Tyler, S. R. | Search bioRxiv

bioRxiv2022

Anti-correlated Feature Selection Prevents False Discovery of Subpopulations in scRNAseq

While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using 45,348 scRNAseq analyses of real and synthetic datasets, we found that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to 245k cells without the need for high-performance computing.

bioinformatics↗

bioRxiv2021

PMD Uncovers Widespread Cell-State Erasure by scRNAseq Batch Correction Methods

Single cell RNAseq (scRNAseq) batches range from technical-replicates to multi-tissue atlases, thus requiring robust batch-correction methods that operate effectively across this spectrum of between-batch similarity. Commonly employed benchmarks quantify removal of batch effects and preservation of within-batch variation, the preservation of biologically meaningful differences between batches has been under-researched. Here, we address these gaps, quantifying batch effects at the level of cluster composition and along overlapping topologies through the introduction of two new measures. We discovered that standard approaches of scRNAseq batch-correction erase cell-type and cell-state variation in real-world biological datasets, single cell gene expression atlases, and in silico experiments. We highlight through examples showing that these issues may create the artefactual appearance of external validation/replication of findings. Our results demonstrate that either biological effects, if known, must be balanced between batches (like bulk-techniques), or technical effects that vary between batches must be explicitly modeled to prevent erasure of biological variation by unsupervised batch correction approaches.

bioinformatics↗