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1 ---
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2 output: html_document
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3 ---
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4
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5 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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6 knitr::opts_chunk$set(
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7 echo = as.logical(opt$X_e),
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8 error = TRUE
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9 )
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10 ```
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11
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12
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13 # Visualization {.tabset}
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14 ## Gene clustering
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15
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16 ```{r}
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17 clustering_groups = strsplit(opt$X_M, ',')[[1]]
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18
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19 topVarGenes <- head(order(rowVars(assay(rld)), decreasing = TRUE), 20)
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20 mat <- assay(rld)[ topVarGenes, ]
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21 mat <- mat - rowMeans(mat)
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22 annotation_col <- as.data.frame(colData(rld)[, clustering_groups])
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23 colnames(annotation_col) = clustering_groups
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24 rownames(annotation_col) = colnames(mat)
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25 pheatmap(mat, annotation_col = annotation_col)
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26 ```
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27
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28 ## Sample-to-sample distance
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29
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30 ```{r}
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31 sampleDistMatrix <- as.matrix( sampleDists )
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32 colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
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33 pheatmap(sampleDistMatrix,
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34 clustering_distance_cols = sampleDists,
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35 col = colors)
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36 ```
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37
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38 ## PCA plot
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39
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40 ```{r}
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41 plotPCA(rld, intgroup = clustering_groups)
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42 ```
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43
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44 ## MDS plot {.tabset}
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45
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46 ### Data table
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47 ```{r}
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48 mds <- as.data.frame(colData(rld)) %>%
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49 cbind(cmdscale(sampleDistMatrix))
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50 knitr::kable(mds)
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51 ```
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52
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53 ### Plot
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54 ```{r}
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55 ggplot(mds, aes(x = `1`, y = `2`, col = time)) +
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56 geom_point(size = 3) + coord_fixed()
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57 ```
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