Biology subjects

Isserlin, R.

Publications and source records attributed to Isserlin, R..

3 recordsLinked to original sources

bioRxiv2018

Single-cell transcriptomics of the aged mouse brain reveals convergent, divergent and unique aging signatures

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how the brain is affected with aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide a comprehensive dataset of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell type specific manner, even at times in opposite directions. Thus, our data reveals that aging, rather than inducing a universal program drives a distinct transcriptional course in each cell population. These data provide an important resource for the aging community and highlight key molecular processes, including ribosomal biogenesis, underlying aging. We believe that this large-scale dataset, which is publicly accessible online (aging-mouse-brain), will facilitate additional discoveries directed towards understanding and modifying the aging process.

neuroscience↗

bioRxiv2017

Pathway enrichment analysis of -omics data

Pathway enrichment analysis helps gain mechanistic insight into large gene lists typically resulting from genome scale (-omics) experiments. It identifies biological pathways that are enriched in the gene list more than expected by chance. We explain pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome sequencing experiments. The protocol comprises three major steps: define a gene list from genome scale data, determine statistically enriched pathways, and visualize and interpret the results. We focus on differentially expressed genes and mutated cancer genes, however the described principles can be applied to diverse -omics data. The protocol is designed for biologists with no prior bioinformatics training and uses freely available software including g:Profiler, GSEA, Cytoscape and Enrichment Map.

bioinformatics↗

bioRxiv2016

netDx: Patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.

bioinformatics↗