rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

Abstract

Motivation Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes’ unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

“…A drawback of single-cell RNA-seq experiments is that, due to the higher cost of profiling of single cells, often fewer biological replicates are available. However, by combining multiple experiments, sample sizes of cells with a desired molecular phenotype can be increased, improving characterization of the transcriptional changes observed in disease (16). In addition, the construction of pseudo-bulk profiles (the sum of the expression values of a gene across all cells obtained from the same individual) from single-cell data makes it possible to account for gene expression variability among patients (17, 18).…”

scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

“… 60 To elucidate MYDGF ’s function, we downloaded multiple human scRNA-seq datasets from the PanglaoDB database. 61 , 62 From the downloaded datasets, we extracted cells from 45 different cell types. Subsequently, a virtual KO of MYDGF for each of these cell types and recovered 45 cell-type-specific perturbation profiles was performed.…”

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

“…60 To elucidate MYDGF ’s function, we downloaded multiple human scRNAseq data sets from the PanglaoDB database. 61,62 From the downloaded data sets, we extracted cells from 45 different cell types. Subsequently, a virtual KO of MYDGF for each of these cell types and recovered 45 cell-type-specific perturbation profiles was performed.…”