Biology subjects

Prabhakaran, S.

Publications and source records attributed to Prabhakaran, S..

2 recordsLinked to original sources

bioRxiv2017

Single-Cell Immune Map of Breast Carcinoma Reveals Diverse Phenotypic States Driven by the Tumor Microenvironment

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.

immunology↗

bioRxiv2016

K-mer Motif Multinomial Mixtures

1MotivationThe advent of inexpensive high-throughput sequencing (HTS) places new demands on motif discovery algorithms. To confront the challenges and embrace the opportunities presented by the growing wealth of information tied up in HTS datasets, we developed K-mer motif multinomial mixtures (KMMMs), a flexible class of Bayesian models for identifying multiple motifs in sequence sets using K-mer tables. Advantages of this framework are inference with time and space complexities that only scale with K, and the ability to be incorporated into larger Bayesian models.\n\nResultsWe derived a class of probabilistic models of K-mer tables generated from sequence containing multiple motifs. KMMMs model the K-mer table as a multinomial mixture, with motif and background components, which are distributions over K-mers overlapping with each of the latent motifs and over K-mers that do not overlap with any motif, respectively. The framework casts motif discovery as a posterior inference problem, and we present several approximate inference methods that provide accurate reconstructions of motifs in synthetic data. Finally we apply the method to discover motifs in DNAse hypersensitive sites and ChIP-seq peaks obtained from the ENCODE project.

bioinformatics↗