Mercurial > repos > mingchen0919 > rmarkdown_wgcna

annotate wgcna_preprocessing.Rmd @ 2:237210176a2b draft

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planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit a23e23222252167ef7c3338a4872e84706df8f83-dirty
author mingchen0919
date Tue, 08 Aug 2017 14:33:59 -0400
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4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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1 ---
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2 title: 'WGCNA: data preprocessing'
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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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15 ```
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17 ```{r}
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18 str(opt)
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19 ```
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21 # Import data
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23 Each row represents a gene and each column represents a sample.
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25 ```{r}
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26 expression_data = read.csv('EXPRESSION_DATA', header = TRUE, row.names = 1)
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27 ```
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29 Display the first 100 genes.
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31 ```{r}
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32 datatable(head(expression_data, 100), style="bootstrap", filter = 'top',
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33 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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34 ```
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36 Transpose expression data matrix so that each row represents a sample and each column represents a gene.
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38 ```{r}
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39 expression_data = as.data.frame(t(expression_data))
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40 ```
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42 # Checking data
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44 Checking data for excessive missing values and identification of outlier microarray samples.
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46 ```{r}
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47 gsg = goodSamplesGenes(expression_data, verbose = 3)
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48 if (!gsg$allOK) {
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49 # Optionally, print the gene and sample names that were removed:
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50 if (sum(!gsg$goodGenes)>0)
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51 printFlush(paste("Removing genes:", paste(names(expression_data)[!gsg$goodGenes], collapse = ", ")));
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52 if (sum(!gsg$goodSamples)>0)
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53 printFlush(paste("Removing samples:", paste(rownames(expression_data)[!gsg$goodSamples], collapse = ", ")));
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54 # Remove the offending genes and samples from the data:
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55 expression_data = expression_data[gsg$goodSamples, gsg$goodGenes]
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56 } else {
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57 print('all genes are OK!')
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58 }
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59 ```
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61 # Clustering samples
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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.
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65 ```{r fig.align='center'}
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66 sampleTree = hclust(dist(expression_data), method = "average");
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67 plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="",
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68 cex.axis = 1, cex.main = 1, cex = 0.5)
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69 ```
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72 ```{r echo=FALSE}
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73 rm("opt")
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74 save(list=ls(all.names = TRUE), file='PREPROCESSING_WORKSPACE')
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75 ```
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