Alemu Takele Assefa

Assessing differential cell composition in single-cell studies using voomCLR

This paper introduces voomCLR, a method to assess differential cell composition in single-cell studies by accounting for bias uncertainty and mean-variance structure.

Differential detection workflows for multi-sample single-cell RNA-seq data

The paper develops a workflow for differential detection analysis in single-cell RNA-seq data and shows it provides complementary information to differential expression analysis.

Strategies for addressing pseudoreplication in multi-patient scRNA-seq data

This paper evaluates strategies to address pseudoreplication in multi-patient scRNA-seq data, finding that Poisson GEEs and pseudobulk approaches outperform single-cell methods.

Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

This study benchmarks single cell RNA-seq technologies to assess their ability to detect transcriptional changes in NGP neuroblastoma cells after nutlin-3 treatment, finding heterogeneity in response not captured by bulk RNA-seq.

Probabilistic index models for testing differential expression in single cell RNA sequencing data

The paper introduces a semi-parametric probabilistic index model for differential expression testing in scRNA-seq, showing it effectively controls false discovery rate and identifies biologically meaningful DE.

SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

SPsimSeq simulates bulk and single-cell RNA-seq data by estimating the empirical distribution of real data, allowing for various experimental scenarios and library preparation protocols.

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data

The paper evaluates DE tools for RNA-seq data, finding poor performance for lncRNAs and highlighting the need for more samples to achieve acceptable sensitivity.