/
sc-rna-analyze-wf.cwl
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sc-rna-analyze-wf.cwl
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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/