DESeq2 visualizations - heatmap
NOTE: It may take a bit longer to load this exercise.
Visualizations can also be helpful in exploring the significant genes in more detail. The expression heatmap can be helpful in looking at how different the expression of all significant genes are between sample groups, while the expression plot can look at the top significant genes or choose individual genes of interest to investigate the expression levels between samplegroups.
This exercise is part of the course
RNA-Seq with Bioconductor in R
Exercise instructions
Subset the normalized counts to only include the significant genes. Use the row names of the
smoc2_res_sig
significant results to subset the normalized counts,normalized_counts_smoc2
.Create the heatmap using
sig_norm_counts_smoc2
. Color the heatmap using the palette,heat_colors
, cluster the rows without showing row names, and scale the values by "row". For the annotation, useselect()
to select only thecondition
column from thesmoc2_metadata
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Subset normalized counts to significant genes
sig_norm_counts_smoc2 <- ___[___(___), ]
# Choose heatmap color palette
heat_colors <- brewer.pal(n = 6, name = "YlOrRd")
# Plot heatmap
pheatmap(___,
color = ___,
cluster_rows = ___,
show_rownames = ___,
annotation = ___(___, ___),
scale = ___)