Biology subjects

Sivaganesan, S.

Publications and source records attributed to Sivaganesan, S..

3 recordsLinked to original sources

bioRxiv2022

Integrative signatures of signaling pathway response increase robustness and accuracy of pathway predictions

MotivationAberrant cell signaling is known to drive progression of cancer and many other diseases. The study of signaling pathways within cells is central to identifying drugs that seek to modulate these pathways. Expression of pathway genes (i.e. genes that code for pathway proteins) correlates poorly with signaling pathway activity, making prediction of signaling pathway activity changes based on transcriptional disease signatures a challenging problem. Pathway architecture and response also varies across cell lines, which reflects how drug response varies across a patient population. ResultsHere, we present a transcriptional footprinting framework for predicting changes in activity of signaling pathway by integrating transcriptional signatures of genetic perturbations of pathway genes over a diverse set of cell lines into a integrative Pathway Activity Signature (iPAS). We use an unsupervised multi-task learning approach to create pathway signatures across 12 cell lines using genetic loss of function data from the LINCS project. We also use supervised learning to construct an optimal predictor based on the ensemble of 12 cell line signatures. Our methods achieve a sizeable increase in performance, as measured by prediction of pathways targeted by LINCS chemical perturbagens. AvailabilityOpen source R package iPAS is available at https://github.com/uc-bd2k/iPAS. Contactmedvedm@ucmail.uc.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis

Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each genes dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in co-regulated gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.

bioinformatics↗

bioRxiv2019

Predicting mechanism of action of cellular perturbations with pathway activity signatures

MotivationMisregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures a difficult problem. ResultsWe developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the LINCS transcriptional signatures of genetic perturbations of pathway genes and the transcriptional signature of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only a transcriptional signature of the perturbation fail. Availability and ImplementationOpen source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

systems biology↗