Biology subjects

Leviyang, S.

Publications and source records attributed to Leviyang, S..

3 recordsLinked to original sources

bioRxiv2023

A Random Matrix Approach to Single Cell RNA-seq Analysis

Single cell RNA-seq (scRNAseq) workflows typically start with a raw expression matrix and end with the clustering of sampled cells. Viewed broadly, scRNAseq is a signal processing workflow that takes a transcriptional signal as input and outputs a cell clustering. Currently, we lack a quantitative framework through which to describe the input signal and assess the dependence of correct clustering on the signal properties. As a result, fundamental questions regarding the resolution of scRNAseq remain unanswered and experimentalists have little guidance in determining whether a hypothesized cell type will be clustered by a particular scRNAseq experiment. In this work, we define the notion of a transcriptional signal associated with a gene module, show that the tools of random matrix theory can be used to characterize the signal as it moves through a common (PCA-based) scRNAseq workflow, and develop estimates for cell clustering based on the signal properties and, in particular, the signal strength. We give a formula - that can be computed from expression data - for the signal strength, providing a framework through which scRNAseq resolution can be investigated.

bioinformatics↗

bioRxiv2021

Tree Based Co-Clustering Identifies Variation in Chromatin Accessibility Across Hematopoietic Cell Types

Chromatin accessibility, as measured by ATACseq, varies between hematopoietic cell types in different branches of the hematopoietic differentiation tree, e.g. T cells vs B cells, but methods that relate variation in chromatin accessibility to the placement of a cell type on the differentiation tree are lacking. Using an ATACseq dataset recently published by the ImmGen consortium, we construct associations between chromatin accessibility and hematopoietic cell types using a novel co-clustering approach that accounts for the structure of the hematopoietic, differentiation tree. Under a model in which all loci and cell types within a co-cluster have a shared accessibility state, we show that roughly 80% of cell type associated accessibility variation can be captured through 12 cell type clusters and 20 genomic locus clusters. Using publicly available ChIPseq datasets, we show that our clustering reflects transcription factor binding patterns with implications for regulation across cell types. Our results provide a framework for analysis of chromatin state variation across cell types related by a tree or network.

genomics↗

bioRxiv2020

Characterization and Prediction of ISRE Binding Patterns Across Cell Types Under Type I Interferon Stimulation

Stimulation of cells by type I interferons (IFN) leads to the differential expression of 100s of genes known as interferon stimulated genes, ISGs. The collection of ISGs differentially expressed under IFN stimulation, referred to as the IFN signature, varies across cell types. Non-canonical IFN signaling has been clearly associated with variation in IFN signature across cell types, but the existence of variation in canonical signaling and its impact on IFN signatures is less clear. The canonical IFN signaling pathway involves binding of the transcription factor ISGF3 to IFN-stimulated response elements, ISREs. We examined ISRE binding patterns under IFN stimulation across six cell types using existing ChIPseq datasets available on the GEO and ENCODE databases. We find that ISRE binding is cell specific, particularly for ISREs distal to transcription start sites, potentially associated with enhancer elements, while ISRE binding in promoter regions is more conserved. Given variation of ISRE binding across cell types, we investigated associations between the cell type, homeostatic state and ISRE binding patterns. Taking a machine learning approach and using existing ATACseq and ChIPseq datasets available on GEO and ENCODE, we show that the epigenetic state of an ISRE locus at homeostasis and the DNA sequence of the ISRE locus are predictive of the ISREs binding under IFN stimulation in a cell type, specific manner, particularly for ISRE distal to transcription start sites.

immunology↗