Approximate time: 20 minutes

Learning objectives

Introduction to the dataset

For this workshop we will be working with RNA-seq data from a recent publication in Neuron by Baizabal et al. (2018) [1]. We will learn about how to use the ggplot2 package and other related packages to recreate a figure from this publication. In addition, we will also learn how to modify the figure to fit the submission criteria of a different journal.

The publication

The authors in this paper discover an epigenetic mechanism that controls the number and positioning of cortical neurons. They discover that the histone methyltransferase PRDM16 works with enhancer elements to either silence or activate expression of sets of genes that impact the organization of the cerebral cortex.

The authors use various techniques to identify and validate the targets and activities of PRDM16, including ChIP-seq, bulk RNA-seq, FACS, in-situ hybridization and immunofluorescent microscopy on brain samples from embryonic mice, generation of conditional knockout mice, etc. Majority of the figures in this publication are a combination of the evidence gathered from several of these techniques.

The figure

In this workshop, we will focus on recreating Figure 4. This figure demonstrates how knocking out PRDM16 impacts gene expression in three different cell populations in the developing brains of mouse enbryos.

The different types of plots here are:

In addition to the plots listed above, there is (a) a very helpful schematic of the experiment, (b) the FACS output and (c) an immunofluorescence image to show the how well the cell populations were separated from each other.

Reading in the data

In the first half of this workshop, we will be focusing on creating those plots that use the ggplot2 package. In the second half of this workshop, we will (1) explore ggplot2 extensions and external packages to complete the plots in the figure, (2) we will also add the schematics and create a figure with the same layout as in the Neuron paper, and finally, (3) we will show you how to change the layout for a different journal.

First though we need to bring the data into R!

Here is the link to the GEO submission for these data.

  1. We will start by downloading a basic project folder with the data by right-clicking on this link. We recommend that you place this zipped folder on your Desktop for the duration of the workshop.
  2. Unzip the folder, and navigate into the publication_perfect folder. Inside this folder you will find a .Rproj file. Double-click on this to open the project in RStudio.
  3. From the menu bar select ‘File’ –> ‘New File’ –> ‘Rscript’. This will open up the script editor, so you have a place to write and save your code.
  4. Copy and paste the following code into your script. Run the code in the console to read in the data and create three data frames.
# read in the metadata file
meta <- read.csv("data/pp_all_meta.csv", row.names=1)

# read in the normalized gene expression values
normalized_counts <- read.csv("data/pp_all_normalized_counts.csv", row.names=1)

# read in the results of the differential gene expression analysis
results <- read.csv("data/pp_all_results.csv", row.names=1)

Downloaded data

The data we have downloaded and read into R above represents the following 3 files from the larger analysis described in the paper:

This data frame has 25 columns - in addition to the gene name column, there is a column for each sample.

This data frame has 16 columns - in addition to the gene name column, each of the comparisons have 5 columns of results as described below.

  1. _baseMean - Mean of the normalized counts for all samples in the comparison, for a given gene
  2. _log2FoldChange - log2 fold change between WT and PRDM16 KO
  3. _pvalue - Wald test P value
  4. _padj - Benjamini-Hochberg adjusted Wald test P value (P-value after multiple test correction)
  5. _threshold - Logical vector with TRUE values for significantly differentially expressed (DE) genes, FALSE for not DE genes, NA for untested genes. We will be using this column in the next lecture to color the significant genes one color and the non-significant genes a different color.

Making figures for a publication: Art or Science?

Creating plots and figures that convey complex information accurately and in an accessible manner is not easy. Data visualization for biomedical data takes a lot more thinking and planning that we usually set aside time for. A recent paper by O’Donoghue et al. (2018) [2] is a good reference for do’s and dont’s when thinking about displaying information from various types of biomedical experiments. They highlight common practices that create misinterpretation of data, often caused by the human brain’s inability to catch information and process it as we are viewing something.

In the following figure from O’Donoghue et al. (2018) [2] they highlight the shapes and colors that are most effective when plotting.

For the purposes of this workshop, we are focusing on reproducing a well-made, existing figure; but, as we go through both parts of this workshop, we will be highlighting considerations as we encounter various types of data representations.

Having said that, with specific types of biological datasets many good data visualization methods already exist. And it should be realtively simple to emulate a visualization with your dataset. However, you will also encounter datasets that are unique, or you may want to visualize an aspect of the data that is not commonly displayed. In those scenarios, we recommend testing a few visualizations, including different color palettes before settling on the best one. The data-to-viz.com website offers an interactive decision tree to help you identify the best way to display certain dataset formats.

There have been many books written over the years, many papers published, and there is an endless supply of online information about data visualization. In this workshop we are looking to highlight that creating visualization can often take careful consideration of the input data and the final conclusion you want the viewer to reach (quickly). This is, of course, in addition to discussin various R packages that you can use to create a publication-ready figure.

References:

  1. Baizabal et al., 2018, Neuron 98, 945–962. The Epigenetic State of PRDM16-Regulated Enhancers in Radial Glia Controls Cortical Neuron Position
  2. O’Donoghue et al., 2018, Annual Review of Biomedical Data Science 1:1, 275-304. Visualization of Biomedical Data
  3. Betsy Mason, Knowable Magazine 2019. Why scientists need to be better at data visualization

This lesson has been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.