AI Article Synopsis
- Single-cell RNA sequencing (scRNA-seq) technology enables the analysis of transcriptomes from thousands of individual cells at once, improving how we can understand cellular functions and heterogeneity.
- The introduction of barcoding techniques has increased scRNA-seq's efficiency but has also complicated data analysis, highlighting the need for better tools to manage and visualize this complex data.
- The scPipe R/Bioconductor package addresses these challenges by streamlining the processing of scRNA-seq data from various protocols, providing a count matrix for further analysis and generating an HTML report for quality assessment, all with a few simple commands.
Article Abstract
Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorporate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses. To this end, we developed scPipe, an R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple protocols that include CEL-seq, MARS-seq, Chromium 10X, Drop-seq and Smart-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of open-source scRNA-seq analysis tools available in R/Bioconductor and beyond. The scPipe R package is available for download from https://www.bioconductor.org/packages/scPipe.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105007 | PMC |
| http://dx.doi.org/10.1371/journal.pcbi.1006361 | DOI Listing |
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Article Synopsis
- Single-cell RNA sequencing (scRNA-seq) technology enables the analysis of transcriptomes from thousands of individual cells at once, improving how we can understand cellular functions and heterogeneity.
- The introduction of barcoding techniques has increased scRNA-seq's efficiency but has also complicated data analysis, highlighting the need for better tools to manage and visualize this complex data.
- The scPipe R/Bioconductor package addresses these challenges by streamlining the processing of scRNA-seq data from various protocols, providing a count matrix for further analysis and generating an HTML report for quality assessment, all with a few simple commands.
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