scruff: An R/Bioconductor package for preprocessing single-cell RNA-sequencing data

Abstract

Background: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking.Results: Here we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre-and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and opensource R/Bioconductor package.Conclusions: scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands.It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.

Sex Differences in Behavioral and Brainstem Transcriptomic Neuroadaptations following Neonatal Opioid Exposure in Outbred Mice

“…The 12 multiplexed, pooled samples (6 mice from saline and 6 mice from morphine groups) were sequenced (100 bp single-end) on a single lane of the two-lane flowcell. We used the R/Bioconductor package "scruff" for data preprocessing, including demultiplexing, read alignment, read counting, quality checking and data visualization (Wang et al, 2019). Reads were trimmed for quality using Trimmomatic (Bolger et al, 2014).…”

A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model

“…It is worth mentioning that there are R packages for interval-censored data that can also be used for fitting partly interval-censored data. They include the intcox package that implements Pan’s method 11 which extends the iterative convex minorant algorithm to the Cox PH model for interval-censored data; the MIICD package 12 that implements multiple imputation for PH regression with interval-censored data; the coarseDataTools package 13 that fits parametric AFT models to interval-censored data; the interval package 14 that estimates the NPMLE of survival curve and performs log-rank and Wilcoxon type tests for interval-censored data; the SmoothHazard package 15 that can fit semiparametric or parametric PH model to interval-censored data; the survBayes package 16 that fits a PH model by a Bayesian approach to interval-censored data; the dynsurv package 17 that fits Bayesian PH model to interval-censored data; and the icenReg package 18 that fits Bayesian PH, proportional odds, and AFT models for interval-censored data.…”

Smoking Modulates Different Secretory Subpopulations Expressing SARS-CoV-2 Entry Genes in the Nasal and Bronchial Airways

“…Preprocessed count matrices of the nasal and bronchial samples were pre-processed using the Scruff package 27 and analyzed using the Seurat 3.0 28 with standard settings. Quality Control of the nasal and bronchial count matrices was performed using SCTK-QC pipeline 29,30,31 .…”