The experiment

The Hiebert lab is interested in changes in transcription after depletion of a protein called FOXO1 in human B cell lymphoma cells (DHL-4). Hillary and Monica used the dTAG system to degrade FOXO1, and then measured changes in transcription over time. They looked at 5 timepoints after degradation of FOXO1 (0.5 hours, 1 hour, 2 hours, 6 hours, and 24 hours).

Reading in the data

The data was read in using read_csv() from the tidyverse.

library(tidyverse)

proseq <- read_csv("../../data/proseq_082319_noise.csv")

## Parsed with column specification:
## cols(
##   `Transcript ID (duplicates removed)` = col_double(),
##   `0.5 hrs` = col_double(),
##   `1 hr` = col_double(),
##   `2 hrs` = col_double(),
##   `6 hrs` = col_double(),
##   `24 hrs` = col_double()
## )

This table contains the log2-transformed expression values for the untreated cells and for each timepoint after degradation of FOXO1.

Making a heatmap with pheatmap

Monica and Hillary wanted to look for patterns in trancriptional changes over the course of their experiment. To do this they used the pheatmap package to draw a heatmap and the RColorBrewer package to pick aesthetically pleasing colors.

library(pheatmap)
library(RColorBrewer)

First, they removed the first column of their data, which contains gene names.

proseq <- select(proseq, -contains("Transcript"))

Next they specified the color gradient to use for the heatmap

my_palette=colorRampPalette(rev(brewer.pal(n = 11, name = "RdYlBu")))(1000)

Finally they generated a heatmap using the pheatmap() function from pheatmap.

pheatmap(proseq, 
         color = my_palette,
         cellwidth = 60, 
         cellheight = 2, 
         cluster_cols=F, 
         cutree_rows=7, 
         scale="row",
         fontsize_row=2)

By default the pheatmap() function performs hierarchical clustering on the input data before drawing the heatmap. From the clustering and the visualization, there are clear patterns of genes that have increased or decreased transcriptional activity over time after depletion of FOXO1.

The pheatmap() function has many options to customize heatmaps, like adding annotations to the cells and using different clustering algorithms (like k-means). Dave Tang recently wrote an excellent blog post going into more detail about using pheatmap() to examine RNA-seq data.