README.md

In russHyde/coxpresdbr: Parses and Summarises Data from coxpresdb.jp

coxpresdbr: a package for importing data from coxpresdb.jp and for comparing your own datasets against coxpresdb

coxpresdb.jp is a website / database that summarises the correlation between pairs of genes across thousands of different gene-expression studies. As of 2019, it contains downloadable coexpression data from humans and a range of model organisms (rodents, nematodes, flies, yeast etc). In humans, for example, the dataset may contain all correlations amongst \~ 20k genes, as derived from summarising \~ 160k samples.

This package provides some data-access and statistical tools for working with coxpresdb.jp in R.

Why?

Several tools exist for combining network analysis with gene expression data or for finding some other higher-level context for changes observed in gene expression data (GSEA / GSVA). These may construct a network de novo (eg, WGCNA) or identify interesting subcomponents of an existing network after superimposing some expression statistics upon that network (eg, jActiveModules in Cytoscape).

To use WGCNA requires tons of expression data. And no-one I know has that many good quality samples.

jActiveModules and related tools are great. But they don’t provide much statistical context for the submodules that they select. Would they find an interesting subnetwork for white-noise input? How many subnetworks are considered before selecting the small number of optimal modules. And when applied, how relevant is network construction to the results obtained: if there are non-uniformities in data-acquisition (eg, literature searches) or sampling across the network (eg, funding / publication biases, sub-genome-scale studies), if technical issues influence network structure, and if the network is constructed from a data source that may be only loosely-coupled to gene expression (eg, protein-protein interaction data) could a good network analysis tool still return meaningless results?

As an alternative, to put gene expression data in some network context, one could use coexpression databases. This provides a network structure upon which gene expression data can be overlaid (so can still work when samples are limited in your experiment), that is built from genome-scale studies and where the approach is closely connected to gene-expression studies and where we can perform both gene-focussed and network-wide analyses. (admittedly the tissue types and cellular treatments used in the datasets from which coexpression databases are constructed may still be biased).

Here’s what I might do:

• compute fold-changes and p-values from an RNA-Seq experiment

• for each significant gene, have a see what genes it tends to be coexpressed with / correlated with

• have a look if those coexpression partners are also differentially expressed … in the same direction … and have shared ChIP marks.

Let’s formalise that.

Importing data

Data formats

coxpresdbr currently only works with downloaded copies of the coxpresdb.jp databases. A given database comes as a compressed archive and within that archive, a file is present for each gene. In recent memory there have been two related file formats and two compression types (.tar.bz2 and, more recently, .zip).

The first file format had three columns (tab-separated)

(target_gene, mutual_rank, correlation_coefficient)

but has been replaced by a two column (target_gene, mutual_rank) format. Column names are not present and the source_gene (the name of the file) and target_gene (contents of the first column) are numeric Entrez IDs.

When working with the coxpresdb files, I tend to make a smaller archive setting up the file for each source-gene to contain only the top 100 most correlated genes.

The CoxpresDbAccessor Class

We first define an importer object that permits access to the gene-specific files stored in the coxpresdb archive.

my_importer <- CoxpresDbAccessor(path_to_coxpresdb_archive)

Importing the coexpression data & filtering

Then to pull out the top 10 coexpression partners for a given set of genes, you can call:

get_coex_partners(my_gene_set, importer = my_importer, n_partners = 10)

This returns a data-frame with columns: ( source_id, target_id, mutual_rank; correlation coefs, if present in the coxpresdb archive, are disregarded since they aren’t present in all coxpresdb releases)

You can specify the universe of genes from which the top-N coexpression partners are chosen by using the argument universe=... and you can filter to ensure that the returned gene-pairs have a mutual-rank score below some threshold using mr_threshold.

Gene-focussed statistical summaries

Network-level analysis workflows

russHyde/coxpresdbr documentation built on Dec. 24, 2019, 11:59 a.m.