Ali


Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

Our next meeting will be at 3:00 on October 10th, in room 3160 of the Discovery building. Our Selected paper is Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.
The abstract is as follows.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC

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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.

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Tensor decomposition for multiple-tissue gene expression experiments

Our next meeting will be at 12:30 on August 15th, in room 3160 of the Discovery building. Our Selected paper is Tensor decomposition for multiple-tissue gene expression experiments.
The abstract is as follows.

Genome-wide association studies of gene expression traits and other cellular phenotypes have successfully identified links between genetic variation and biological processes. The majority of discoveries have uncovered cis–expression quantitative trait locus (eQTL) effects via mass univariate testing of SNPs against gene expression in single tissues. Here we present a Bayesian method for multiple-tissue experiments focusing on uncovering gene networks linked to genetic variation. Our method decomposes the 3D array (or tensor) of gene expression measurements into a set of latent components. We identify sparse gene networks that can then be tested for association against genetic variation across the genome. We apply our method to a data set of 845 individuals from the TwinsUK cohort with gene expression measured via RNA-seq analysis in adipose, lymphoblastoid cell lines (LCLs) and skin. We uncover several gene networks with a genetic basis and clear biological and statistical significance. Extensions of this approach will allow integration of different omics, environmental and phenotypic data sets.

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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. .

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Simultaneous Pathway Activity Inference and Gene Expression Analysis Using RNA Sequencing

Our next meeting will be at 12:30 on June 20th, in room 3160 of the Discovery building. Our Selected paper is Simultaneous Pathway Activity Inference and Gene Expression Analysis Using RNA Sequencing.
The abstract is as follows.

Reporter gene assays are a venerable tool for studying signaling pathways, but they lack the throughput and complexity necessary to contribute to a systems-level understanding of endogenous signaling networks. We present a parallel reporter assay, transcription factor activity sequencing (TF-seq), built on synthetic DNA enhancer elements, which enables parallel measurements in primary cells of the transcriptome and transcription factor activity from more than 40 signaling pathways. Using TF-seq in Myd88−/− macrophages, we captured dynamic pathway activity changes underpinning the global transcriptional changes of the innate immune response. We also applied TF-seq to investigate small molecule mechanisms of action and find a role for NF-κB activation and coordination of the STAT1 response in the macrophage reaction to the anti-inflammatory natural product halofuginone. Simultaneous TF-seq and global gene expression profiling represent an integrative approach for gaining mechanistic insight into pathway activity and transcriptional changes that result from genetic and small molecule perturbations.

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