Now published: Ten common mistakes that could ruin your enrichment analysis

 



Glad to share with you our published article Ten common mistakes that could ruin your enrichment analysis. The work was done by Anusuiya Bora (Burnet Bioinformatics, Deakin Uni), Matthew McKenzie (Deakin) and I.

My working group got interested into pathway enrichment analysis in around 2020/2021 because I was receiving review requests for manuscripts and many of them suffered from severe problems, leading me to believe that these issues were prevalent. Our first article on this theme Wijesooriya et al, 2022 demonstrated that severe problems were really common across a big sample of peer-reviewed articles including:

  1. Lack of p-value correction.

  2. Wrong background list.

  3. Lack of methodological details.

We showed that problems 1 and 2 led to dramatic biases in results that could alter the conclusions of transcriptome studies. Due to reviewer disagreements, we were unable to provide a more comprehensive set of best practices in that article, but this remained a priority of ours, especially given that many of the existing recommendations articles actually misses some of the most crucial problems. For example the Chicco and Agapito 2022 aricle missed the background problem.

During the past few years, we developed a deeper understanding of the various enrichment tools and their strengths and weaknesses, including identifying some bugs in some over-representation tools Ziemann et al, 2024.

This lead us to identify some other less common issues that enrichment users should be aware of. These recommendations are based on empirical data. Throughout this new article, we use simulated and real transcriptome data to demonstrate the impact of these errors.

I hope this is a turning point in pathway enrichment and bioinformatics in general. We should no longer be tolerating sloppy analysis and reporting. Our funders deserve better than to have expensive omics studies undermined by flawed enrichment analysis.

Location: Melbourne VIC 3004, Australia

Popular posts from this blog

Uploading data to GEO - which method is faster?

If you have had to upload omics data to GEO before, you'll know it's a bit of a hassle and takes a long time. There are a few methods suggested by the GEO team if you are using the Unix command line: Using 'ncftp' ncftp set passive on set so-bufsize 33554432 open  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/your @mail.com_ yourfolder put -R Folder_with_submission_files Using 'lftp' lftp  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/ your @mail.com _ yourfolder mirror -R Folder_with_submission_files Using 'sftp' (expect slower transfer speeds since this method encrypts on-the-fly) sftp  geoftp @s ftp-private.ncbi.nlm.nih.gov password: yourpasscode cd uploads/ your @mail.com _ yourfolder mkdir new_geo_submission cd new_geo_submission put file_name Using 'ncftpput' (transfers from the command-line without entering an interactive shell) Usage example: ncftpput -F -R -z -u  geoftp  -p "yourpasscode...
Read more

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there...
Read more

My personal thoughts on gene name errors

Image
Well, our paper "Gene name errors are widespread in the scientific literature" in Genome Biology has stirred up some interest. There are a lot of reasons why this article has taken off beyond what I initially envisioned: Most tech-savvy people hate Excel People over-rely on Excel, when there are better alternatives for analytics Everyone has experienced an auto-correct fail and can relate People love "bloopers" People are interested when scientists get it wrong (especially other scientists)  In this post I want to share a few things: Some responses to journalist questions List of media coverage and whether they are reporting things accurately A look into the scripts used themselves Future directions I'll also be answering your questions, so pop them in the comments section.  Some responses to journalist questions Why did you do this? We saw that the problem was first described in 2004, but these errors were present in files fr...
Read more