Mutational Signature in Single Cell Analysis
On Wednesday, December 4, Dr. Josh Campbell and Dr. W. Evan Johnson will present "Mutational Signature in Single Cell Analysis."
In this talk, Dr. Campbell and Dr. Johnson will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for importing data, filtering, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design using population Bioconductor packages.
Drs. Campbell and Johnson also developed novel statistical methods in the toolkit, including approaches for bi-clustering of genes and cells (Celda), batch correction (Combat-seq), and decontamination of ambient RNA (DecontX). The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. They will demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.
The Data Science Seminar Series presents talks from innovators in the research and informatics communities both within and outside of NCI.
Dr. Josh Campbell is an assistant professor in the Division of Computational Biomedicine at Boston University's Department of Medicine. His research interests are in rapidly-evolving and high-throughput genomic technologies in the areas of DNA and RNA sequencing. His current focus includes developing and/or applying methods for identifying genomic alterations in cancer, quantifying the mutagenic effect of carcinogens, and characterizing cellular heterogeneity using single cell RNA sequencing. Through this, he and his team have developed the Celda framework (CEllular Latent Dirichlet Allocation), which can be used to identify hidden transcriptional states and cellular populations in count-based single-cell RNA-seq data. Dr. Campbell holds a doctorate in bioinformatics from Boston University.
Dr. W. Evan Johnson is an associate professor of medicine and biostatistics at Boston University. His research interests include applications in precision genomic medicine, metagenomics, infectious disease diagnostics, batch effects, genomic data analysis, tumor heterogeneity, and cancer research. Dr. Johnson holds a doctorate and master's degree in biostatistics from Harvard University and a master's degree in statistics from Brigham Young University.
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