cwlVersion: v1.0 class: Workflow requirements: - class: SubworkflowFeatureRequirement - class: StepInputExpressionRequirement - class: InlineJavascriptRequirement - class: MultipleInputFeatureRequirement inputs: feature_bc_matrices_folder: type: File doc: | Path to the compressed folder with feature-barcode matrix from Cell Ranger Count/Aggregate experiment in MEX format. aggregation_metadata: type: File? doc: | Path to the metadata TSV/CSV file to set the datasets identities. If '--mex' points to the Cell Ranger Aggregate outputs, the aggregation.csv file can be used. If input is not provided, the default dummy_metadata.csv will be used instead. grouping_data: type: File? doc: | Path to the TSV/CSV file to define datasets grouping. First column - 'library_id' with the values and order that correspond to the 'library_id' column from the '--identity' file, second column 'condition'. Default: each dataset is assigned to its own group. barcodes_data: type: File? doc: | Path to the headerless TSV/CSV file with the list of barcodes to select cells of interest (one barcode per line). Prefilters input feature-barcode matrix to include only selected cells. Default: use all cells. rna_minimum_cells: type: int? doc: | Include only genes detected in at least this many cells. Default: 5 (applied to all datasets) minimum_genes: type: - "null" - int - int[] doc: | Include cells where at least this many genes are detected. If multiple values provided, each of them will be applied to the correspondent dataset from the '--mex' input based on the '--identity' file. Default: 250 (applied to all datasets) maximum_genes: type: - "null" - int - int[] doc: | Include cells with the number of genes not bigger than this value. If multiple values provided, each of them will be applied to the correspondent dataset from the '--mex' input based on the '--identity' file. Default: 5000 (applied to all datasets) rna_minimum_umi: type: - "null" - int - int[] doc: | Include cells where at least this many UMI (transcripts) are detected. If multiple values provided, each of them will be applied to the correspondent dataset from the '--mex' input based on the '--identity' file. Default: 500 (applied to all datasets) minimum_novelty_score: type: - "null" - float - float[] doc: | Include cells with the novelty score not lower than this value, calculated for as log10(genes)/log10(UMI). If multiple values provided, each of them will be applied to the correspondent dataset from the '--mex' input based on the '--identity' file. Default: 0.8 (applied to all datasets) mito_pattern: type: string? doc: | Regex pattern to identify mitochondrial genes. Default: '^Mt-' maximum_mito_perc: type: float? doc: | Include cells with the percentage of transcripts mapped to mitochondrial genes not bigger than this value. Default: 5 (applied to all datasets) cell_cycle_data: type: File? doc: | Path to the TSV/CSV file with the information for cell cycle score assignment. First column - 'phase', second column 'gene_id'. If loaded Seurat object already includes cell cycle scores in 'S.Score' and 'G2M.Score' metatada columns they will be removed. Default: skip cell cycle score assignment. normalization_method: type: - "null" - type: enum symbols: - "sct" - "log" - "sctglm" doc: | Normalization method applied to genes expression counts. If loaded Seurat object includes multiple datasets, normalization will be run independently for each of them, unless integration is disabled with --ntgr set to 'none' Default: sct integration_method: type: - "null" - type: enum symbols: - "seurat" - "none" doc: | Integration method used for joint analysis of multiple datasets. Automatically set to 'none' if loaded Suerat object includes only one dataset. Default: seurat highly_var_genes_count: type: int? doc: | Number of highly variable genes used in datasets integration, scaling and dimensionality reduction. Default: 3000 regress_mito_perc: type: boolean? doc: | Regress the percentage of transcripts mapped to mitochondrial genes as a confounding source of variation. Default: false regress_rna_umi: type: boolean? doc: | Regress UMI per cell counts as a confounding source of variation. Default: false regress_genes: type: boolean? doc: | Regress genes per cell counts as a confounding source of variation. Default: false regress_cellcycle: type: boolean? doc: | Regress cell cycle scores as a confounding source of variation. Ignored if --cellcycle is not provided. Default: false dimensions: type: - "null" - int - int[] doc: | Dimensionality to use in UMAP projection and when constructing nearest-neighbor graph before clustering (from 1 to 50). If single value N is provided, use from 1 to N dimensions. If multiple values are provided, subset to only selected dimensions. Default: from 1 to 10 umap_spread: type: float? doc: | The effective scale of embedded points on UMAP. In combination with '--mindist' it determines how clustered/clumped the embedded points are. Default: 1 umap_mindist: type: float? doc: | Controls how tightly the embedding is allowed compress points together on UMAP. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5. Default: 0.3 umap_neighbors: type: int? doc: | Determines the number of neighboring points used in UMAP. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50. Default: 30 umap_metric: type: - "null" - type: enum symbols: - "euclidean" - "manhattan" - "chebyshev" - "minkowski" - "canberra" - "braycurtis" - "mahalanobis" - "wminkowski" - "seuclidean" - "cosine" - "correlation" - "haversine" - "hamming" - "jaccard" - "dice" - "russelrao" - "kulsinski" - "ll_dirichlet" - "hellinger" - "rogerstanimoto" - "sokalmichener" - "sokalsneath" - "yule" doc: | The metric to use to compute distances in high dimensional space for UMAP. Default: cosine umap_method: type: - "null" - type: enum symbols: - "uwot" - "uwot-learn" - "umap-learn" doc: | UMAP implementation to run. If set to 'umap-learn' use --umetric 'correlation' Default: uwot cluster_metric: type: - "null" - type: enum symbols: - "euclidean" - "cosine" - "manhattan" - "hamming" doc: | Distance metric used when constructing nearest-neighbor graph before clustering. Default: euclidean resolution: type: - "null" - float - float[] doc: | Clustering resolution applied to the constructed nearest-neighbor graph. Can be set as an array. Default: 0.3, 0.5, 1.0 genes_of_interest: type: - "null" - string - string[] doc: | Genes of interest to build genes expression plots. Default: None identify_diff_genes: type: boolean? doc: | Identify differentially expressed genes (putative gene markers) between each pair of clusters for all resolutions. Default: false minimum_logfc: type: float? doc: | For putative gene markers identification include only those genes that on average have log fold change difference in expression between every tested pair of clusters not lower than this value. Ignored if '--diffgenes' is not set. Default: 0.25 minimum_pct: type: float? doc: | For putative gene markers identification include only those genes that are detected in not lower than this fraction of cells in either of the two tested clusters. Ignored if '--diffgenes' is not set. Default: 0.1 only_positive_diff_genes: type: boolean? doc: | For putative gene markers identification return only positive markers. Ignored if '--diffgenes' is not set. Default: false test_to_use: type: - "null" - type: enum symbols: - "wilcox" - "bimod" - "roc" - "t" - "negbinom" - "poisson" - "LR" - "MAST" - "DESeq2" doc: | Statistical test to use for putative gene markers identification. Ignored if '--diffgenes' is not set. Default: wilcox parallel_memory_limit: type: int? doc: | Maximum memory in GB allowed to be shared between the workers when using multiple --cpus. Default: 32 vector_memory_limit: type: int? doc: | Maximum vector memory in GB allowed to be used by R. Default: 128 threads: type: int? doc: | Number of cores/cpus to use. Default: 1 outputs: raw_1_2_qc_mtrcs_pca_plot_png: type: File? outputSource: sc_rna_filter/raw_1_2_qc_mtrcs_pca_plot_png doc: | PC1 and PC2 from the QC metrics PCA (not filtered). PNG format raw_2_3_qc_mtrcs_pca_plot_png: type: File? outputSource: sc_rna_filter/raw_2_3_qc_mtrcs_pca_plot_png doc: | PC2 and PC3 from the QC metrics PCA (not filtered). PNG format raw_cells_count_plot_png: type: File? outputSource: sc_rna_filter/raw_cells_count_plot_png doc: | Number of cells per dataset (not filtered). PNG format raw_umi_dnst_plot_png: type: File? outputSource: sc_rna_filter/raw_umi_dnst_plot_png doc: | UMI per cell density (not filtered). PNG format raw_gene_dnst_plot_png: type: File? outputSource: sc_rna_filter/raw_gene_dnst_plot_png doc: | Genes per cell density (not filtered). PNG format raw_gene_umi_corr_plot_png: type: File? outputSource: sc_rna_filter/raw_gene_umi_corr_plot_png doc: | Genes vs UMI per cell correlation (not filtered). PNG format raw_mito_dnst_plot_png: type: File? outputSource: sc_rna_filter/raw_mito_dnst_plot_png doc: | Percentage of transcripts mapped to mitochondrial genes per cell density (not filtered). PNG format raw_nvlt_dnst_plot_png: type: File? outputSource: sc_rna_filter/raw_nvlt_dnst_plot_png doc: | Novelty score per cell density (not filtered). PNG format raw_qc_mtrcs_dnst_plot_png: type: File? outputSource: sc_rna_filter/raw_qc_mtrcs_dnst_plot_png doc: | QC metrics per cell density (not filtered). PNG format raw_umi_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/raw_umi_dnst_spl_cnd_plot_png doc: | Split by grouping condition UMI per cell density (not filtered). PNG format raw_gene_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/raw_gene_dnst_spl_cnd_plot_png doc: | Split by grouping condition genes per cell density (not filtered). PNG format raw_mito_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/raw_mito_dnst_spl_cnd_plot_png doc: | Split by grouping condition the percentage of transcripts mapped to mitochondrial genes per cell density (not filtered). PNG format raw_nvlt_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/raw_nvlt_dnst_spl_cnd_plot_png doc: | Split by grouping condition the novelty score per cell density (not filtered). PNG format fltr_1_2_qc_mtrcs_pca_plot_png: type: File? outputSource: sc_rna_filter/fltr_1_2_qc_mtrcs_pca_plot_png doc: | PC1 and PC2 from the QC metrics PCA (filtered). PNG format fltr_2_3_qc_mtrcs_pca_plot_png: type: File? outputSource: sc_rna_filter/fltr_2_3_qc_mtrcs_pca_plot_png doc: | PC2 and PC3 from the QC metrics PCA (filtered). PNG format fltr_cells_count_plot_png: type: File? outputSource: sc_rna_filter/fltr_cells_count_plot_png doc: | Number of cells per dataset (filtered). PNG format fltr_umi_dnst_plot_png: type: File? outputSource: sc_rna_filter/fltr_umi_dnst_plot_png doc: | UMI per cell density (filtered). PNG format fltr_gene_dnst_plot_png: type: File? outputSource: sc_rna_filter/fltr_gene_dnst_plot_png doc: | Genes per cell density (filtered). PNG format fltr_gene_umi_corr_plot_png: type: File? outputSource: sc_rna_filter/fltr_gene_umi_corr_plot_png doc: | Genes vs UMI per cell correlation (filtered). PNG format fltr_mito_dnst_plot_png: type: File? outputSource: sc_rna_filter/fltr_mito_dnst_plot_png doc: | Percentage of transcripts mapped to mitochondrial genes per cell density (filtered). PNG format fltr_nvlt_dnst_plot_png: type: File? outputSource: sc_rna_filter/fltr_nvlt_dnst_plot_png doc: | Novelty score per cell density (filtered). PNG format fltr_qc_mtrcs_dnst_plot_png: type: File? outputSource: sc_rna_filter/fltr_qc_mtrcs_dnst_plot_png doc: | QC metrics per cell density (filtered). PNG format fltr_umi_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/fltr_umi_dnst_spl_cnd_plot_png doc: | Split by grouping condition UMI per cell density (filtered). PNG format fltr_gene_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/fltr_gene_dnst_spl_cnd_plot_png doc: | Split by grouping condition genes per cell density (filtered). PNG format fltr_mito_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/fltr_mito_dnst_spl_cnd_plot_png doc: | Split by grouping condition the percentage of transcripts mapped to mitochondrial genes per cell density (filtered). PNG format fltr_nvlt_dnst_spl_cnd_plot_png: type: File? outputSource: sc_rna_filter/fltr_nvlt_dnst_spl_cnd_plot_png doc: | Split by grouping condition the novelty score per cell density (filtered). PNG format sc_rna_filter_stdout_log: type: File outputSource: sc_rna_filter/stdout_log doc: | stdout log generated by sc_rna_filter step sc_rna_filter_stderr_log: type: File outputSource: sc_rna_filter/stderr_log doc: | stderr log generated by sc_rna_filter step elbow_plot_png: type: File? outputSource: sc_rna_reduce/elbow_plot_png doc: | Elbow plot (from cells PCA). PNG format qc_dim_corr_plot_png: type: File? outputSource: sc_rna_reduce/qc_dim_corr_plot_png doc: | Correlation plots between QC metrics and cells PCA components. PNG format umap_qc_mtrcs_plot_png: type: File? outputSource: sc_rna_reduce/umap_qc_mtrcs_plot_png doc: | QC metrics on cells UMAP. PNG format umap_spl_mito_plot_png: type: File? outputSource: sc_rna_reduce/umap_spl_mito_plot_png doc: | Split by the percentage of transcripts mapped to mitochondrial genes cells UMAP. PNG format umap_spl_umi_plot_png: type: File? outputSource: sc_rna_reduce/umap_spl_umi_plot_png doc: | Split by the UMI per cell counts cells UMAP. PNG format umap_spl_gene_plot_png: type: File? outputSource: sc_rna_reduce/umap_spl_gene_plot_png doc: | Split by the genes per cell counts cells UMAP. PNG format umap_gr_cnd_spl_ph_plot_png: type: File? outputSource: sc_rna_reduce/umap_gr_cnd_spl_ph_plot_png doc: | Grouped by condition split by cell cycle cells UMAP. PNG format umap_gr_cnd_spl_mito_plot_png: type: File? outputSource: sc_rna_reduce/umap_gr_cnd_spl_mito_plot_png doc: | Grouped by condition split by the percentage of transcripts mapped to mitochondrial genes cells UMAP. PNG format umap_gr_cnd_spl_umi_plot_png: type: File? outputSource: sc_rna_reduce/umap_gr_cnd_spl_umi_plot_png doc: | Grouped by condition split by the UMI per cell counts cells UMAP. PNG format umap_gr_cnd_spl_gene_plot_png: type: File? outputSource: sc_rna_reduce/umap_gr_cnd_spl_gene_plot_png doc: | Grouped by condition split by the genes per cell counts cells UMAP. PNG format sc_rna_reduce_stdout_log: type: File outputSource: sc_rna_reduce/stdout_log doc: | stdout log generated by sc_rna_reduce step sc_rna_reduce_stderr_log: type: File outputSource: sc_rna_reduce/stderr_log doc: | stderr log generated by sc_rna_reduce step umap_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/umap_res_plot_png doc: | Clustered cells UMAP. PNG format slh_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/slh_res_plot_png doc: | Silhouette scores. Downsampled to max 500 cells per cluster. PNG format umap_spl_idnt_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/umap_spl_idnt_res_plot_png doc: | Split by dataset clustered cells UMAP. PNG format cmp_gr_clst_spl_idnt_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_clst_spl_idnt_res_plot_png doc: | Grouped by cluster split by dataset cells composition plot. Downsampled. PNG format cmp_gr_idnt_spl_clst_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_idnt_spl_clst_res_plot_png doc: | Grouped by dataset split by cluster cells composition plot. Downsampled. PNG format umap_spl_cnd_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/umap_spl_cnd_res_plot_png doc: | Split by grouping condition clustered cells UMAP. PNG format cmp_gr_clst_spl_cnd_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_clst_spl_cnd_res_plot_png doc: | Grouped by cluster split by condition cells composition plot. Downsampled. PNG format cmp_gr_cnd_spl_clst_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_cnd_spl_clst_res_plot_png doc: | Grouped by condition split by cluster cells composition plot. Downsampled. PNG format umap_spl_ph_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/umap_spl_ph_res_plot_png doc: | Split by cell cycle phase clustered cells UMAP. PNG format cmp_gr_ph_spl_idnt_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_ph_spl_idnt_res_plot_png doc: | Grouped by cell cycle phase split by dataset cells composition plot. Downsampled. PNG format cmp_gr_ph_spl_clst_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/cmp_gr_ph_spl_clst_res_plot_png doc: | Grouped by cell cycle phase split by cluster cells composition plot. Downsampled. PNG format xpr_avg_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/xpr_avg_res_plot_png doc: | Log normalized scaled average gene expression per cluster. PNG format xpr_per_cell_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/xpr_per_cell_res_plot_png doc: | Log normalized gene expression on cells UMAP. PNG format xpr_dnst_res_plot_png: type: - "null" - type: array items: File outputSource: sc_rna_cluster/xpr_dnst_res_plot_png doc: | Log normalized gene expression density per cluster. PNG format gene_markers_tsv: type: File? outputSource: sc_rna_cluster/gene_markers_tsv doc: | Differentially expressed genes between each pair of clusters for all resolutions. TSV format ucsc_cb_html_data: type: Directory? outputSource: sc_rna_cluster/ucsc_cb_html_data doc: | Directory with UCSC Cellbrowser html data. seurat_data_rds: type: File outputSource: sc_rna_cluster/seurat_data_rds doc: | Processed Seurat data in RDS format sc_rna_cluster_stdout_log: type: File outputSource: sc_rna_cluster/stdout_log doc: | stdout log generated by sc_rna_cluster step sc_rna_cluster_stderr_log: type: File outputSource: sc_rna_cluster/stderr_log doc: | stderr log generated by sc_rna_cluster step steps: uncompress_feature_bc_matrices: doc: | Extracts the content of TAR file into a folder run: ../tools/tar-extract.cwl in: file_to_extract: feature_bc_matrices_folder out: - extracted_folder sc_rna_filter: doc: | Filters single-cell RNA-Seq datasets based on the common QC metrics run: ../tools/sc-rna-filter.cwl in: feature_bc_matrices_folder: uncompress_feature_bc_matrices/extracted_folder aggregation_metadata: aggregation_metadata grouping_data: grouping_data barcodes_data: barcodes_data rna_minimum_cells: rna_minimum_cells minimum_genes: minimum_genes maximum_genes: maximum_genes rna_minimum_umi: rna_minimum_umi minimum_novelty_score: minimum_novelty_score mito_pattern: mito_pattern maximum_mito_perc: maximum_mito_perc export_pdf_plots: default: false verbose: default: false export_h5seurat_data: default: false output_prefix: default: "step_1" parallel_memory_limit: parallel_memory_limit vector_memory_limit: vector_memory_limit threads: threads out: - raw_1_2_qc_mtrcs_pca_plot_png - raw_2_3_qc_mtrcs_pca_plot_png - raw_cells_count_plot_png - raw_umi_dnst_plot_png - raw_gene_dnst_plot_png - raw_gene_umi_corr_plot_png - raw_mito_dnst_plot_png - raw_nvlt_dnst_plot_png - raw_qc_mtrcs_dnst_plot_png - raw_umi_dnst_spl_cnd_plot_png - raw_gene_dnst_spl_cnd_plot_png - raw_mito_dnst_spl_cnd_plot_png - raw_nvlt_dnst_spl_cnd_plot_png - fltr_1_2_qc_mtrcs_pca_plot_png - fltr_2_3_qc_mtrcs_pca_plot_png - fltr_cells_count_plot_png - fltr_umi_dnst_plot_png - fltr_gene_dnst_plot_png - fltr_gene_umi_corr_plot_png - fltr_mito_dnst_plot_png - fltr_nvlt_dnst_plot_png - fltr_qc_mtrcs_dnst_plot_png - fltr_umi_dnst_spl_cnd_plot_png - fltr_gene_dnst_spl_cnd_plot_png - fltr_mito_dnst_spl_cnd_plot_png - fltr_nvlt_dnst_spl_cnd_plot_png - seurat_data_rds - stdout_log - stderr_log sc_rna_reduce: doc: | Integrates multiple single-cell RNA-Seq datasets, reduces dimensionality using PCA run: ../tools/sc-rna-reduce.cwl in: query_data_rds: sc_rna_filter/seurat_data_rds cell_cycle_data: cell_cycle_data normalization_method: normalization_method integration_method: integration_method highly_var_genes_count: highly_var_genes_count regress_mito_perc: regress_mito_perc regress_rna_umi: regress_rna_umi regress_genes: regress_genes regress_cellcycle: regress_cellcycle dimensions: dimensions umap_spread: umap_spread umap_mindist: umap_mindist umap_neighbors: umap_neighbors umap_metric: umap_metric umap_method: umap_method export_pdf_plots: default: false verbose: default: false export_h5seurat_data: default: false export_ucsc_cb: default: false low_memory: default: true output_prefix: default: "step_2" parallel_memory_limit: parallel_memory_limit vector_memory_limit: vector_memory_limit threads: threads out: - elbow_plot_png - qc_dim_corr_plot_png - umap_qc_mtrcs_plot_png - umap_spl_mito_plot_png - umap_spl_umi_plot_png - umap_spl_gene_plot_png - umap_gr_cnd_spl_ph_plot_png - umap_gr_cnd_spl_mito_plot_png - umap_gr_cnd_spl_umi_plot_png - umap_gr_cnd_spl_gene_plot_png - seurat_data_rds - stdout_log - stderr_log sc_rna_cluster: doc: | Clusters single-cell RNA-Seq datasets, identifies gene markers run: ../tools/sc-rna-cluster.cwl in: query_data_rds: sc_rna_reduce/seurat_data_rds dimensions: dimensions cluster_metric: cluster_metric resolution: resolution genes_of_interest: genes_of_interest identify_diff_genes: identify_diff_genes minimum_logfc: minimum_logfc minimum_pct: minimum_pct only_positive_diff_genes: only_positive_diff_genes test_to_use: test_to_use export_pdf_plots: default: false verbose: default: false export_h5seurat_data: default: false export_ucsc_cb: default: true output_prefix: default: "step_3" parallel_memory_limit: parallel_memory_limit vector_memory_limit: vector_memory_limit threads: threads out: - umap_res_plot_png - slh_res_plot_png - umap_spl_idnt_res_plot_png - cmp_gr_clst_spl_idnt_res_plot_png - cmp_gr_idnt_spl_clst_res_plot_png - umap_spl_cnd_res_plot_png - cmp_gr_clst_spl_cnd_res_plot_png - cmp_gr_cnd_spl_clst_res_plot_png - umap_spl_ph_res_plot_png - cmp_gr_ph_spl_idnt_res_plot_png - cmp_gr_ph_spl_clst_res_plot_png - xpr_avg_res_plot_png - xpr_per_cell_res_plot_png - xpr_dnst_res_plot_png - gene_markers_tsv - ucsc_cb_html_data - seurat_data_rds - stdout_log - stderr_log $namespaces: s: http://schema.org/ $schemas: - https://github.com/schemaorg/schemaorg/raw/main/data/releases/11.01/schemaorg-current-http.rdf label: "Single-cell RNA-Seq Analyze" s:name: "Single-cell RNA-Seq Analyze" s:alternateName: | Runs filtering, normalization, scaling, integration (optionally) and clustering for a single or aggregated single-cell RNA-Seq datasets s:downloadUrl: https://raw.githubusercontent.com/Barski-lab/scRNA-Seq-Analysis/main/workflows/sc-rna-analyze.cwl s:codeRepository: https://github.com/Barski-lab/scRNA-Seq-Analysis s:license: http://www.apache.org/licenses/LICENSE-2.0 s:isPartOf: class: s:CreativeWork s:name: Common Workflow Language s:url: http://commonwl.org/ s:creator: - class: s:Organization s:legalName: "Cincinnati Children's Hospital Medical Center" s:location: - class: s:PostalAddress s:addressCountry: "USA" s:addressLocality: "Cincinnati" s:addressRegion: "OH" s:postalCode: "45229" s:streetAddress: "3333 Burnet Ave" s:telephone: "+1(513)636-4200" s:logo: "https://www.cincinnatichildrens.org/-/media/cincinnati%20childrens/global%20shared/childrens-logo-new.png" s:department: - class: s:Organization s:legalName: "Allergy and Immunology" s:department: - class: s:Organization s:legalName: "Barski Research Lab" s:member: - class: s:Person s:name: Michael Kotliar s:email: mailto:misha.kotliar@gmail.com s:sameAs: - id: http://orcid.org/0000-0002-6486-3898 doc: | Single-cell RNA-Seq Analyze ==================================================================== Runs filtering, normalization, scaling, integration (optionally) and clustering for a single or aggregated single-cell RNA-Seq datasets.