Biology subjects

Tran, B.

Publications and source records attributed to Tran, B..

23 records · Page 2Linked to original sources

bioRxiv2020

A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

Single-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithms logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

genomics↗

bioRxiv2019

Fast and precise single-cell data analysis using hierarchical autoencoder

A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce a hierarchical autoencoder that reliably extracts representative information of each cell. In an extensive analysis, we demonstrate that the approach vastly outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.

bioinformatics↗

bioRxiv2019

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

We characterized two reference samples for NGS technologies: a human triple-negative breast cancer cell line and a matched normal cell line. Leveraging several whole-genome sequencing (WGS) platforms, multiple sequencing replicates, and orthogonal mutation detection bioinformatics pipelines, we minimized the potential biases from sequencing technologies, assays, and informatics. Thus, our \"truth sets\" were defined using evidence from 21 repeats of WGS runs with coverages ranging from 50X to 100X (a total of 140 billion reads). These \"truth sets\" present many relevant variants/mutations including 193 COSMIC mutations and 9,016 germline variants from the ClinVar database, nonsense mutations in BRCA1/2 and missense mutations in TP53 and FGFR1. Independent validation in three orthogonal experiments demonstrated a successful stress test of the truth set. We expect these reference materials and \"truth sets\" to facilitate assay development, qualification, validation, and proficiency testing. In addition, our methods can be extended to establish new fully characterized reference samples for the community.

bioinformatics↗

bioRxiv2019

Single-cell profiling of tumor-reactive CD4+ T-cells reveals unexpected transcriptomic diversity

Most current tumor immunotherapy strategies leverage cytotoxic CD8+ T cells. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their activity. To address this issue, we have used single-cell mRNA sequencing to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). Designing new computational approaches to characterize subpopulations, we identify TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes follicular helper (Tfh)-like cells but lacks Th1 cells. We identify a type I interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor specific CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies. One Sentence SummarySingle-cell RNA sequencing reveals novel and highly diverse transcriptomic patterns characteristic of CD4+ T cell responses to tumors.

immunology↗

bioRxiv2018

PacBio library preparation using blunt-end adapter ligation produces significant artefactual fusion DNA sequences

Pacific Biosciences (PacBio) RS II sequencer, utilizing Single-Molecule, Real-Time (SMRT) technology, has revolutionized next-generation sequencing by providing an accurate long-read platform. PacBio single-molecule long reads have been used to delineate complex spliceoforms, detect mutations in highly homologous sequences, identify mRNA chimeras and chromosomal translocations, accurately haplotype phasing over multiple kilobase distances and aid in assembly of genomes with complex structural variation. The PacBio protocol for preparation of sequencing templates employs blunt-end hairpin adapter ligation, which enables a short turnaround time for sequence production. However, we have found a significant portion of sequencing yield contains chimeric reads resulting from blunt-end ligation of multiple template molecules to each other prior to adapter ligation. These artefactual fusion DNA sequences pose a major challenge to analysis and can lead to false-positive detection of fusion events. We assessed the frequency of artefactual fusion when using blunt-end adapter ligation and compared it to an alternative method using A/T overhang adapter ligation. The A/T overhang adapter ligation method showed a vast improvement in limiting artefactual fusion events and is now our recommended procedure for adapter ligation during PacBio library preparation.

genetics↗