11,658 research outputs found

    Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

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    Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene-and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq

    Interactive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)

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    I will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design. The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. We demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.NIH U01CA22041

    Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

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    Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used
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