Biology subjects

Amezquita, R. A.

Publications and source records attributed to Amezquita, R. A..

4 recordsLinked to original sources

bioRxiv2022

Defining cellular population dynamics at single cell resolution during prostate cancer progression

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigated prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in vivo. We observe a dramatic expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is in part androgen responsive. Androgen independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

cancer biology↗

bioRxiv2019

Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

Multivariate logistic regression (mLR) has been recently proposed by Ntranos et al. to perform gene differential expression analyses of single-cell RNA-sequencing (scRNAseq) data. Herein we reproduce and extend some of their findings. We notably show that while mLR performs better in simulated datasets, these simulations do not recapitulate important features of experimental datasets. Indeed, our results suggest that MAST followed by Sidak aggregation of the p-values perform better than mLR on experimental datasets. Overall, we highlight that most of the new results obtained by Ntranos et al is likely due to the quantification of scRNAseq data at the transcript or transcript compatibility classes level, rather than the use of mLR.

bioinformatics↗

bioRxiv2019

Orchestrating Single-Cell Analysis with Bioconductor

Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.

genomics↗

bioRxiv2018

marge: An API for Analysis of Motifs Using HOMER in R

Profiling of open chromatin regions through the assay for transposase-accessible chromatin (ATAC) and transcription factor binding via chromatin immunoprecipitation (ChIP) sequencing has increased our ability to resolve patterns of putative transcription factor binding sites at the genome-wide level. Popular tools such as [HOMER (http://homer.ucsd.edu/homer/) and [MEME] (http://meme-suite.org/) have driven forward the analyses of sequence composition, deriving de novo motifs and searching for the enrichment of known motifs. However, their interfaces do not allow for the construction of parallel inquiries of multiple datasets. Furthermore, their results do not conform to formats amenable to tidy analyses, presenting a significant bottleneck in motif analysis. Here, I present marge, a companion R package that interfaces with HOMER to facilitate the construction of queries and to tidy results for further downstream analyses.

bioinformatics↗