Post-selection inference in testing for phenotypic differences with scRNA-Seq
- 1UC Berkeley
Abstract
For the purpose of differential expression (DE) analysis in single-cell RNA-sequencing (scRNA-Seq), phenotype differences between samples are often tested within specific cell types. Cell types are regularly imputed by clustering the same gene expression data which is later used for phenotype testing. This creates the potential for a "double-dipping" or post-selection inference problem resulting in inflated rates of false discoveries. While this selection bias is known to inflate significance in cell-type marker identification, its effect on sample-level phenotype testing, e.g. in patient cohorts, has never been explored despite the growing preponderance of this type of analysis. To address this, we perform an extensive simulation study and demonstrate that naive clustering on uncorrected embeddings can severely inflate the False Discovery Rate (FDR) in the presence of strong phenotypic differences. However, we further show that applying batch-correction methods to remove phenotypic effects prior to clustering resolves the FDR inflation with no obvious loss of power. Finally, we provide measures of phenotypic imbalance that can be applied to real datasets which closely track the false discovery proportion and thus can be used to as part of data exploration to gauge the risk of post-selection inflation of p-values in a particular dataset.
Related
- "Double-dipping" in testing for phenotypic differences with scRNA-Seq
- Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq
- Regularized logistic regression-based gene selection for functional analysis of heterogeneous scRNA-seq macrophage populations in muscle aging and regeneration.
- Multi-Trait Phenotypic and SSR-Based Genetic Diversity Assessment of Individual Plants for Parental Selection in Turf-Type Tall Fescue
- Sexual conflict, directional sexual selection and phenotypic plasticity jointly drive the evolution of extreme phenotypic variation
BibTeX
@misc{sanchez2026postselection,
title = {{Post-selection inference in testing for phenotypic differences with scRNA-Seq}},
author = {N. Sanchez and L. Etourneau and E. Purdom},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.17.752411},
url = {https://www.biorxiv.org/content/10.64898/2026.09.17.752411v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Post-selection inference in testing for phenotypic differences with scRNA-Seq
AU - N. Sanchez
AU - L. Etourneau
AU - E. Purdom
PY - 2026
DA - 2026/09/17
PB - bioRxiv
DO - 10.64898/2026.09.17.752411
UR - https://www.biorxiv.org/content/10.64898/2026.09.17.752411v1
AB - For the purpose of differential expression (DE) analysis in single-cell RNA-sequencing (scRNA-Seq), phenotype differences between samples are often tested within specific cell types. Cell types are regularly imputed by clustering the same gene expression data which is later used for phenotype testing. This creates the potential for a "double-dipping" or post-selection inference problem resulting in inflated rates of false discoveries. While this selection bias is known to inflate significance in cell-type marker identification, its effect on sample-level phenotype testing, e.g. in patient cohorts, has never been explored despite the growing preponderance of this type of analysis. To address this, we perform an extensive simulation study and demonstrate that naive clustering on uncorrected embeddings can severely inflate the False Discovery Rate (FDR) in the presence of strong phenotypic differences. However, we further show that applying batch-correction methods to remove phenotypic effects prior to clustering resolves the FDR inflation with no obvious loss of power. Finally, we provide measures of phenotypic imbalance that can be applied to real datasets which closely track the false discovery proportion and thus can be used to as part of data exploration to gauge the risk of post-selection inflation of p-values in a particular dataset.
ER -