sampleDistMatrix,
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85 clustering_distance_cols = sampleDists,
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86 col = colors)
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87 ```
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88
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89 ## PCA plot
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90
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91 ```{r}
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92 plotPCA(rld, intgroup = clustering_groups)
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93 ```
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94
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95 ## MDS plot {.tabset}
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96
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97 ### Data table
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98 ```{r}
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99 mds <- as.data.frame(colData(rld)) %>%
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100 cbind(cmdscale(sampleDistMatrix))
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101 knitr::kable(mds)
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102 ```
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103
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104 ### Plot
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105 ```{r}
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106 ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
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107 geom_point(size = 3) + coord_fixed()
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108 ```
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109
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