Mercurial > repos > mingchen0919 > rmarkdown_deseq2

annotate DESeq_visualization.Rmd @ 4:7f0281d26b81 draft

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planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_deseq2 commit a23e23222252167ef7c3338a4872e84706df8f83-dirty
author mingchen0919
date Tue, 08 Aug 2017 14:33:57 -0400
parents a0d37b034e45
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1 ---
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2 title: 'DESeq2: Visualization'
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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 )
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16 library(stringi)
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17 library(DESeq2)
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18 library(pheatmap)
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19 # library(PoiClaClu)
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20 library(RColorBrewer)
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21 ```
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23 # Import workspace
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25 ```{r eval=TRUE}
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26 fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
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27 load("deseq.RData")
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28 ```
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30 # Visualization
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32 ## Heatmaps of sample-to-sample distances {.tabset}
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34 ### rlog-transformed values
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36 ```{r}
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37 sampleDistMatrix <- as.matrix( sampleDists )
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38 colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
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39 pheatmap(sampleDistMatrix,
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40 # clustering_distance_rows = sampleDists,
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41 clustering_distance_cols = sampleDists,
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42 col = colors)
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43 ```
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44
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45 ### Poisson Distance
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47 ```{r eval=FALSE}
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48 count_t = t(counts(dds))
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49 rownames(count_t) = colnames(counts(dds))
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50 poisd <- PoissonDistance(count_t)
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51 samplePoisDistMatrix <- as.matrix( poisd$dd )
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52 rownames(samplePoisDistMatrix) = rownames(count_t)
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53 colnames(samplePoisDistMatrix) = rownames(count_t)
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54 pheatmap(samplePoisDistMatrix,
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55 # clustering_distance_rows = poisd$dd,
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56 clustering_distance_cols = poisd$dd,
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57 col = colors)
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58 ```
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59
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61 ## PCA plots {.tabset}
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63 ### Using `plotPCA()` function
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64
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65 ```{r}
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66 # interest groups
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67 col_index = as.numeric(strsplit("INTGROUPS_PCA", ',')[[1]])
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68 intgroup_pca = colnames(sample_table)[col_index]
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69 ```
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71 ```{r}
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72 plotPCA(rld, intgroup = intgroup_pca)
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73 ```
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76 ### Using *ggplot2*
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78 ```{r}
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79 pcaData <- plotPCA(rld, intgroup = intgroup_pca, returnData = TRUE)
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80 percentVar <- round(100 * attr(pcaData, "percentVar"))
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81 ggplot(pcaData, aes(x = PC1, y = PC2, color = time)) +
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82 geom_point(size =3) +
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83 xlab(paste0("PC1: ", percentVar[1], "% variance")) +
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84 ylab(paste0("PC2: ", percentVar[2], "% variance")) +
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85 coord_fixed()
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86 ```
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87
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88 ### PCA data table
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89
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90 ```{r}
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91 knitr::kable(pcaData)
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92 ```
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93
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94
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95 ## MDS plots {.tabset}
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96
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97 ### Using rlog-transformed values
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98
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99 ```{r}
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100 mds <- as.data.frame(colData(rld)) %>%
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101 cbind(cmdscale(sampleDistMatrix))
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102 mds
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103 ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
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104 geom_point(size = 3) + coord_fixed()
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105 ```
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106
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107 ### Using the *Poisson Distance*
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108
2
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109 ```{r eval=FALSE}
0
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110 mdsPois <- as.data.frame(colData(dds)) %>%
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111 cbind(cmdscale(samplePoisDistMatrix))
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112 ggplot(mdsPois, aes(x = `1`, y = `2`, col = time)) +
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113 geom_point(size = 3) + coord_fixed()
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114 ```