We are analyzing https://link.springer.com/article/10.1186/s13059-020-1949-z.

Title:
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data | Genome Biology
Description:
Background 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. Results 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. Conclusions 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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Keywords {🔍}

gene, cells, data, analysis, pathway, single, dorothea, performance, tools, progeny, bulk, scrnaseq, activity, genes, expression, number, additional, file, activities, cell, benchmark, article, coverage, sets, figure, singlecell, tfs, scenic, metaviper, daucell, perturbation, cluster, methods, google, scholar, level, set, performed, size, confidence, samples, dataset, functional, low, simulated, footprint, library, matrices, sample, statistical,

Topics {✒️}

cell-type/cell family-specific cluster-id transcriptome profile julio saez-rodriguez cis-regulatory dna-motif databases /science/article/pii/s2452310019300149 cantini jan gleixner single-cell rna-seq data single-cell transcriptome profiles author information authors scrna-seq protocol + input matrix jovan tanevski single-cell rna-seq single-cell rna sequencing nih grant u54-ca217377 functional genomics analysis macrophage-specific gene functions cell-type-specific information tukeyhsd post-hoc-test ca/fisher/methods/chap6 cell-type-specific tf spi1-directed innate immunity bulk rna-seq data diverse high-throughput techniques supplementary figures s1-s15 real scrna-seq data real single-cell data rna-seq bulk sample original author putative direct-binding interactions underlying scrna-seq protocol bulk rna-seq tools bulk rna-seq samples analyze scrna-seq data cell lines/cell types simulated scrna-seq data simulated single-cell data javier perales-patón saez-rodriguez large-scale rnai screens author correspondence scrna-seq profiles main antigen-presenting cells real scrna-seq datasets single-cell data sets quartz-seq2 protocol yielded pbmc single-cell data scrna-seq dataset originates representative scrna-seq dataset footprint-based functional analysis single-cell based tools

Schema {🗺️}

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      headline:Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
      description: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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            name:Barcelona Institute of Science and Technology (BIST)
            address:
               name:CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
               type:PostalAddress
            type:Organization
            name:Universitat Pompeu Fabra (UPF)
            address:
               name:Universitat Pompeu Fabra (UPF), Barcelona, Spain
               type:PostalAddress
            type:Organization
      name:Bence Szalai
      affiliation:
            name:Semmelweis University
            address:
               name:Faculty of Medicine, Department of Physiology, Semmelweis University, Budapest, Hungary
               type:PostalAddress
            type:Organization
      name:Julio Saez-Rodriguez
      url:http://orcid.org/0000-0002-8552-8976
      affiliation:
            name:Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital
            address:
               name:Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany
               type:PostalAddress
            type:Organization
            name:RWTH Aachen University, Faculty of Medicine
            address:
               name:Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany
      name:Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany
      name:Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany
      name:Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
      name:Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany
      name:German Cancer Research Center (DKFZ), Heidelberg, Germany
      name:European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
      name:Department of Biological Engineering, MIT, Cambridge, USA
      name:CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
      name:Department of Biological Engineering, MIT, Cambridge, USA
      name:Koch Institute for Integrative Cancer Biology, MIT, Cambridge, USA
      name:German Cancer Research Center (DKFZ), Heidelberg, Germany
      name:European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
      name:European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
      name:Department of Biological Engineering, MIT, Cambridge, USA
      name:CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
      name:Universitat Pompeu Fabra (UPF), Barcelona, Spain
      name:Faculty of Medicine, Department of Physiology, Semmelweis University, Budapest, Hungary
      name:Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany
      name:Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany

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