Biology subjects

Christina Kendziorski

Publications and source records attributed to Christina Kendziorski.

4 recordsLinked to original sources

bioRxiv2016

SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions

MotivationWith the development of single cell RNA-seq (scRNA-seq) technology, scRNA-seq experiments with ordered conditions (e.g. time-course) are becoming common. Methods developed for analyzing ordered bulk RNA-seq experiments are not applicable to scRNA-seq, since their distributional assumptions are often violated by additional heterogeneities prevalent in scRNA-seq. Here we present SC-Pattern - an empirical Bayes model to characterize genes with expression changes in ordered scRNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates.\n\nResultsSimulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.\n\nAvailability and ImplementationThe SCPattern is implemented as an R package along with a user-friendly graphical interface, which are available at:https://github.com/lengning/SCPattern\n\nContact: rstewart@morgridge.org

Bioinformatics↗

bioRxiv2015

scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. Although understanding such heterogeneity is of primary interest in a number of studies, for convenience, statistical methods often treat cellular heterogeneity as a nuisance factor. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, we demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences. The freely available R package scDD implements the approach.

Genomics↗

bioRxiv2015

OEFinder: A user interface to identify and visualize ordering effects in single-cell RNA-seq data

A recent paper identified an artifact in multiple single-cell RNA-seq (scRNA-seq) data sets generated by the Fluidigm C1 platform. Specifically, Leng* et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations that allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.\n\nAvailability and ImplementationOEFinder is freely available at https://github.com/lengning/OEFinder\n\nContactrstewart@morgridge.org

Bioinformatics↗

bioRxiv2015

Identification of Slco1a6 as a candidate gene that broadly affects gene expression in mouse pancreatic islets

We surveyed gene expression in six tissues in an F2 intercross between mouse strains C57BL/6J (abbreviated B6) and BTBR T+ tf /J (abbreviated BTBR) made genetically obese with the Leptin(ob) mutation. We identified a number of expression quantitative trait loci (eQTL) affecting the expression of numerous genes distal to the locus, called trans-eQTL hotspots. Some of these trans-eQTL hotspots showed effects in multiple tissues, whereas some were specific to a single tissue. An unusually large number of transcripts (7% of genes) mapped in trans to a hotspot on chromosome 6, specifically in pancreatic islets. By considering the first two principal components of the expression of genes mapping to this region, we were able to convert the multivariate phenotype into a simple Mendelian trait. Fine-mapping the locus by traditional methods reduced the QTL interval to a 298 kb region containing only three genes, including Slco1a6, one member of a large family of organic anion transporters. Direct genomic sequencing of all Slco1a6 exons identified a non-synonymous coding SNP that converts a highly conserved proline residue at amino acid position 564 to serine. Molecular modeling suggests that Pro564 faces an aqueous pore within this 12-transmembrane domain-spanning protein. When transiently overexpressed in HEK293 cells, BTBR OATP1A6-mediated cellular uptake of the bile acid taurocholic acid (TCA) was enhanced compared to B6 OATP1A6. Our results suggest that genetic variation in Slco1a6 leads to altered transport of TCA (and potentially other bile acids) by pancreatic islets, resulting in broad gene regulation.

Genetics↗