A recent paper identified an artifact in multiple single-cell RNA-seq (scRNA-seq) data sets generated by the Fluidigm C1 platform. Specifically, Leng* et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. Researchers from the Morgridge Institute have referred to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, the researchers developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations that allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.
The OEFinder GUI for identifying OE genes (shown is implementation using R/RGtk2 package; an implementation using R/shiny package is also available.
Availability – OEFinder is available at: https://github.com/lengning/OEFinder