config.yaml

#-------------------------------------
# Path to sample sheet
#-------------------------------------

sample: sample.tsv

#-------------------------------------
# Config
#-------------------------------------

# If you run the workflow with slurm (sbatch run_slurm.sh) = true
run_slurm: false
# If you data are demultiplexed = true
run_demultiplex: false
# If your data are not demultiplexed = true
run_multiplex: true

rules:
    # Optimizing and assembling genome annotations for 3’ single-cell RNA-sequencing analysis
    reference_enhancer_rule: "FALSE"
    # Build the reference with cellranger
    ref_cellranger_rule: "FALSE"
    # Run SIMS to anotate cells
    sims_rule: "TRUE"

run:
    # FASTQ file naming convention : [Sample name]_S1_L00[Lane Number]_[Read Type]_001.fastq.gz
    
    # Can be a list if pair-end : "R1,R2" or "1,2" or "R1_001,R2_001"
    types: "1,2"
    # Library if multiplexed 
    library: "1_A_GEX,2_A_CMO"

fastq:
    # pair-end or not (R1,R2 or 1,2 etc...)
    pair: "R1,R2"
    # Bioproject
    biop: "SRP215051_SRP287614"
    # Sequence Read Archive (SRA)
    sra: "SRP287614"
    # Sample name ; Multiple names may be supplied as a comma-separated list, in which case they will be treated as one sample.
    sname: "72h,80h,86h,96h"
    # Project name
    nproject: "SRP215051_SRP287614"

# Genome and gff link to create the pre-built Cell Ranger reference
reference:
    link_ref_fasta: "https://ftp.ensembl.org/pub/release-98/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz"
    ref_fasta: "Mus_musculus.GRCm38.dna.primary_assembly.fa"
    link_ref_gtf: "https://storage.googleapis.com/generecovery/mouse_mm10_optimized_annotation_v2.gtf.gz"
    ref_gtf: "Mus_musculus.GRCm39.109.gtf"
    # Name for the file of the cellranger reference
    ref_cellranger: "refdata-cellranger-mm10"
    # Optional reference version string to include with
    ref_version: "1.0.0"

    # link_ref_fasta: "https://ftp.ensembl.org/pub/release-98/fasta/mus_musculus/dna/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz"
    # link_ref_gtf: "https://storage.googleapis.com/generecovery/mouse_mm10_optimized_annotation_v2.gtf.gz"
    # ref_cellranger: "refdata-cellranger-mm10"
    #ref_fasta: "Mus_musculus.GRCm38.dna.primary_assembly.fa"

# Parameter for cellranger multi
multi:
    # A unique run ID string: e.g. sample345 that is also the output folder name. Cannot be more than 64 characters.
    id: "S004647_to_S004650"
    # Path to config CSV file with input libraries and analysis parameters.
    config: "multi_config.csv"

# parameter for cellranger count
cellranger:
    # Path of the fastq_path folder. Can take multiple comma-separated paths, which is helpful if the same library was sequenced on multiple flowcells. 
    fastqs: "00_RawData"
    # Expected number of recovered cells. Default: 3,000 cells.
    expect_cells: 5000
    # Restricts cellranger to use specified number of cores to execute pipeline stages
    localcores: 6
    # Restricts cellranger to use specified amount of memory (in GB) to execute pipeline stages.
    localmem: 50
    # Sample to be aggregated by cell ranger
    sagrr: "M12,M15,M27,Normal_Melanocytes,Mix_MM_lines"
    # Aggregating libraries with different chemistry versions. Need to be in the same order than sagrr.
    batch: "v3_lib,v3_lib,v3_lib,v3_lib,v3_lib"
    # Assign categories to individual samples. Need to be in the same order than sagrr.
    categorie: "melanoma,melanoma,melanoma,normal,melanoma"
    # Depth normalization for aggregate ("none" or "mapped"). The none option may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization in a downstream step.
    normagrr: "none"
    demuxlet:
    # Tell if multiple samples are pooled by barcoded single cell sequencing and need deconvolute sample identity and identify multiplets.
    spooled: "TRUE"

# Seurat parameters
seurat:
    ##### General
    # Number of cells above which use ggplot instead of interactive plotly
    plot_raster_nbcells_threshold : 10000
    # CellRanger count path for each sample for Read10X
    cell_ranger_count_path: "/count/sample_filtered_feature_bc_matrix/"

    ##### Filtering 
    # Switch from QC exploration mode and QC filtering
    # In exploration mode, the cells that would be filtered by QC threshold are not filtered but marked
    # They are shown in the later analysis with a different color
    # "TRUE" : Exploration mode, "FALSE" : Not in exploration mode
    qc_exploration_mode: "FALSE"
    # Include features detected in at least this many cells. Will subset the counts matrix as well.
    min_cells: 3
    # Include cells where at least this many features are detected.
    min_features: 200
    # Cells with number of UMIs outside the range will be excluded
    # Can be a list of UMIs but MUST be on the same order than sample.tsv id.
    filter_umi_min: "0,0,0,0"
    filter_umi_max: "55000,42000,42000,40000"
    # Filter cells that have unique feature counts over/less n
    # Can be a list of feature counts but MUST be on the same order than sample.tsv id.
    filter_feature_min: "1700,1700,1400,1400"
    filter_feature_max: "7900,7500,7700,7200"
    # Filter cells that have > n% mitochondrial counts
    filter_percent_mt: "10,10,8,8"
    # Filter cells that have < n% mitochondrial counts
    filter_percent_mt_min: "0.2,0.2,0.2,0.2"
    # Filter cells that have < n% ribosomal counts
    filter_percent_rb: "0,0,0,0"
    # Pattern to match features against => mitochondrial genes | for mouse : "^mt-" / for human : "^MT-"
    pattern_mt: "^mt-"
    # Pattern to match features against => ribosomal genes
    pattern_rb: "^rps|^rpl"

    ##### Normalization
    # Normalization parameters (see Seurat::NormalizeData())
    norm_method: "LogNormalize"
    norm_scale_factor: 10000

    ##### Find the most variable genes
    # How to choose top variable features => vst, mean.var.plot or dispersion
    feature_select_method: "vst"
    # Number of features to select as top variable features; only used when select_method is set to 'dispersion' or 'vst'
    variable_features: 2000
    # For table in report
    variable_features_showtop : 200

    ##### PCA parameters
    # Number of dimention to show for the vizualisation
    dims: 30
    # Default number of dimensions to use for PCA (see Seurat::RunPCA())
    pca_npc: 22
    # Number of dimensions to show in PCA-related plots
    pca_plots_nbdims : 3
    #  Number of'top' features to show when plotting PCA loadings
    pca_plot_nbfeatures : 3

    ##### Dimensionality reduction parameters (TSNE/UMAP)
    # Number of dimensions to use from PCA results
    dimreduc_use_pca_nbdims : 22

    ##### Cluster identification parameters
    # Number of dimensions to use from PCA results
    findclusters_use_pca_nbdims : 22
    # Nearest-neighbor graph construction
    findneighbors_k : 30
    # Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
    findclusters_resolution : 1
    # Algorithm for modularity optimization
    # 1 = Louvain; 2 = Louvain with multilevel refinement; 3 = SLM; 4 = Leiden
    findclusters_algorithm : 1

rm_doublets:
    # If you want to use the scran normalisation : scran or "" if seurat
    normalisation : "scran"

# Parameters to build correct reference(s) for sims
reference_sims:
    ##### General #####
    #  Path of the script you want to execute (Corespond to the reference you want to use)
    # For now : arlotta.R, allen_mouse_whole_cortex.R, mousegastrulation.R
    r_script: "mousegastrulation.R"
    # Metadata of the reference matrix
    output_name_ref_metadata: "metadata"
    # Reference matrix
    output_name_ref_matrix: "mousegastrulation"
    # Reference matrix with metadata
    output_name_ref_matrix_metadata: "mousegastrulation_join_metadata"

    output_name_matrix: "data_matrix"
    # Name of the column that contain the cells
    # allen mouse whole cortex : "sample_name", arlotta : "NEW_NAME"
    cells_column: "Cell"

    ##### Our Matrix to annotate #####
    # The name of the folder of the sample you want to annotate (Folder in the Seurat folder)
    sample_id: "96h"

    ##### Paola Arlotta #####
    # Path for the arlotta metadata
    arlotta_metadata: "arlotta/metaData_scDevSC.txt"
    #
    arlotta_matrix: "arlotta/gene_sorted-matrix.mtx.gz"
    #
    arlotta_cells: "arlotta/barcodes.tsv"
    #
    arlotta_features: "arlotta/genes.tsv"
    # Pattern that correspond to the mouse age that we want to keep for the training of our data
    # It's possible to select multiple ones if separate by a coma
    # In this reference : E10, E11, E12, E13, E14, E15, E16, E17, E18, P1, P4
    pattern_to_keep: "P4"

    ##### Allen_mouse_whole_cortex #####
    #
    allen_metadata: "allen/"
    #
    allen_matrix: "allen/"
    #
    allen_genes: "allen/"
    #
    allen_cells: "allen/"

    ##### mousegastrulation #####
    # Samples from the reference as described in EmbryoAtlasData object (for Lenne project : c(1:20,23:37)
    mousegastrulation_samples: "1,2,3,4,5,6,7,8,9,10,12,13,14,15,16,17,18,19,20,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37"
    # mousegastrulation_samples: "1,5"
    # File to save the atlas reference matrice in h5ad (col: genes (in exactly the same order than the data to annotate) ; raw = cells (need to be exactly the same than the metadata)) + celllabels
    mousegastrulation_matrix: "mousegastrulation.h5ad"
    # File to save the data to annotate in h5ad (col: genes (in exactly the same order than the reference)
    data_matrix: "data_matrix.h5ad"

sims:
    ##### General
    # Name of the column in your metadata on which you wish to train your model.
    # Allen mouse whole cortex : "subclass_label", arlotta : "New_cellType"
    class_label: "CellType"
    # Your personnal key to connect to your wandb account
    key: "536dbe5b6a398ef77292f0db3e39d55089c56583"
    # Number of cores (Put the same in the run_slurm.sh if necessary)
    num_workers: 24
    # Maximum number of epoch to train our model (Will stop after, start counting from 0)
    max_epoch: 500

    ##### Logger
    # Name of the project (Will be the name of the run and checkpoint)
    project_name: "mouse_gastrulation_atlas"

    ##### Early Stopping
    # The validation metric you wish to monitor for early stop.
    monitor: "val_loss"
    # The number of epochs during which the validation metric (monitor) may not improve before early shutdown is triggered. (To prevent overtraining)
    patience: 20

    # Epoch file from wandb to chosse for the cell assignation
    epoch: epoch=39-step=87120.ckpt

    ##### Cells annotation
    # Threshold of the difference between the first and second score of the prediction to say if a cell is unknown or not
    threshold: 0.425231
    # Seurat object with sims cell_assignation
    rds_assignation: filtered_assigned_seurat_object.rds
    # Matrix with cell predtion after filtration with the threshold
    pred_filtered: data_matrix_prediction_filtered.csv
    # pdf for the confusion matrix
    confusion_matrix: confusion_matrix.pdf
    
# Differentially expressed features analyses
diffexp:
    # Features of interest
    features: "SOX10,SOX9,RIPOR2,MITF,DCT,MYC,FLT1,TNFRSF11B,XIRP2,ST3GAL1"
    # Cells of interest to find differentially expressed features against "Normal" or Wild Type cells
    cell_marker: "M12,M15,M27,MM029,A375,MM031,MM057,MM099,MM087,MM001,MM074,MM011,MM047"
    # Test to find differentially expressed genes. Can be wilcox, bimod, roc, t, negbinom, poisson, LR, MAST, DESeq2.
    test: "wilcox"
    # Threshold to detect the under or over expressed genes
    threshold: 0.0
    # qvalue threshold
    pCutoff: 0.01
    # Foldchange threshold (not in log base 2)
    FCcutoff: 2

# Differentially expressed features analyses inside cluster after subset (by gene expression)
diffexpsubset:
    # Marker of interest to select cluster of Cells with expression or not of this gene. This gene will be removed for the DE analyses due to is high DE value.
    marker_gene: "RIPOR2"
    # If no gene indicated this filter is not performed.
    genetoremove: ""
    # Cluster on the seurat object (choose cluster with high number of the marker gene of interest)
    cluster: "Normal_Melanocytes,M15,M27,A375,MM057,MM087,MM001,MM074"

urd:
    # Calculate PCA and consider those PCs that with standard deviation 'seed'x expected by noise as significant
    seed: 2
    # Number of nearest neighbors for the diffusion map
    knn: 100
    # Width of the Gaussian used to transform cell-cell distances into transition probabilities
    sigma: 20
    # Number of simulations to perform and average during calculation pseudotime by flooding
    floodNsim: 50
    # (Numeric) The number of cells in the direction specified by pseudotime.direction at which the logistic should reach 1-asymptote.
    cellsForward: 20
    # (Numeric) The number of cells in the direction opposite from that specified by pseudotime.direction at which the logistic should reach asymptote.
    cellsBack: 40
    # Max steps for simulateRandomWalksFromTips fonction (default ncol(object@logupx.data))
    RWmaxSteps: 5000
    # (Character: "ks" or "preference") Test to use to determine whether visitation has diverged for each pseudotime window.
    treeMeth: "preference"
    # (Numeric) Approximate number of cells to assign to each pseudotime bin for branchpoint finding.
    cellsPerPTbin: 25
    # (Numeric) Width of moving window in pseudotime used for branchpoint finding, in terms of bins.
    binsPerPTwindow: 8
    # (Numeric) p-value threshold to use in determining whether visitation is significantly different from pairs of tips.
    treePtreshold: 0.001

correlation:
    # File with the count to take for the correlation between genes
    # input_count_matrices: "05_Output/02_seurat/72h/72h_scran_normalized_matrix.csv"
    # input_count_matrices: "05_Output/02_seurat/80h/80h_scran_normalized_matrix.csv"
    # input_count_matrices: "05_Output/02_seurat/86h/86h_scran_normalized_matrix.csv"
    input_count_matrices: "05_Output/02_seurat/96h/96h_scran_normalized_matrix.csv"
    # Time to analyse
    time: "96h"
    # Gene X
    gene_X: "Cdh2"
    # Gene Y
    gene_Y: "Cdh11"