diff --git a/session3_solutions.Rmd b/session3_solutions.Rmd index e37b46b..71dddc5 100644 --- a/session3_solutions.Rmd +++ b/session3_solutions.Rmd @@ -54,6 +54,8 @@ results_tgf %>% The tricky part of making a heatmap is deciding which genes to include. The gene names have to be in ENSEMBL format, as the count matrix we will be plotting has ENSEMBL names in the rows. +In the following example, we chose the genes with most extreme log2 fold-change + ```{r} # The pheatmap library is needed to make the heatmap @@ -64,42 +66,32 @@ library(pheatmap) vsd <- vst(dds) sampleInfo <- as.data.frame(colData(dds)[,c("condition","Treated")]) -# create a new table that includes gene variability - -res_with_var <- mutate(results_tgf, GeneVar = rowSds(assay(vsd))) %>% - filter(!is.na(padj)) - -# arrange by the new column and extract ENSEMBL ID of the first 100 rows +## Order results by log2FoldChange and get the most extreme -genes_to_plot <- arrange(res_with_var, desc(GeneVar)) %>% - dplyr::slice(1:100) %>% +top_genes_logFC <- results_tgf %>% + arrange(desc(abs(log2FoldChange))) %>% + slice(1:30) %>% pull(ENSEMBL) +top_genes_logFC_labels <- results_tgf %>% + arrange(desc(abs(log2FoldChange))) %>% + slice(1:30) %>% + pull(SYMBOL) -``` +## get the corresponding geen symbols -```{r fig.width=12} +pheatmap(assay(vsd)[top_genes_logFC,], + scale = "row", + labels_row = top_genes_logFC_labels) -pheatmap(assay(vsd)[genes_to_plot,], - annotation_col = sampleInfo, - scale="row") ``` -```{r fig.width=12} -gene_labels <- arrange(res_with_var, desc(GeneVar)) %>% - dplyr::slice(1:100) %>% - pull(SYMBOL) -pheatmap(assay(vsd)[genes_to_plot,], - annotation_col = sampleInfo, - labels_row = gene_labels, - scale="row") -``` + # Exercise: Pathways -**Need to fix, as currently this doesn't return anything!** In order to use the `enrichKEGG` function, we will have to define a set of enriched genes in terms of their `ENTREZID` (instead of `ENSEMBL`) diff --git a/session3_solutions.nb.html b/session3_solutions.nb.html index c527630..dee47c7 100644 --- a/session3_solutions.nb.html +++ b/session3_solutions.nb.html @@ -1511,7 +1511,7 @@ $(this).detach().appendTo(div); // add a show code button right above - var showCodeText = $('' + (showThis ? 'Hide' : 'Code') + ''); + var showCodeText = $('' + (showThis ? 'Hide' : 'Show') + ''); var showCodeButton = $(''); showCodeButton.append(showCodeText); showCodeButton @@ -1537,7 +1537,7 @@ // * Change text // * add a class for intermediate states styling div.on('hide.bs.collapse', function () { - showCodeText.text('Code'); + showCodeText.text('Show'); showCodeButton.addClass('btn-collapsing'); }); div.on('hidden.bs.collapse', function () { @@ -1755,10 +1755,12 @@

R Notebook

We start by loading our results from the previous week

+ 
library(DESeq2)
- + + 
Loading required package: S4Vectors
 Loading required package: stats4
 Loading required package: BiocGenerics
@@ -1771,29 +1773,28 @@ 
R Notebook

 
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-    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated,
-    eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
-    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
-    rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min
+    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
+    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
+    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
+    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
+    Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
+    tapply, union, unique, unsplit, which.max, which.min
 
 
 Attaching package: ‘S4Vectors’
 
+The following object is masked from ‘package:utils’:
+
+    findMatches
+
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     expand.grid, I, unname
 
 Loading required package: IRanges
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-    windows
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 Loading required package: GenomicRanges
 Loading required package: GenomeInfoDb
-Warning: package ‘GenomeInfoDb’ was built under R version 4.2.1Loading required package: SummarizedExperiment
+Loading required package: SummarizedExperiment
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 Loading required package: matrixStats
 
@@ -1801,22 +1802,28 @@ 
R Notebook

 
 The following objects are masked from ‘package:matrixStats’:
 
-    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins,
-    colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads,
-    colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges,
-    colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
-    colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs,
-    rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
-    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs,
-    rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
-    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads,
-    rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars
+    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
+    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
+    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
+    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
+    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
+    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
+    colWeightedMeans, colWeightedMedians, colWeightedSds,
+    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
+    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
+    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
+    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
+    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
+    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
+    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
+    rowWeightedSds, rowWeightedVars
 
 Loading required package: Biobase
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-    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
-    see 'citation("Biobase")', and for packages 'citation("pkgname")'.
+    Vignettes contain introductory material; view with
+    'browseVignettes()'. To cite Bioconductor, see
+    'citation("Biobase")', and for packages 'citation("pkgname")'.
 
 
 Attaching package: ‘Biobase’
@@ -1829,9 +1836,11 @@ 
R Notebook

 
     anyMissing, rowMedians
+ 
library(dplyr)
+ 

 Attaching package: ‘dplyr’
@@ -1872,12 +1881,14 @@ 
R Notebook

 
     intersect, setdiff, setequal, union
+ 
## Read the annotated results
 results_tgf <- readRDS("Robjects/results_TGF_vs_CTR_annotated_BACKUP.rds")
 ## Read the counts that we produced previously
 dds <- readRDS("Robjects/dds_BACKUP.rds")
+
@@ -1886,27 +1897,28 @@

Exercise: Volcano plot

ggplot2 package

- + + 

-library(ggplot2)
- - -
Warning: package ‘ggplot2’ was built under R version 4.2.2
- - -
results_tgf %>% 
+library(ggplot2)
+results_tgf %>% 
   ggplot(aes(x = log2FoldChange, y = -log10(padj))) + geom_point()
- -

+ + + +

+ + 
library(org.Hs.eg.db)
+ 
Loading required package: AnnotationDbi
 
@@ -1916,44 +1928,56 @@ 
Exercise: Volcano plot

 
     select
+ 
ecm_anno <- AnnotationDbi::select(org.Hs.eg.db,
                                  keys = "GO:0030198",
                                  keytype = "GO",
                                  columns=c("ENSEMBL","SYMBOL"))
+ 
'select()' returned 1:many mapping between keys and columns
+ 
ecm_anno
- + + +
+ + 
ecm_ids <- pull(ecm_anno, "ENSEMBL")
+ + 
results_tgf %>% 
   mutate(ECM_Gene = ENSEMBL %in% ecm_ids) %>% 
   ggplot(aes(x = log2FoldChange, y = -log10(padj),col=ECM_Gene)) + geom_point()
- -

+ + + +

+

The default colouring does not distinguish the ECM genes as much as @@ -1962,14 +1986,18 @@

Exercise: Volcano plot

(alpha) can also be adjusted. Below is a suggestion.

+ 
results_tgf %>% 
   mutate(ECM_Gene = ENSEMBL %in% ecm_ids) %>% 
   ggplot(aes(x = log2FoldChange, y = -log10(padj),col=ECM_Gene,alpha=ECM_Gene)) + geom_point() + scale_color_manual(values=c("black","red")) + scale_alpha_manual(values=c(0.2,1))
- -

+ + + +

+
@@ -1978,8 +2006,11 @@

Exercise: Heatmap

The tricky part of making a heatmap is deciding which genes to include. The gene names have to be in ENSEMBL format, as the count matrix we will be plotting has ENSEMBL names in the rows.

+

In the following example, we chose the genes with most extreme log2 +fold-change

+ 
# The pheatmap library is needed to make the heatmap
 
@@ -1989,120 +2020,209 @@ 
Exercise: Heatmap

 
 vsd <- vst(dds)
+ 
using 'avgTxLength' from assays(dds), correcting for library size
- + + 
sampleInfo <- as.data.frame(colData(dds)[,c("condition","Treated")])
 
-# create a new table that includes gene variability
-
-res_with_var <- mutate(results_tgf, GeneVar = rowSds(assay(vsd))) %>% 
-  filter(!is.na(padj))
+## Order results by log2FoldChange and get the most extreme
 
-# arrange by the new column and extract ENSEMBL ID of the first 100 rows
-
-genes_to_plot <- arrange(res_with_var, desc(GeneVar)) %>% 
-  dplyr::slice(1:100) %>% 
+top_genes_logFC <- results_tgf %>% 
+  arrange(desc(abs(log2FoldChange))) %>% 
+  slice(1:30) %>% 
   pull(ENSEMBL)
-
- - - - - - -

-pheatmap(assay(vsd)[genes_to_plot,],
-         annotation_col = sampleInfo,
-         scale="row")
- - -

- - - - - - -
gene_labels <- arrange(res_with_var, desc(GeneVar)) %>% 
-  dplyr::slice(1:100) %>% 
+
+top_genes_logFC_labels <- results_tgf %>% 
+  arrange(desc(abs(log2FoldChange))) %>% 
+  slice(1:30) %>% 
   pull(SYMBOL)
 
-pheatmap(assay(vsd)[genes_to_plot,],
-         annotation_col = sampleInfo,
-         labels_row = gene_labels,
-         scale="row")
+## get the corresponding geen symbols
+
+pheatmap(assay(vsd)[top_genes_logFC,],
+         scale = "row",
+         labels_row = top_genes_logFC_labels)
- -

+ + + +

+

Exercise: Pathways

-

Need to fix, as currently this doesn’t return -anything!

In order to use the enrichKEGG function, we will have to define a set of enriched genes in terms of their ENTREZID (instead of ENSEMBL)

+ 
sigGenesEntrez <- results_tgf %>% 
   filter(padj < 0.05, !is.na(ENTREZID)) %>% pull(ENTREZID)
+

Make sure you check the help for enrichKEGG, as it uses different argument names

- + + +
library(clusterProfiler)
+ + + +

+Registered S3 method overwritten by 'data.table':
+  method           from
+  print.data.table     
+clusterProfiler v4.12.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
+
+Please cite:
+
+S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang,
+W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize
+multiomics data. Nature Protocols. 2024, doi:10.1038/s41596-024-01020-z
+
+Attaching package: ‘clusterProfiler’
+
+The following object is masked from ‘package:AnnotationDbi’:
+
+    select
+
+The following object is masked from ‘package:IRanges’:
+
+    slice
+
+The following object is masked from ‘package:S4Vectors’:
+
+    rename
+
+The following object is masked from ‘package:stats’:
+
+    filter
+ + + +
enrich_kegg <- enrichKEGG(gene = sigGenesEntrez,
+                       organism = "hsa",)
+ + + +
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
+Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
+ + + +
as.data.frame(enrich_kegg)
+ + + +
+

The same plots can be created from the results. Here is the dot plot.

+ 
dotplot(enrich_kegg)
- -
Error in (function (classes, fdef, mtable)  : 
-  unable to find an inherited method for function ‘dotplot’ for signature ‘"NULL"’
+ + + +

+

and the upset plot.

+ + +
enrichplot::upsetplot(enrich_kegg)
+ + + + +

+ +

For the GSEA analysis using KEGG we can use the same set of ranked statistics, but make sure to name them according to ENTREZID

+ + +
ranked_genes <- results_tgf %>% 
+  arrange(desc(stat)) %>% 
+  filter(!is.na(stat))
+  
+geneList <- pull(ranked_genes, stat)
+names(geneList) <- pull(ranked_genes, ENTREZID)
+ +

Again, make sure to check the arguments for gseKEGG.

+ + +
gse_kegg <- gseKEGG(geneList = geneList,
+                    organism = "hsa")
+ + + +
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
+
+preparing geneSet collections...
+GSEA analysis...
+Warning: There are ties in the preranked stats (11.21% of the list).
+The order of those tied genes will be arbitrary, which may produce unexpected results.Warning: There are duplicate gene names, fgsea may produce unexpected results.Warning: For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.leading edge analysis...
+done...
+ + + +
as.data.frame(gse_kegg)
+ + + + +
+ +
+ +
-
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