Biology subjects

Assefa, A. T.

Publications and source records attributed to Assefa, A. T..

5 recordsLinked to original sources

bioRxiv2024

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

The rapidly evolving field of single-cell transcriptomics has provided a powerful means for understanding cellular heterogeneity. Large-scale studies with multiple biological samples hold promise for discovering differentially expressed biomarkers with a higher level of confidence through a better characterization of the target population. However, the hierarchical nature of these experiments introduces a significant challenge for downstream statistical analysis. Indeed, despite the availability of numerous differential expression methods, only a select few can accurately address the within-patient correlation of single-cell expression profiles. Furthermore, due to the high computational costs associated with some of these methods, their practical use is limited. In this manuscript, we undertake a comprehensive assessment of different strategies to address the hierarchical correlation structure in multi-sample scRNA-seq data. We employ synthetic data generated from a simulator that retains the original correlation structure of multi-patient data while making minimal assumptions, providing a robust platform for benchmarking method performance. Our analyses indicate that neglecting within-patient correlation jeopardizes type I error control. We show that, in line with some previous reports and in contrast with others, Poisson Generalized Estimation Equations provide a useful and flexible framework for addressing these issues. We also show that pseudobulk approaches outperform single-cell level methods across the board. In this work, we resolve the conflicting results regarding the utility of GEEs and their performance relative to pseudobulk approaches. As such, we provide valuable guidelines for researchers navigating the complex landscape of gene expression modeling, and offer insights on choosing the most appropriate methods based on the specific structure and design of their datasets.

bioinformatics↗

bioRxiv2020

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

Technological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

molecular biology↗

bioRxiv2019

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

Single-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

genomics↗

bioRxiv2019

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

SPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.\n\nAvailability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.\n\nSupplementary informationSupplementary data are available at bioR{chi}iv online.

genomics↗

bioRxiv2017

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

BackgroundProtein-coding RNAs (mRNA) have been the primary target of most transcriptome studies in the past, but in recent years, attention has expanded to include long non-coding RNAs (lncRNA). lncRNAs are typically expressed at low levels, and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 14 popular tools for testing DE in RNA-seq data along with their normalization methods is comprehensively evaluated, with a particular focus on lncRNAs and low abundant mRNAs.\n\nResultsThirteen performance metrics were used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. Non-parametric procedures are used to simulate gene expression data in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, we kept track of the results for mRNA and lncRNA separately. All statistical models exhibited inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and analysis of benchmark RNA-seq datasets. No single tool uniformly outperformed the others.\n\nConclusionOverall, the linear modeling with empirical Bayes moderation (limma) and the nonparametric approach (SAMSeq) showed best performance: good control of the false discovery rate (FDR) and reasonable sensitivity. However, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in a realistic clinical settings such as in cancer research. About half of the methods showed severe excess of false discoveries, making these methods unreliable for differential expression analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, http://statapps.ugent.be/tools/AppDGE/

genomics↗