10x chromium gel bead sequence12/6/2023 Therefore, currently there is no systematic multi-center study that evaluated the influence of technology platform, sample composition, and bioinformatic methods (including preprocessing, normalization, and batch-effect correction) using publicly available standard reference samples and datasets consisting of both mixed and non-mixed biologically distinct samples. By also distributing samples of both cell lines to different centers, where they were subsequently cultured before analysis, we were able to additionally evaluate the sort of experimental variability likely to be encountered in real-world collaborations. ![]() For example, it might be difficult to identify the effect of different technology platforms if studies considered only mixtures of multiple cell types across different platforms as we pointed out in our Nature Biotechnology paper 13, Scanorama failed to integrate data from different technology platforms. However, technical factors (technology platform, inter-laboratory differences in cell handling, and library construction) cannot be distinguished from purely biological variability if only mixtures of cells are used. As described in our associated Nature Biotechnology paper 13, prior studies have attempted to address various aspects relating to scRNA-seq benchmarking 9, 11, 12. Large differences exist across different protocols and platforms in specificity, sensitivity, throughput, chemistry of library construction, and bioinformatics 8, 9, 10, 11, 12, as well as cost. The 3′ end counting-based methods allow the incorporation of unique molecular identifiers (UMIs) to improve quantification of mRNA molecules whereas full-length methods generally provide greater sensitivity of gene detection and ability to identify changes across the length of a transcript, such as alternative splicing, novel transcripts, and mutations, etc. These technologies can be divided into two broad categories: full-length and 3′ end counting-based. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.Ī variety of scRNA-seq technologies and protocols have been developed for biomedical research 1, 2, 3, 4, 5, 6, 7. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. ![]() To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods.
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