Assessing Sex Bias in Transcriptomic Data

The majority of samples are missing metadata sex labels. To address this issue, we trained models to infer sample sex labels from gene expression data. We then used these labeled to assess sex bias overall, in cell line, and in drug studies.

This work is published in BMC Bioinformatics: Flynn, E., Chang, A. & Altman, R.B. Large-scale labeling and assessment of sex bias in publicly available expression data. BMC Bioinformatics 22, 168 (2021). https://doi.org/10.1186/s12859-021-04070-2

See my repository https://github.com/erflynn/sl_label for the code used to train our sex labeling models and perform downstream analyses.

We are in the process of integrating these labels into the refine-bio resource so that other researchers have easy access to them.