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comparison DESeq_visualization.Rmd @ 5:507e408cd6cc draft

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author mingchen0919
date Mon, 07 Aug 2017 18:11:27 -0400
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1 ---
2 title: 'DESeq2: Visualization'
3 output:
4 html_document:
5 number_sections: true
6 toc: true
7 theme: cosmo
8 highlight: tango
9 ---
10
11 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
12 knitr::opts_chunk$set(
13 echo = ECHO
14 )
15
16 library(stringi)
17 library(DESeq2)
18 library(pheatmap)
19 library(PoiClaClu)
20 library(RColorBrewer)
21 ```
22
23 # Import workspace
24
25 ```{r eval=TRUE}
26 fcp = file.copy("DESEQ_WORKSPACE", "deseq.RData")
27 load("deseq.RData")
28 ```
29
30 # Visualization
31
32 ## Heatmaps of sample-to-sample distances {.tabset}
33
34 ### rlog-transformed values
35
36 ```{r}
37 sampleDistMatrix <- as.matrix( sampleDists )
38 colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
39 pheatmap(sampleDistMatrix,
40 # clustering_distance_rows = sampleDists,
41 clustering_distance_cols = sampleDists,
42 col = colors)
43 ```
44
45 ### Poisson Distance
46
47 ```{r}
48 count_t = t(counts(dds))
49 rownames(count_t) = colnames(counts(dds))
50 poisd <- PoissonDistance(count_t)
51 samplePoisDistMatrix <- as.matrix( poisd$dd )
52 rownames(samplePoisDistMatrix) = rownames(count_t)
53 colnames(samplePoisDistMatrix) = rownames(count_t)
54 pheatmap(samplePoisDistMatrix,
55 # clustering_distance_rows = poisd$dd,
56 clustering_distance_cols = poisd$dd,
57 col = colors)
58 ```
59
60
61 ## PCA plots {.tabset}
62
63 ### Using `plotPCA()` function
64
65 ```{r}
66 # interest groups
67 col_index = as.numeric(strsplit("INTGROUPS_PCA", ',')[[1]])
68 intgroup_pca = colnames(sample_table)[col_index]
69 ```
70
71 ```{r}
72 plotPCA(rld, intgroup = intgroup_pca)
73 ```
74
75
76 ### Using *ggplot2*
77
78 ```{r}
79 pcaData <- plotPCA(rld, intgroup = intgroup_pca, returnData = TRUE)
80 percentVar <- round(100 * attr(pcaData, "percentVar"))
81 ggplot(pcaData, aes(x = PC1, y = PC2, color = time)) +
82 geom_point(size =3) +
83 xlab(paste0("PC1: ", percentVar[1], "% variance")) +
84 ylab(paste0("PC2: ", percentVar[2], "% variance")) +
85 coord_fixed()
86 ```
87
88 ### PCA data table
89
90 ```{r}
91 knitr::kable(pcaData)
92 ```
93
94
95 ## MDS plots {.tabset}
96
97 ### Using rlog-transformed values
98
99 ```{r}
100 mds <- as.data.frame(colData(rld)) %>%
101 cbind(cmdscale(sampleDistMatrix))
102 mds
103 ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
104 geom_point(size = 3) + coord_fixed()
105 ```
106
107 ### Using the *Poisson Distance*
108
109 ```{r}
110 mdsPois <- as.data.frame(colData(dds)) %>%
111 cbind(cmdscale(samplePoisDistMatrix))
112 ggplot(mdsPois, aes(x = `1`, y = `2`, col = time)) +
113 geom_point(size = 3) + coord_fixed()
114 ```