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Config Files (Parameter Files)
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Cellsnake can be configured using a config file. The config file is in YAML format.

The use can specify the config file to use with the **--configfile** option. If no config file is specified, cellsnake will use the default parameters.

It is recommended to use the default config file as a template and modify it to your needs. You can download the default one here: https://raw.githubusercontent.com/sinanugur/cellsnake/main/config.yaml


.. code-block:: bash

    #example usage with a config file
    cellsnake standard data --configfile config.yaml



The default config file looks like this. This is modified for human samples. 

.. code-block:: bash

    #These are also default settings of a Cellsnake run without any configfile
    #basic paramaters
    min_cells: 3 #seurat defaults, recommended
    min_features: 200 #seurat defaults, recommended, nFeature_RNA
    max_features: Inf #seurat default, nFeature_RNA, 5000 can be a good cutoff
    max_molecules: Inf #seurat default, nCount_RNA, to filter potential doublets, doublet filtering is already default, so keep this Inf
    min_molecules: 0 #seurat default, nCount_RNA, min_features usually handles this so keep it 0


    percent_mt: 10 #Mitochondrial genes maximum percentage (0-100), default 10, you can use "auto"
    percent_rp: 0 #Ribosomal genes minimum percentage (0-100), default no filtering
    highly_variable_features: 2000 #seurat defaults, recommended
    variable_selection_method: "vst" #seurat defaults, recommended

    #plotting paramaters
    min_percentage_to_plot: 2 #only plot clusters with more than 2% of cells, still all of them visible on HTMLs and on plots if labels are True
    show_labels: T #show labels on plots

    #doublet filtering, True:T or False:F
    doublet_filter: T #this may fail on some samples

    #clustering and normalization paramaters
    normalization_method: "LogNormalize"
    scale_factor: 10000
    resolution: "0.8" #seurat default

    #Differential expression paramaters
    logfc_threshold: 0.25
    test_use: "wilcox"
    marker_plots_per_cluster_n: 20 #plot summary marker plots for top markers
    umap_markers_plot: True
    tsne_markers_plot: False

    #enrichment paramaters
    mapping: "org.Hs.eg.db" #you may install others from Bioconductor, this is for human
    organism: "hsa" #alternatives https://www.genome.jp/kegg/catalog/org_list.html

    #SingleR reference
    singler_ref: "BlueprintEncodeData" # https://bioconductor.org/packages/release/data/experiment/vignettes/celldex/inst/doc/userguide.html#1_Overview
    singler_granulation: "label.main" #label.main or label.fine

    #celltypist
    celltypist_model: "Immune_All_Low.pkl" #refer to Celltypist for another model 

    #krakendb settings
    kraken_db_folder:
    taxa: "genus" # available options "domain", "kingdom", "phylum", "class", "order", "family", "genus", "species"
    microbiome_min_cells: 1
    microbiome_min_features: 3
    confidence: 0.05 #see kraken2 manual
    min_hit_groups: 4 #see kraken2 manual

    #cellchat
    species: "human" #human or mouse is accepted, http://www.cellchat.org/cellchatdb/

    #integration params
    dims: 30
    reduction: "cca"


Changing the organism to mouse
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You can change the organism and mapping to use it for other species.

For example, for mouse you can change these parameters:

.. code-block:: bash

    species: "mouse"
    mapping: "org.Mm.eg.db"
    organism: "mmu"
    singler_ref: "MouseRNAseqData"