3. Example parameters

params:
  method: 
    label: "method -- experimental method"
    choices: ['384plate', 'droplet']
    value: '384plate'
  kb.dir: 
    label: "kb.dir -- directory containing kb-python output"
    value: /home/user/experiment/results/kallistobus/
  barcode_file: 
    label: "barcode_file -- .tab delimited file with 2-col layout for plate based methods (with DNA barcode and well-id)"
    value: /home/user/experiment/barcode_384.tab
  mt_genes_file:
    label: "mt_genes_file -- .txt file containing one column with mitchondrial genes"
    value: /home/user/experiment/genomes/genome_addons/MT_genes.txt
  meta_data:
    label: "meta_data -- .csv file containing cell meta data. Alternative for extract_meta_columns"
    value: ../data/examples/sample_meta_example.csv
  meta_type:
      label: "meta_data_level -- type of meta data"
      choices: ["sample", "cell","names","default"]
      value: sample      
  extract_meta_columns: 
    label: "extract_meta_columns -- extracts meta data from cell names (name fields separated by '_'). Alternative for meta_data."
    value:  ""
  resultsdir:
     label: "resultsdir -- output directory for qc files" 
     value: ../output
  meta_group_id: 
      label: "meta_group_id -- combine meta data fields for plotting, creates variable combined_id (separate multiple values by comma)" 
      value: ""
  lab_col: 
      label: "lab_col -- meta data field used for QC plot labels (for instance: combined_id)"
      value: library
  umap_cols: 
      label: "umap_cols -- meta data fields used for Seurat's RunUMAP plot labels (separate multiple values by comma)"
      value: library,timepoint,combined_id
  confounders_to_test: 
      label: "confounders_to_test -- meta data variables used for confounder testing (separate multiple values by comma)"
      value: library
  isvelo:
      label: "is_velo -- velocity workflow"
      value: true
  run.sct:
      label: "run.sct -- perform SCTransform normalization, FALSE runs log(p1) NormalizeData, HVG selection and ScaleData in Seurat"
      value: true
  run.jackstraw:
       label: "run.jackstraw -- perform Jackstraw analysis (does not work with SCTransform)"
       value: false    
  add.spikes.ercc: 
      label: "add.spikes.ercc -- use ERCC RNA spike-ins (if present)"
      value: true
  ercc_pct_max: 
      label: "ercc_pct_max -- max percentage ERCC spike-ins counts"
      value: 20
  add.spikes.mt: 
      label: " add.spikes.mt -- use mitochondrial gene-list for qc"
      value: true
  mt_pct_max: 
      label: "mt_pct_max -- max percentage mitochondrial genes counts"
      value: 50
  gene_tresh: 
      label: "gene_tresh -- gene filter: threshold for genes considered expressed"
      value: 1
  amount_cells_expr: 
      label: "amount_cells_expr -- gene filter: threshold for minimal amount of cells a gene should be expressed in"
      value: 2
  total_counts_tresh: 
      label: "total_counts_tresh -- cell filter: threshold for minimal amount of UMI counts detected in a cell"
      value: 1000
  total_feat_tresh: 
      label: "total_feat_tresh -- cell filter: threshold for minimal amount of features (genes) detected in a cell"
      value: 500
  nhvg: 
      label: "nhvg -- number of Highly Variable Genes, used in Seurat's FindVariableFeatures"
      value: 2000
  cell_id_filter_option: 
      label: "cell_id_subset_filter -- filter options for cell_id_subset" 
      choices: ['in', 'out', 'none']
      value: in   
  cell_id_filter_pattern: 
      label: "cell_id_subset -- cell ids to include/exclude based on cell_id_subset_filter"
      value: "EHT"  
  pcs_for_overview:
      label: "pcs_for_overview -- principal components for overview in combined umap plot, runs UMAP for PC 1-value"
      value: '5,10,13,20,30,40'
  pcs_max_hvg: 
      label: "pcs_max_hvg -- max number of principal components to visualize"
      value: 70
  vars_to_regress_sf:
      label: "vars_to_regress -- variables to regress out (spliced, for example: nCount_sf,nFeature_sf)"
      value:  nCount_sf,nFeature_sf
  vars_to_regress_uf:
      label: "vars_to_regress -- variables to regress out (unspliced, for example: nCount_uf,nFeature_uf))"
      value:  nCount_uf,nFeature_uf
  old_col_pattern: 
      label: "old_col_pattern -- old column substring to replace by new_column_pattern"
      value: ""
  new_col_pattern:
      label: "new_col_pattern -- new column substring to replace old_column_pattern"
      value: ""