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covid-rna-analysis.R
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covid-rna-analysis.R
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# Load packages
library(tximport)
library(DESeq2)
library(plotly)
library(biomaRt)
library(pheatmap)
library(RColorBrewer)
library(clusterProfiler)
library(org.Hs.eg.db)
library(tidyverse)
## NOT data.frame BUT tibble
sample_table = read_csv("SraRunTable.txt") %>%
select(`Sample Name`, source_name, treatment,
Cell_Line, Cell_type, time_point) %>%
slice(seq(1, 48, by=4))
sample_table = read_csv("SraRunTable.txt")
sample_table = select(sample_table,
`Sample Name`, source_name, treatment,
Cell_Line, Cell_type, time_point)
sample_table = unique(sample_table)
'GSM4432378/quant.sf'
paste0(c('Simon', 'S', 'SJ'), 'Cockell')
sample_files = paste0(
pull(sample_table, `Sample Name`),
'/quant.sf')
names(sample_files) = pull(sample_table, `Sample Name`)
gene_map = read_csv("gene_map.csv",
col_names = c('enstid', 'ensgid'))
count_data = tximport(files = sample_files,
type = "salmon",
tx2gene = gene_map,
ignoreTxVersion = TRUE)
count_data[['counts']]
count_data$counts[1:6,]
sample_table = as.data.frame(sample_table)
colnames(sample_table)[1] = "Sample"
conditions = c('mock_nhbe', 'infected_nhbe',
'mock_a549', 'infected_a549')
conditions = rep(conditions, each=3)
conditions = factor(conditions)
sample_table$conditions = conditions
# y ~ x
deseq_dataset = DESeqDataSetFromTximport(txi=count_data,
colData=sample_table,
design=~conditions)
counts(deseq_dataset)[1:6, 1:3]
count_data$counts[1:6, 1:3]
deseq_dataset = estimateSizeFactors(deseq_dataset)
normalizationFactors(deseq_dataset)
counts(deseq_dataset, normalized=TRUE)[1:6, 1:3]
boxplot(counts(deseq_dataset, normalized=TRUE))
vst = varianceStabilizingTransformation(deseq_dataset)
boxplot(assay(vst))
# observe the cell line effect
plotPCA(vst, intgroup='conditions') +
theme_bw()
# split experiment in two
dds1 = deseq_dataset[,1:6]
dds2 = deseq_dataset[,7:12]
dds1 = estimateSizeFactors(dds1)
dds2 = estimateSizeFactors(dds2)
# PCA for 2 groups
vst1 = varianceStabilizingTransformation(dds1)
plotPCA(vst1, intgroup='conditions')
vst2 = varianceStabilizingTransformation(dds2)
plotPCA(vst2, intgroup='conditions')
d = assay(vst1)
d = t(d)
d = dist(d)
h = hclust(d)
plot(h)
k = kmeans(t(assay(vst1)), centers=2)
k$cluster
# 3 steps to DESeq2 analysis
# 1) estimate size factors (normalisation)
# 2) estimate dispersions
# 3) apply statistics (Wald Test)
# dds1 = estimateDispersions(dds1)
## The above doesn't work, so split experiment
## BEFORE importing data
# sample_files = paste0(
# pull(sample_table, `Sample Name`),
# '/quant.sf')
# names(sample_files) = pull(sample_table, `Sample Name`)
sample_files_nhbe = sample_files[1:6]
count_data_nhbe = tximport(files = sample_files_nhbe,
type = "salmon",
tx2gene = gene_map,
ignoreTxVersion = TRUE)
sample_table_nhbe = sample_table[1:6,]
sample_table_nhbe$conditions = factor(rep(c('mock', 'infected'), each=3),
levels=c('mock', 'infected'))
dds_nhbe = DESeqDataSetFromTximport(txi=count_data_nhbe,
colData=sample_table_nhbe,
design=~conditions)
dds_nhbe = estimateSizeFactors(dds_nhbe)
dds_nhbe = estimateDispersions(dds_nhbe)
plotDispEsts(dds_nhbe)
# step 3
dds_nhbe = nbinomWaldTest(dds_nhbe)
## DESeq2 shortcut
# dds_nhbe = DESeq(dds_nhbe)
result_table = results(dds_nhbe)
summary(result_table)
View(as.data.frame(result_table))
# result_table is a DataFrame not a data.frame!
result_df = as.data.frame(result_table)
View(result_df)
plotCounts(dds_nhbe, gene='ENSG00000265794',
intgroup='conditions')
sum(complete.cases(result_df))
filter_df1 = result_df[complete.cases(result_df),]
View(filter_df1)
# Filter results
# padj < 0.05
# log2FoldChange > 1 < -1
filter_df1$padj < 0.05
filter_df2 = filter_df1[filter_df1$padj < 0.05,]
abs(filter_df2$log2FoldChange) > 1
filter_df3 = filter_df2[abs(filter_df2$log2FoldChange) > 1,]
View(filter_df3)
plotMA(result_table)
# volcano plot
filter_df1$test = filter_df1$padj < 0.05 & abs(filter_df1$log2FoldChange) > 1
filter_df1 = rownames_to_column(filter_df1, var='ensgene')
g = ggplot(filter_df1, aes(x=log2FoldChange,
y=-log10(padj),
name=ensgene)) +
geom_point(aes(colour=test), size=1, alpha=0.3) +
scale_colour_manual(values=c('black', 'red')) +
geom_vline(xintercept=1, colour='green', linetype=3) +
geom_vline(xintercept=-1, colour='green', linetype=3) +
geom_hline(yintercept=-log10(0.05), colour='blue', linetype=3) +
theme_bw() +
theme(legend.position = 'none')
ggplotly(g)
listMarts()
ensembl99 = useEnsembl(biomart="ensembl", version=99)
View(listDatasets(ensembl99))
ensembl99 = useDataset("hsapiens_gene_ensembl",
mart=ensembl99)
View(listAttributes(ensembl99))
View(listFilters(ensembl99))
getBM(attributes=c('ensembl_gene_id', 'ensembl_gene_id_version',
'ensembl_transcript_id', 'ensembl_transcript_id_version',
'external_gene_name'),
filters = c('ensembl_gene_id'),
values = filter_df1$ensgene[1:6],
mart = ensembl99)
annotation = getBM(attributes=c('ensembl_gene_id',
'chromosome_name',
'start_position',
'end_position',
'strand',
'gene_biotype',
'external_gene_name',
'description'),
filters = c('ensembl_gene_id'),
values = filter_df1$ensgene,
mart = ensembl99)
View(annotation)
View(filter_df1)
annotated_df = left_join(filter_df1, annotation,
by=c('ensgene'='ensembl_gene_id'))
View(annotated_df)
g = ggplot(annotated_df, aes(x=log2FoldChange,
y=-log10(padj),
name=external_gene_name)) +
geom_point(aes(colour=test), size=1, alpha=0.3) +
scale_colour_manual(values=c('black', 'red')) +
geom_vline(xintercept=1, colour='green', linetype=3) +
geom_vline(xintercept=-1, colour='green', linetype=3) +
geom_hline(yintercept=-log10(0.05), colour='blue', linetype=3) +
theme_bw() +
theme(legend.position = 'none')
ggplotly(g)
anno_df2 = annotated_df[annotated_df$padj < 0.05,]
anno_df3 = anno_df2[abs(anno_df2$log2FoldChange) > 1,]
degs = anno_df3$ensgene
vst_nhbe = varianceStabilizingTransformation(dds_nhbe)
vst_nhbe_mat = assay(vst_nhbe)
data_for_hm = vst_nhbe_mat[degs,]
rownames(data_for_hm) = anno_df3$external_gene_name
heatmap(data_for_hm)
pheatmap(data_for_hm, fontsize_row=4, scale='row')
greys = colorRampPalette(brewer.pal(9, "Greys"))(100)
pheatmap(data_for_hm, fontsize_row=4, scale='row',
color=greys)
pairs = colorRampPalette(brewer.pal(12, "Paired"))(100)
pheatmap(data_for_hm, fontsize_row=4, scale='row',
color=pairs)
last_scheme = colorRampPalette(brewer.pal(7, "Blues"))(100)
pheatmap(data_for_hm, fontsize_row=4, scale='row',
color=last_scheme, cutree_cols = 2,
cutree_rows = 2)
## GO enrichment
ent_gene = getBM(attributes=c('entrezgene_id'),
filters = c('ensembl_gene_id'),
values = anno_df3$ensgene,
mart = ensembl99)
ent_gene = ent_gene$entrezgene_id
ent_gene = as.character(ent_gene)
ent_uni = getBM(attributes=c('entrezgene_id'),
filters = c('ensembl_gene_id'),
values = annotated_df$ensgene,
mart = ensembl99)
ent_uni = as.character(ent_uni$entrezgene_id)
ent_uni = as.character(ent_uni)
ego = enrichGO(gene = ent_gene,
OrgDb = org.Hs.eg.db,
ont = "BP",
universe = ent_uni)
barplot(ego, showCategory=20)
dotplot(ego, showCategory=20)
cnetplot(ego, showCategory=5)
goplot(ego)
ekg = enrichKEGG(gene = ent_gene,
universe = ent_uni)
write_tsv(anno_df3, "filtered_nhbe_results.txt")