Biology subjects

Li, W. V.

Publications and source records attributed to Li, W. V..

4 recordsLinked to original sources

bioRxiv2018

A statistical simulator scDesign for rational scRNA-seq experimental design

Single-cell RNA-sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within an individual cell. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths, and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and six different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design.

bioinformatics↗

bioRxiv2018

AIDE: annotation-assisted isoform discovery and abundanceestimation from RNA-seq data

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform DiscovEry), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.

bioinformatics↗

bioRxiv2018

Basal Forebrain Deep Brain Stimulation Impacts the Regulation of Extracellular Vesicle Related Proteins in the Rat Brain

Extracellular vesicle (EV) signaling has attracted considerable attention in recent years because EVs play a key role in long distance cellular communication functions. EV studies have begun to reveal aspects of physiological and physiopathological regulation in numerous applications, although many areas remain to date largely unexplored. Deep brain stimulation (DBS) has shown remarkable therapeutic benefits of patients with neuropsychiatric disorders, but despite of the long and successful history of use, the mechanisms of action on neural ensemble activity are not yet fully understood. Here we explore how DBS of the basal forebrain impacts EV signaling in the rat brain. We employed differential centrifugations to isolate the EVs prefrontal cortex (PFC), hippocampus and striatum. We then performed quantitative analysis of EV-associated proteins using an MS-based proteomics method. We identified a considerable number of EV-associated proteins are modulated by DBS in three brain regions, some of which have been previously linked with central nervous system disorders. Particularly, neurofilament proteins NFL and NFM were both significantly changed in EVs of PFC, hippocampus and striatum after DBS stimulation compared with controls. The SOD1 protein, associated previously with neurodegenerative diseases, was significantly increased only in PFC. Our study is the first, to our knowledge, to use EV protein analysis to examine DBS effects on brain physiological regulation. Our findings open an entirely new perspective on brain area specific DBS effects.

neuroscience↗

bioRxiv2017

scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

The emerging single cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at single-cell resolution. The analysis of scRNA-seq data is complicated by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. Downstream analysis of scRNA-seq would be severely biased if the dropout events are not properly corrected. We introduce scImpute, a statistical method to accurately and robustly impute the dropout values in scRNA-seq data. ScImpute automatically identifies gene expression values affected by dropout events, and only perform imputation on these values without introducing new bias to the rest data. ScImpute also detects outlier or rare cells and excludes them from imputation. Evaluation based on both simulated and real scRNA-seq data on mouse embryos, mouse brain cells, human blood cells, and human embryonic stem cells suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropout events. scImpute is shown to correct false zero counts, enhance the clustering of cell populations and subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

bioinformatics↗