DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Abstract

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

Growth Signaling Autonomy in Circulating Tumor Cells Aids Metastatic Seeding

“…Mechanistically, growth signaling autonomy is supported by a secretion-coupled sensing circuit at the Golgi apparatus (24,25) which controls the secretory flux and organelle shape (compact vs fragmented stacks when the circuit is enabled or disabled, respectively). These findings are in keeping with prior work showing that CTC population level behavior is dominated by a few high-secreting cells (47), and that Golgi shape (fragmentation vs compact) is a strong correlate of CTC-EMT and invasiveness (48). Although it remains unknown if/how the Golgi-resident circuit begets EM-plasticity, we conclude that growth signaling autonomy and phenotypic plasticity, two hallmarks of cancer, co-exist in CTCs.…”

A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression

“…Data are available in the GEO database with accession numbers GSE51827, GSE55807, GSE67939, GSE75367, GSE109761, GSE111065, GSE86978 and PRJNA471754. After downloading raw scRNA-seq read count data we followed a standard pre-processing pipeline to filter the cells and genes as described in our previous work [37][38][39]. After filtering the cells we performed the median by ratio normalization method followed by log transformation [39].…”

“…After downloading raw scRNA-seq read count data we followed a standard pre-processing pipeline to filter the cells and genes as described in our previous work [37][38][39]. After filtering the cells we performed the median by ratio normalization method followed by log transformation [39]. For comparison purposes among circulating tumor cells, normal and breast cancer cells, cells with Giantin expression as zero were excluded from further downstream analysis.…”

A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression

“…Data are available in the GEO database with accession numbers GSE51827, GSE55807, GSE67939, GSE75367, GSE109761, GSE111065, GSE86978 and PRJNA471754. After downloading raw scRNAseq read count data we followed a standard pre-processing pipeline to filter the cells and genes as described in our previous work (34)(35)(36). After filtering the cells we performed the median by ratio normalization method followed by log transformation (36).…”

“…After downloading raw scRNAseq read count data we followed a standard pre-processing pipeline to filter the cells and genes as described in our previous work (34)(35)(36). After filtering the cells we performed the median by ratio normalization method followed by log transformation (36). For comparison purposes among circulating tumor cells, normal and breast cancer cells, cells with Giantin expression as zero were excluded from further downstream analysis.…”