Detect tissue heterogeneity in expression profiles with gene sets (original) (raw)

BioQC is a is a R/Bioconductor package to detect tissue heterogeneity in gene expression data. Tissue heterogeneity is a consequence of unintended profiling of cells of other origins than the tissue of interest and can have both technical (e.g. imperfect disection) or biological (e.g. immune infiltration) reasons.

We demonstrated that tissue heterogeneity is prevalent in 5-15% of all gene expression studies. Ignoring tissue heterogeneity reduces statistical power of data analysis and can, in the worst case, invalidate the conclusions of a study. Therefore, we propose applying BioQC as a routine step in every gene-expression analysis pipeline.

The BioQC method is described in

Zhang, Jitao David, Klas Hatje, Gregor Sturm, Clemens Broger, Martin Ebeling, Martine Burtin, Fabiola Terzi, Silvia Ines Pomposiello, and Laura Badi. “Detect Tissue Heterogeneity in Gene Expression Data with BioQC.” BMC Genomics 18 (2017): 277. doi:10.1186/s12864-017-3661-2.

Installation

Bioconda

Alternatively, you can use the conda package manager.

1. Make sure you set-up the Bioconda channel correctly. The order of the channels is important!
2. (Optional) Create and activate an environment for BioQC

conda create -n bioqc  
conda activate bioqc  

1. Install the bioconductor-bioqc package in your current environment

conda install bioconductor-bioqc  

From Github

The easiest way to install the development version from GitHub is using the remotes package: