# Data analysis & differential expression¶

Note

if you want to start from here, you can do:

mkdir ~/rnaseq
cd ~/rnaseq


and then run the ‘curl’ commands below.

At this point, you should have four files:

female_repl1_counts.txt
female_repl2_counts.txt
male_repl1_counts.txt
male_repl2_counts.txt


If you look at the file content with ‘head’,

head female_repl1_counts.txt


you’ll see something like this:

XLOC_000001     1
XLOC_000002     0
XLOC_000003     2
XLOC_000004     0
XLOC_000005     0
XLOC_000006     3
XLOC_000007     2
XLOC_000008     0
XLOC_000009     0
XLOC_000010     14


These are the unique gene identifiers from cuffmerge_all.fa, together with their counts as measured by TopHat and htseq-count.

What we’ll do next is compare gene counts across all four files and do differential expression analysis.

We’ll be using edgeR to do the basic differential expression analysis of our counts.

To run edgeR, you need to write a data loading and manipulation script in R. In this case, I’ve provided one – chick.R. This script will load in two samples with two replicates, execute an MA plot, do an MDS analysis/plot, and provide a spreadsheet with differential expression information in it. To download it, click here.

## Running edgeR on a data subset¶

To run the script on the HPC, download the script and data files for the 100k read subsets. At the command line, do:

cd ~/rnaseq


These various .txt files are produced by Mapping reads to the transcriptome with TopHat, Processing another sample with TopHat and HTSeq, and Using the HPC to run jobs in parallel.

Next, to run the R script, do:

module load R
Rscript chick.R


This will produce three files, chick-edgeR-MA-plot.pdf, chick-edgeR-MDS.pdf, and chick-edgeR.csv; they will be in your rnaseq folder in your home directory on the HPC. The CSV file can be opened directly in Excel; you can also look at it here. It consists of five columns: gene name, log fold change, P-value, and FDR-adjusted P-value.

If you look closely at the MA plot, you’ll see that there are three red dots. These are the genes with a False Discovery Rate of 0.2 or less (see chick.R), line 28. Note that the axes on the MA plot are counts per million (CPM, X axis) and fold change (Y axis).

Next, let’s take a look at chick-edgeR.csv. This is a comma-separated value file that you can download and open in Excel; go ahead and do so.

As you can see, it’s got the two columns with fold change and counts per million; it’s also got a P value and a FDR (false discovery rate) value for each gene. And, if you look at the first three rows, you’ll see that these are three rows that yield the red dots on the MA plot! Hey, I wonder what those genes are...

Well, here is where the XLOC gene names are perhaps not that useful :). Let’s go back and see if we can get any more information out of our transcriptome...

Questions:

• Why does the MA plot have the shape that it does?

## Transferring “official” gene names from the official transcriptome¶

If you look at Building a new reference transcriptome, we used TopHat and Cufflinks to build new gene models from our RNAseq, and then merged the gene models with the already existing gene models from the official annotation. This gave us a file ‘cuffmerge_all/nostrand.gtf’ which contained gene annotaions and the gene coordinates for exons; from this, we extracted ‘cuffmerge_all.fa’, which contains a bunch of FASTA sequences. If you look at the top of this file, you’ll see that the FASTA sequence names look like this:

>TCONS_00000001 gene=17.5

These ‘TCONS’ names are unique transcript identifiers; what we really want are the gene names, though. Unfortunately, we don’t have TCONS, we have XLOC, which are unique gene identifiers. How do we turn those into gene names!?

If you look at cuffmerge_all/nostrand.gtf,

head -1 cuffmerge_all/nostrand.gtf


you’ll see lines that contain information like this:

"XLOC_000001"; transcript_id "TCONS_00000002"; exon_number "1"; gene_name "17.5"; oId "NM_205429"; nearest_ref "NM_205429"; class_code "="; tss_id "TSS1"; p_id "P2";


There’s the XLOC number, along with a bunch of other info! We want (at least!) two pieces of information from this - the gene name (here ‘17.5’) and the nearest reference gene (here ‘NM_205429’). How do we get those into the same spreadsheet as the differentially expressed genes?

As with the R script above, this is a situation where a little bit of scripting comes in handy - I’ve written a small Python script to do this, add-gene-name-to-diffexpr-csv.py.

curl -O https://raw.githubusercontent.com/ngs-docs/2014-msu-rnaseq/master/files/add-gene-name-to-diffexpr-csv.py
python add-gene-name-to-diffexpr-csv.py cuffmerge_all/nostrand.gtf chick-edgeR.csv > chick-edgeR-named.csv


You can download my copy of this file and open it in Excel, or you can just look at it online. And hey, look, gene names!

You can look up the NM_ stuff in genbank (actually, googling “genbank NM_204286” will bring you right to a birdbase link), and the gene names can be fed direclty into services like DAVID.

One quick note before we move on – it’s important to realize that we didn’t do any clever analysis to get the gene name and nearest reference gene information into this file. It was simply transferred from the official gene annotation for chick when we ran cuffmerge. We’ll talk a little bit about how to generate your own annotations later.

## Working with DAVID¶

When you’re interested in looking at enrichment of functional gene categories, the DAVID tool for gene enrichment analysis is a common recommendation. The essential idea is to look at some selection of genes (ones that are differentially expressed, usually!) in the background context of a much larger set of genes (all expressed genes that are not differentially expressed).

The simplest way to do this is to pick an FDR, and select all gene accessions above that FDR. For example:

• go to DAVID;

• Select ‘upload’, and paste in the first 1,000 accessions from chick-edgeR-named;

• Under “Select identifier”, choose “GENBANK_ACCESSION”;

• Select “Gene List” for List Type;

• and then “Submit List”.

DAVID will now tell you that less than 80% of the list has mapped; that’s expected, since there are a number of blank spots in the list. Select “Continue to submit the IDs that DAVID could map.”

• You should now be on Step 2. Select “Functional annotation tool.” Go to that link.

• Now, each of the three views (Clustering, Chart, and Table) will give you more information.

For me, under Clustering, Annotation Cluster 6 shows an enrichment of sex-related genes, so I guess that’s good, since we’re comparing male and female blastoderm gene expression from chick! But this also highlights the problems with this kind of analysis – we can see what we want! Bear in mind that we are really looking more at the background of what genes are expressed than what genes are differentially expressed; to do the latter, we’d need to do a larger analysis.

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