/
seurat-cluster.cwl
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seurat-cluster.cwl
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cwlVersion: v1.0
class: Workflow
requirements:
- class: SubworkflowFeatureRequirement
- class: StepInputExpressionRequirement
- class: MultipleInputFeatureRequirement
- class: InlineJavascriptRequirement
expressionLib:
- var split_features = function(line) {
function get_unique(value, index, self) {
return self.indexOf(value) === index && value != "";
}
let splitted_line = line?line.split(/[\s,]+/).filter(get_unique):null;
return (splitted_line && !!splitted_line.length)?splitted_line:null;
};
- var split_numbers = function(line) {
let splitted_line = line?line.split(/[\s,]+/).map(parseFloat):null;
return (splitted_line && !!splitted_line.length)?splitted_line:null;
};
'sd:upstream':
sc_rnaseq_aggr_sample:
- "cellranger-aggr.cwl"
inputs:
alias:
type: string
label: "Experiment short name/Alias"
sd:preview:
position: 1
filtered_feature_bc_matrix_folder:
type: File
label: "scRNA-Seq Cellranger Aggregate Experiment"
doc: |
Compressed folder with aggregated filtered feature-barcode matrices in MEX format
'sd:upstreamSource': "sc_rnaseq_aggr_sample/filtered_feature_bc_matrix_folder"
'sd:localLabel': true
aggregation_metadata:
type: File
label: "scRNA-Seq Cellranger Aggregate Experiment"
doc: |
Aggregation metadata in CSV format
'sd:upstreamSource': "sc_rnaseq_aggr_sample/aggregation_metadata"
'sd:localLabel': true
minimum_cells:
type: int?
default: 5
label: "Include genes detected in at least this many cells"
doc: |
Include genes detected in at least this many cells
(applied to thoughout all datasets together).
'sd:layout':
advanced: true
minimum_features:
type: string?
default: "250"
label: "Include cells where at least this many genes are detected"
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.
'sd:layout':
advanced: true
maximum_features:
type: string?
default: "5000"
label: "Include cells with the number of genes not bigger than this value"
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.
'sd:layout':
advanced: true
minimum_umis:
type: string?
default: "500"
label: "Include cells where at least this many UMIs are detected"
doc: |
Include cells where at least this many UMIs are detected.
If multiple values provided each of them will be applied
to the correspondent dataset.
'sd:layout':
advanced: true
minimum_novelty_score:
type: string?
default: "0.8"
label: "Include cells with the novelty score (the ratio of genes per cell over UMIs per cell) not lower than this value"
doc: |
Include cells with the novelty score (the ratio of genes per cell over UMIs per cell)
not lower than this value (calculated as log10(genes)/log10(UMIs)). If multiple values
provided each of them will be applied to the correspondent dataset.
'sd:layout':
advanced: true
maximum_mito_perc:
type: float?
default: 5
label: "Include cells with the percentage of transcripts mapped to mitochondrial genes not bigger than this value"
doc: |
Include cells with the percentage of transcripts mapped to mitochondrial genes not bigger than this value.
'sd:layout':
advanced: true
mito_pattern:
type: string?
default: "^Mt-"
label: "Pattern to identify mitochondrial genes"
doc: |
Pattern to identify mitochondrial genes.
'sd:layout':
advanced: true
high_var_features_count:
type: int?
default: 3000
label: "Number of highly variable genes to detect (used for dataset integration and dimensional reduction)"
doc: |
Number of highly variable genes to detect (used for dataset integration and dimensional reduction).
'sd:layout':
advanced: true
dimensionality:
type: int?
default: 10
label: "Number of principal components to use in UMAP projection and clustering (from 1 to 50)"
doc: |
Number of principal components to use in UMAP projection and clustering (from 1 to 50).
Use Elbow plot to adjust this parameter.
'sd:layout':
advanced: true
umap_spread:
type: float?
default: 1
label: "Effective scale of embedded points on UMAP. Determines how clustered/clumped the embedded points are."
doc: |
The effective scale of embedded points on UMAP. In combination with mindist
this determines how clustered/clumped the embedded points are.
'sd:layout':
advanced: true
umap_mindist:
type: float?
default: 0.3
label: "Controls how tightly the embedding is allowed compress points together on UMAP. Sensible values are in the range 0.001 to 0.5"
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.
'sd:layout':
advanced: true
umap_nneighbors:
type: int?
default: 30
label: "Number of neighboring points used in UMAP. Larger values result in loss of detailed local structure."
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.
'sd:layout':
advanced: true
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"
default: "cosine"
label: "The metric to use to compute distances in high dimensional space for UMAP"
doc: |
The metric to use to compute distances in high dimensional space for UMAP.
'sd:layout':
advanced: true
umap_method:
type:
- "null"
- type: enum
symbols:
- "uwot"
- "uwot-learn"
- "umap-learn"
default: "uwot"
label: "UMAP implementation to run"
doc: |
UMAP implementation to run.
'sd:layout':
advanced: true
cluster_metric:
type:
- "null"
- type: enum
symbols:
- "euclidean"
- "cosine"
- "manhattan"
- "hamming"
default: "euclidean"
label: "Distance metric used by the nearest neighbors algorithm when running clustering"
doc: |
Distance metric used by the nearest neighbors algorithm when running clustering.
'sd:layout':
advanced: true
resolution:
type: string?
default: "0.1"
label: "Comma or space separated list of clustering resolutions"
doc: |
Comma or space separated list of clustering resolutions
'sd:layout':
advanced: true
minimum_logfc:
type: float?
default: 0.25
label: "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"
doc: |
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.
'sd:layout':
advanced: true
minimum_pct:
type: float?
default: 0.1
label: "Include only those genes that are detected in not lower than this fraction of cells in either of the two tested clusters"
doc: |
Include only those genes that are detected in not lower than
this fraction of cells in either of the two tested clusters.
'sd:layout':
advanced: true
test_use:
type:
- "null"
- type: enum
symbols:
- "wilcox"
- "bimod"
- "roc"
- "t"
- "negbinom"
- "poisson"
- "LR"
- "MAST"
- "DESeq2"
default: "wilcox"
label: "Statistical test to use for gene markers identification"
doc: |
Statistical test to use for gene markers identification.
'sd:layout':
advanced: true
threads:
type: int?
default: 6
label: "Threads number to use"
doc: |
Threads number
'sd:layout':
advanced: true
species:
type:
- "null"
- type: enum
symbols:
- "hs"
- "mm"
- "none"
default: "none"
label: "Species for gene name conversion when running cell type prediction"
doc: |
Select species for gene name conversion when running cell type prediction
with Garnett classifier.
If "none" - do not convert gene names
'sd:layout':
advanced: true
regress_cellcycle:
type: boolean?
default: false
label: "Regress cell cycle as a confounding source of variation"
doc: |
Regress cell cycle as a confounding source of variation.
'sd:layout':
advanced: true
regress_mito_perc:
type: boolean?
default: false
label: "Regress mitochondrial gene expression as a confounding source of variation"
doc: |
Regress mitochondrial gene expression as a confounding source
of variation.
'sd:layout':
advanced: true
only_positive_markers:
type: boolean?
default: false
label: "Report only positive gene markers"
doc: |
Report only positive gene markers.
'sd:layout':
advanced: true
no_sct:
type: boolean?
default: false
label: "Use LogNormalize instead of SCTransform when integrating datasets"
doc: |
Do not use SCTransform when running datasets integration. Use LogNormalize instead.
'sd:layout':
advanced: true
selected_features:
type: string?
default: null
label: "Comma or space separated list of genes of interest"
doc: |
Comma or space separated list of genes of interest.
Default: do not highlight any features
'sd:layout':
advanced: true
conditions_data:
type: File?
label: "TSV/CSV file to define datasets conditions with 'library_id' and 'condition' columns. Rows order should correspond to the aggregation metadata."
doc: |
Path to the TSV/CSV file to define datasets grouping. First column -
'library_id' with the values provided in the same order as in the
correspondent column of the --identity file, second column 'condition'.
If not provided, each dataset is assigned to its own
biological condition
barcodes_data:
type: File?
label: "Headerless TSV/CSV file with cell barcodes (one barcode per line) to prefilter input data"
doc: |
Path to the headerless TSV/CSV file with selected barcodes
(one per line) to prefilter input feature-barcode matrices.
If not provided, use all cells
'sd:layout':
advanced: true
cell_cycle_data:
type: File?
label: "TSV/CSV file with cell cycle data with 'phase' and 'gene_id' columns"
doc: |
TSV/CSV file with cell cycle data. First column - 'phase', second column 'gene_id'.
If not provided, skip cell cycle score assignment
'sd:layout':
advanced: true
classifier_rds:
type: File?
label: "Garnett classifier rds file for cell type prediction"
doc: |
Path to the Garnett classifier rds file for cell type prediction.
If not provided, skip cell type prediction
'sd:layout':
advanced: true
outputs:
raw_cell_count_plot_png:
type: File?
outputSource: seurat_cluster/raw_cell_count_plot_png
label: "Number of cells per dataset (not filtered)"
doc: |
Number of cells per dataset (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Number of cells per dataset (not filtered)'
raw_cell_count_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_cell_count_plot_pdf
label: "Number of cells per dataset (not filtered)"
doc: |
Number of cells per dataset (not filtered).
PDF format
raw_umi_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/raw_umi_dnst_spl_by_cond_plot_png
label: "Split by condition UMI density per cell (not filtered)"
doc: |
Split by condition UMI density per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Split by condition UMI density per cell (not filtered)'
raw_umi_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_umi_dnst_spl_by_cond_plot_pdf
label: "Split by condition UMI density per cell (not filtered)"
doc: |
Split by condition UMI density per cell (not filtered).
PDF format
raw_gene_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/raw_gene_dnst_spl_by_cond_plot_png
label: "Split by condition gene density per cell (not filtered)"
doc: |
Split by condition gene density per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Split by condition gene density per cell (not filtered)'
raw_gene_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_gene_dnst_spl_by_cond_plot_pdf
label: "Split by condition gene density per cell (not filtered)"
doc: |
Split by condition gene density per cell (not filtered).
PDF format
raw_gene_umi_corr_spl_by_ident_plot_png:
type: File?
outputSource: seurat_cluster/raw_gene_umi_corr_spl_by_ident_plot_png
label: "Split by identity genes vs UMIs per cell correlation (not filtered)"
doc: |
Split by identity genes vs UMIs per cell correlation (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Split by identity genes vs UMIs per cell correlation (not filtered)'
raw_gene_umi_corr_spl_by_ident_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_gene_umi_corr_spl_by_ident_plot_pdf
label: "Split by identity genes vs UMIs per cell correlation (not filtered)"
doc: |
Split by identity genes vs UMIs per cell correlation (not filtered).
PDF format
raw_mito_perc_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/raw_mito_perc_dnst_spl_by_cond_plot_png
label: "Split by condition density of transcripts mapped to mitochondrial genes per cell (not filtered)"
doc: |
Split by condition density of transcripts mapped to mitochondrial genes per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Split by condition density of transcripts mapped to mitochondrial genes per cell (not filtered)'
raw_mito_perc_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_mito_perc_dnst_spl_by_cond_plot_pdf
label: "Split by condition density of transcripts mapped to mitochondrial genes per cell (not filtered)"
doc: |
Split by condition density of transcripts mapped to mitochondrial genes per cell (not filtered).
PDF format
raw_nvlt_score_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/raw_nvlt_score_dnst_spl_by_cond_plot_png
label: "Split by condition novelty score density per cell (not filtered)"
doc: |
Split by condition novelty score density per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Split by condition novelty score density per cell (not filtered)'
raw_nvlt_score_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_nvlt_score_dnst_spl_by_cond_plot_pdf
label: "Split by condition novelty score density per cell (not filtered)"
doc: |
Split by condition novelty score density per cell (not filtered).
PDF format
raw_qc_mtrcs_plot_png:
type: File?
outputSource: seurat_cluster/raw_qc_mtrcs_plot_png
label: "QC metrics densities per cell (not filtered)"
doc: |
QC metrics densities per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'QC metrics densities per cell (not filtered)'
raw_qc_mtrcs_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_qc_mtrcs_plot_pdf
label: "QC metrics densities per cell (not filtered)"
doc: |
QC metrics densities per cell (not filtered).
PDF format
raw_qc_mtrcs_gr_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/raw_qc_mtrcs_gr_by_cond_plot_png
label: "Grouped by condition QC metrics densities per cell (not filtered)"
doc: |
Grouped by condition QC metrics densities per cell (not filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (not filtered)'
Caption: 'Grouped by condition QC metrics densities per cell (not filtered)'
raw_qc_mtrcs_gr_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/raw_qc_mtrcs_gr_by_cond_plot_pdf
label: "Grouped by condition QC metrics densities per cell (not filtered)"
doc: |
Grouped by condition QC metrics densities per cell (not filtered).
PDF format
fltr_cell_count_plot_png:
type: File?
outputSource: seurat_cluster/fltr_cell_count_plot_png
label: "Number of cells per dataset (filtered)"
doc: |
Number of cells per dataset (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Number of cells per dataset (filtered)'
fltr_cell_count_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_cell_count_plot_pdf
label: "Number of cells per dataset (filtered)"
doc: |
Number of cells per dataset (filtered).
PDF format
fltr_umi_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/fltr_umi_dnst_spl_by_cond_plot_png
label: "Split by condition UMI density per cell (filtered)"
doc: |
Split by condition UMI density per cell (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by condition UMI density per cell (filtered)'
fltr_umi_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_umi_dnst_spl_by_cond_plot_pdf
label: "Split by condition UMI density per cell (filtered)"
doc: |
Split by condition UMI density per cell (filtered).
PDF format
fltr_gene_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/fltr_gene_dnst_spl_by_cond_plot_png
label: "Split by condition gene density per cell (filtered)"
doc: |
Split by condition gene density per cell (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by condition gene density per cell (filtered)'
fltr_gene_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_gene_dnst_spl_by_cond_plot_pdf
label: "Split by condition gene density per cell (filtered)"
doc: |
Split by condition gene density per cell (filtered).
PDF format
fltr_gene_umi_corr_spl_by_ident_plot_png:
type: File?
outputSource: seurat_cluster/fltr_gene_umi_corr_spl_by_ident_plot_png
label: "Split by identity genes vs UMIs per cell correlation (filtered)"
doc: |
Split by identity genes vs UMIs per cell correlation (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by identity genes vs UMIs per cell correlation (filtered)'
fltr_gene_umi_corr_spl_by_ident_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_gene_umi_corr_spl_by_ident_plot_pdf
label: "Split by identity genes vs UMIs per cell correlation (filtered)"
doc: |
Split by identity genes vs UMIs per cell correlation (filtered).
PDF format
fltr_mito_perc_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/fltr_mito_perc_dnst_spl_by_cond_plot_png
label: "Split by condition density of transcripts mapped to mitochondrial genes per cell (filtered)"
doc: |
Split by condition density of transcripts mapped to mitochondrial genes per cell (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by condition density of transcripts mapped to mitochondrial genes per cell (filtered)'
fltr_mito_perc_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_mito_perc_dnst_spl_by_cond_plot_pdf
label: "Split by condition density of transcripts mapped to mitochondrial genes per cell (filtered)"
doc: |
Split by condition density of transcripts mapped to mitochondrial genes per cell (filtered).
PDF format
fltr_nvlt_score_dnst_spl_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/fltr_nvlt_score_dnst_spl_by_cond_plot_png
label: "Split by condition novelty score density per cell (filtered)"
doc: |
Split by condition novelty score density per cell (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by condition novelty score density per cell (filtered)'
fltr_nvlt_score_dnst_spl_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_nvlt_score_dnst_spl_by_cond_plot_pdf
label: "Split by condition novelty score density per cell (filtered)"
doc: |
Split by condition novelty score density per cell (filtered).
PDF format
fltr_qc_mtrcs_plot_png:
type: File?
outputSource: seurat_cluster/fltr_qc_mtrcs_plot_png
label: "QC metrics densities per cell (filtered)"
doc: |
QC metrics densities per cell (filtered).
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'QC metrics densities per cell (filtered)'
fltr_qc_mtrcs_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_qc_mtrcs_plot_pdf
label: "QC metrics densities per cell (filtered)"
doc: |
QC metrics densities per cell (filtered).
PDF format
fltr_qc_mtrcs_gr_by_cond_plot_png:
type: File?
outputSource: seurat_cluster/fltr_qc_mtrcs_gr_by_cond_plot_png
label: "Grouped by condition QC metrics densities per cell (filtered)"
doc: |
Grouped by condition QC metrics densities per cell (filtered).
PDF format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Grouped by condition QC metrics densities per cell (filtered)'
fltr_qc_mtrcs_gr_by_cond_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_qc_mtrcs_gr_by_cond_plot_pdf
label: "Grouped by condition QC metrics densities per cell (filtered)"
doc: |
Grouped by condition QC metrics densities per cell (filtered).
PDF format
fltr_pca_spl_by_ph_plot_png:
type: File?
outputSource: seurat_cluster/fltr_pca_spl_by_ph_plot_png
label: "Split by cell cycle phase PCA of filtered unintegrated/scaled datasets"
doc: |
Split by cell cycle phase PCA of filtered unintegrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by cell cycle phase PCA of filtered unintegrated/scaled datasets'
fltr_pca_spl_by_ph_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_pca_spl_by_ph_plot_pdf
label: "Split by cell cycle phase PCA of filtered unintegrated/scaled datasets"
doc: |
Split by cell cycle phase PCA of filtered unintegrated/scaled datasets.
PDF format
fltr_pca_spl_by_mito_perc_plot_png:
type: File?
outputSource: seurat_cluster/fltr_pca_spl_by_mito_perc_plot_png
label: "Split by level of transcripts mapped to mitochondrial genes PCA of filtered unintegrated/scaled datasets"
doc: |
Split by level of transcripts mapped to mitochondrial genes PCA of filtered unintegrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by level of transcripts mapped to mitochondrial genes PCA of filtered unintegrated/scaled datasets'
fltr_pca_spl_by_mito_perc_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_pca_spl_by_mito_perc_plot_pdf
label: "Split by level of transcripts mapped to mitochondrial genes PCA of filtered unintegrated/scaled datasets"
doc: |
Split by level of transcripts mapped to mitochondrial genes PCA of filtered unintegrated/scaled datasets.
PDF format
fltr_umap_spl_by_idnt_plot_png:
type: File?
outputSource: seurat_cluster/fltr_umap_spl_by_idnt_plot_png
label: "Split by identity UMAP projected PCA of filtered unintegrated/scaled datasets"
doc: |
Split by identity UMAP projected PCA of filtered unintegrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (filtered)'
Caption: 'Split by identity UMAP projected PCA of filtered unintegrated/scaled datasets'
fltr_umap_spl_by_idnt_plot_pdf:
type: File?
outputSource: seurat_cluster/fltr_umap_spl_by_idnt_plot_pdf
label: "Split by identity UMAP projected PCA of filtered unintegrated/scaled datasets"
doc: |
Split by identity UMAP projected PCA of filtered unintegrated/scaled datasets.
PDF format
ntgr_elbow_plot_png:
type: File?
outputSource: seurat_cluster/ntgr_elbow_plot_png
label: "Elbow plot from PCA of filtered integrated/scaled datasets"
doc: |
Elbow plot from PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Dimensionality evaluation'
Caption: 'Elbow plot from PCA of filtered integrated/scaled datasets'
ntgr_elbow_plot_pdf:
type: File?
outputSource: seurat_cluster/ntgr_elbow_plot_pdf
label: "Elbow plot from PCA of filtered integrated/scaled datasets"
doc: |
Elbow plot from PCA of filtered integrated/scaled datasets.
PDF format
ntgr_pca_plot_png:
type: File?
outputSource: seurat_cluster/ntgr_pca_plot_png
label: "PCA of filtered integrated/scaled datasets"
doc: |
PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Dimensionality evaluation'
Caption: 'PCA of filtered integrated/scaled datasets'
ntgr_pca_plot_pdf:
type: File?
outputSource: seurat_cluster/ntgr_pca_plot_pdf
label: "PCA of filtered integrated/scaled datasets"
doc: |
PCA of filtered integrated/scaled datasets.
PDF format
ntgr_pca_heatmap_png:
type: File?
outputSource: seurat_cluster/ntgr_pca_heatmap_png
label: "Genes per cells expression heatmap sorted by their PC scores from PCA of filtered integrated/scaled datasets"
doc: |
Genes per cells expression heatmap sorted by their PC scores from PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Dimensionality evaluation'
Caption: 'Genes per cells expression heatmap sorted by their PC scores from PCA of filtered integrated/scaled datasets'
ntgr_pca_heatmap_pdf:
type: File?
outputSource: seurat_cluster/ntgr_pca_heatmap_pdf
label: "Genes per cells expression heatmap sorted by their PC scores from PCA of filtered integrated/scaled datasets"
doc: |
Genes per cells expression heatmap sorted by their PC scores from PCA of filtered integrated/scaled datasets.
PDF format
ntgr_pca_loadings_plot_png:
type: File?
outputSource: seurat_cluster/ntgr_pca_loadings_plot_png
label: "PC scores of the most variant genes from PCA of filtered integrated/scaled datasets"
doc: |
PC scores of the most variant genes from PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Dimensionality evaluation'
Caption: 'PC scores of the most variant genes from PCA of filtered integrated/scaled datasets'
ntgr_pca_loadings_plot_pdf:
type: File?
outputSource: seurat_cluster/ntgr_pca_loadings_plot_pdf
label: "PC scores of the most variant genes from PCA of filtered integrated/scaled datasets"
doc: |
PC scores of the most variant genes from PCA of filtered integrated/scaled datasets.
PDF format
ntgr_umap_spl_by_idnt_plot_png:
type: File?
outputSource: seurat_cluster/ntgr_umap_spl_by_idnt_plot_png
label: "Split by identity UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Split by identity UMAP projected PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'QC (integrated/scaled)'
Caption: 'Split by identity UMAP projected PCA of filtered integrated/scaled datasets'
ntgr_umap_spl_by_idnt_plot_pdf:
type: File?
outputSource: seurat_cluster/ntgr_umap_spl_by_idnt_plot_pdf
label: "Split by identity UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Split by identity UMAP projected PCA of filtered integrated/scaled datasets.
PDF format
clst_umap_res_plot_png:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_res_plot_png
label: "Clustered UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Clustered UMAP projected PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Clustering'
Caption: 'Clustered UMAP projected PCA of filtered integrated/scaled datasets'
clst_umap_res_plot_pdf:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_res_plot_pdf
label: "Clustered UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Clustered UMAP projected PCA of filtered integrated/scaled datasets.
PDF format
clst_umap_spl_by_cond_res_plot_png:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_spl_by_cond_res_plot_png
label: "Split by condition clustered UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Split by condition clustered UMAP projected PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Clustering'
Caption: 'Split by condition clustered UMAP projected PCA of filtered integrated/scaled datasets'
clst_umap_spl_by_cond_res_plot_pdf:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_spl_by_cond_res_plot_pdf
label: "Split by condition clustered UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Split by condition clustered UMAP projected PCA of filtered integrated/scaled datasets.
PDF format
clst_umap_ctype_res_plot_png:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_ctype_res_plot_png
label: "Grouped by predicted cell types UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Grouped by predicted cell types UMAP projected PCA of filtered integrated/scaled datasets.
PNG format
'sd:visualPlugins':
- image:
tab: 'Clustering'
Caption: 'Grouped by predicted cell types UMAP projected PCA of filtered integrated/scaled datasets'
clst_umap_ctype_res_plot_pdf:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_ctype_res_plot_pdf
label: "Grouped by predicted cell types UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Grouped by predicted cell types UMAP projected PCA of filtered integrated/scaled datasets.
PDF format
clst_umap_spl_by_ph_res_plot_png:
type:
- "null"
- type: array
items: File
outputSource: seurat_cluster/clst_umap_spl_by_ph_res_plot_png
label: "Split by cell cycle phase clustered UMAP projected PCA of filtered integrated/scaled datasets"
doc: |
Split by cell cycle phase clustered UMAP projected PCA of filtered integrated/scaled datasets.