differential_expression


Alignment of single-cell trajectories to compare cellular expression dynamics

Our next meeting will be at 2pm on April 23rd, in room 4160 of the Discovery building. Our Selected paper is Alignment of single-cell trajectories to compare cellular expression dynamics.
The abstract is as follows.

Single-cell RNA sequencing and high-dimensional cytometry can be used to generate detailed trajectories of dynamic biological processes such as differentiation or development. Here we present cellAlign, a quantitative framework for comparing expression dynamics within and between single-cell trajectories. By applying cellAlign to mouse and human embryonic developmental trajectories, we systematically delineate differences in the temporal regulation of gene expression programs that would otherwise be masked.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

Our next meeting will be at 12:30 on September 12th, in room 3160 of the Discovery building. Our Selected paper is Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.
The abstract is as follows.

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations

Our next meeting will be at 12:30 on August 1st, in room 3160 of the Discovery building. Our Selected paper is CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations.
The abstract is as follows.

Motivation: Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable.

Results: In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type. .

We welcome all who can join us for this discussion. Feel free to begin that discussion in the comments section below.


04.29.15

EBSeq-HMM: A Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Ning Leng1,2, Yuan Li<sup)1, Brian E. Mcintosh2, Bao Kim Nguyen2, Bret Duffin2, Shulan Tian2, James A. Thomson2,3,4, Colin Dewey5, Ron Stewart2 and Christina Kendziorski5,*

– Author Affiliations

1Department of Statistics, University of Wisconsin, Madison, WI
2Regenerative Biology, Morgridge Institute for Research, Madison, WI
3Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI
4Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA
5Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI
*To whom correspondence should be addressed. Christina Kendziorski, E-mail: kendzior@biostat.wisc.edu

  • Received October 14, 2014.
  • Revision received February 23, 2015.
  • Accepted March 30, 2015.

Abstract

Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data.

Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression.

Availability: An R package containing examples and sample datasets is available at Bioconductor.

Contact: kendzior@biostat.wisc.edu

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com