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comparison wgcna_preprocessing.Rmd @ 0:4275479ada3a draft

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planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
author mingchen0919
date Tue, 08 Aug 2017 12:35:50 -0400
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1 ---
2 title: 'WGCNA: data preprocessing'
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$set(
13 echo = ECHO
14 )
15 ```
16
17 ```{r}
18 str(opt)
19 ```
20
21 # Import data
22
23 Each row represents a gene and each column represents a sample.
24
25 ```{r}
26 expression_data = read.csv('EXPRESSION_DATA', header = TRUE, row.names = 1)
27 ```
28
29 Display the first 100 genes.
30
31 ```{r}
32 datatable(head(expression_data, 100), style="bootstrap", filter = 'top',
33 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
34 ```
35
36 Transpose expression data matrix so that each row represents a sample and each column represents a gene.
37
38 ```{r}
39 expression_data = as.data.frame(t(expression_data))
40 ```
41
42 # Checking data
43
44 Checking data for excessive missing values and identification of outlier microarray samples.
45
46 ```{r}
47 gsg = goodSamplesGenes(expression_data, verbose = 3)
48 if (!gsg$allOK) {
49 # Optionally, print the gene and sample names that were removed:
50 if (sum(!gsg$goodGenes)>0)
51 printFlush(paste("Removing genes:", paste(names(expression_data)[!gsg$goodGenes], collapse = ", ")));
52 if (sum(!gsg$goodSamples)>0)
53 printFlush(paste("Removing samples:", paste(rownames(expression_data)[!gsg$goodSamples], collapse = ", ")));
54 # Remove the offending genes and samples from the data:
55 expression_data = expression_data[gsg$goodSamples, gsg$goodGenes]
56 } else {
57 print('all genes are OK!')
58 }
59 ```
60
61 # Clustering samples
62
63 If there are any outliers, choose a height cut that will remove the offending sample. Remember this number since you will need this number in further analysis.
64
65 ```{r fig.align='center'}
66 sampleTree = hclust(dist(expression_data), method = "average");
67 plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="",
68 cex.axis = 1, cex.main = 1, cex = 0.5)
69 ```
70
71
72 ```{r echo=FALSE}
73 rm("opt")
74 save(list=ls(all.names = TRUE), file='PREPROCESSING_WORKSPACE')
75 ```
76