TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation.
Brief Bioinform
; 23(3)2022 05 13.
Article
in En
| MEDLINE
| ID: mdl-35325048
We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer's disease progression in humans.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Genetic Testing
/
Single-Cell Analysis
Type of study:
Prognostic_studies
Limits:
Animals
/
Humans
Language:
En
Journal:
Brief Bioinform
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2022
Document type:
Article
Country of publication:
United kingdom