Computational Biologist

The project involves applying robust methods included but not limited to differential abundance (MILO), cell-cell communication (CellChat), DESeq2, pseudotime inference, topic modeling, and others, to infer associations between gene expression, cell states, signaling pathways, and treatment outcomes.This project will evolve around the analysis of data collected from clinical trial specimens with the goal of identifying Client response and resistance mechanisms in the context of immunotherapy treatment.

This will include analyzing pre-treatment single cell and bulk RNAseq of these specimens. Additionally, data from digital pathology and mutation profiling will be analyzed in an integrative manner. Analyses will include R packages such as Seurat and Limma as well as some more advanced pipelines, to infer associations between treatment outcomes and gene expression, cell states or signaling pathways.Must Have: Proficiency in R AND python Experience analyzing single-cell RNA-seq datasets Experience with working on High Performance Computing clusters Experience with the following scRNAseq analysis tools: Cell-Chat, Seurat, scVI, scanpy.Responsibilities Perform computational analyses to:o Identify cell types, cell states, genes, and pathways associated with clinical outcomes using single-nuc RNAseq data.o Infer pseudo-time based trajectories in pre- and post-treatment specimens and determine their association with clinical outcomes.o Construct gene signatures associated with particular cell states pre and post-treatment Collaborate closely with other computational and wet lab scientists Contribute intellectually to the ongoing research projects Present at team & department meetingsEducation Education level: PhD (preferred), M.Sbull; Preferred majors/disciplines: Bioinformatics, Computational Biology, or related fieldQualifications Background in computational biology, bioinformatics, or related field Proficiency in R, Python, Unix, and Git Experience with performing statistical analysis Experience with analyzing bulk and single-cell RNA-seq Experience with single-cell analysis tools including Seurat, scVI & scanpyNice-to-Have Expertise Familiarity with MILO & Hotspot for differential abundance and topic modeling Familiarity with R Shiny for app development & deployment