Mercurial > repos > mingchen0919 > aurora_deseq2_site

comparison DESeq_results.Rmd @ 0:6f94b4b9de44 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload
author mingchen0919
date Tue, 27 Feb 2018 23:57:53 -0500
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:6f94b4b9de44
1 ---
2 title: 'DESeq2: Results'
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$set(
13 echo = as.logical(opt$X_e),
14 error = TRUE
15 )
16 ```
17
18
19 ```{r eval=TRUE}
20 # Import workspace
21 # fcp = file.copy(opt$X_W, "deseq.RData")
22 load(opt$X_W)
23 ```
24
25 # Results {.tabset}
26
27 ## Result table
28
29 ```{r}
30 cat('--- View the top 100 rows of the result table ---')
31 res <- results(dds, contrast = c(opt$X_C, opt$X_T, opt$X_K))
32 write.csv(as.data.frame(res), file = opt$X_R)
33 res_df = as.data.frame(res)[1:100, ]
34 datatable(res_df, style="bootstrap", filter = 'top',
35 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
36 ```
37
38 ## Result summary
39
40 ```{r}
41 summary(res)
42 ```
43
44
45 # MA-plot {.tabset}
46
47
48
49 ```{r}
50 cat('--- Shrinked with Bayesian procedure ---')
51 plotMA(res)
52 ```
53
54
55 # Histogram of p values
56
57 ```{r}
58 hist(res$pvalue[res$baseMean > 1], breaks = 0:20/20,
59 col = "grey50", border = "white", main = "",
60 xlab = "Mean normalized count larger than 1")
61 ```
62
63
64 # Visualization {.tabset}
65 ## Gene clustering
66
67 ```{r}
68 clustering_groups = strsplit(opt$X_M, ',')[[1]]
69
70 topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)
71 mat <- assay(rld)[ topVarGenes, ]
72 mat <- mat - rowMeans(mat)
73 annotation_col <- as.data.frame(colData(rld)[, clustering_groups])
74 colnames(annotation_col) = clustering_groups
75 rownames(annotation_col) = colnames(mat)
76 pheatmap(mat, annotation_col = annotation_col)
77 ```
78
79 ## Sample-to-sample distance
80
81 ```{r}
82 sampleDistMatrix <- as.matrix( sampleDists )
83 colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
84 pheatmap(sampleDistMatrix,
85 clustering_distance_cols = sampleDists,
86 col = colors)
87 ```
88
89 ## PCA plot
90
91 ```{r}
92 plotPCA(rld, intgroup = clustering_groups)
93 ```
94
95 ## MDS plot {.tabset}
96
97 ### Data table
98 ```{r}
99 mds <- as.data.frame(colData(rld)) %>%
100 cbind(cmdscale(sampleDistMatrix))
101 knitr::kable(mds)
102 ```
103
104 ### Plot
105 ```{r}
106 ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
107 geom_point(size = 3) + coord_fixed()
108 ```
109