pipeline.yml

# ==============================================
# Preprocessing, spatial transcriptomics
# ==============================================


# ---------------------------
# 0. Compute resource options
# ---------------------------

resources:
  # Number of threads used for parallel jobs
  threads_high: 1 # Must be enough memory to load all input files and create MuDatas
  threads_medium: 1  # Must be enough memory to load your mudata and do computationally light tasks
  threads_low: 1 # Must be enough memory to load text files and do plotting, requires much less memory

condaenv:   # Path to conda env, leave blank if running native or your cluster automatically inherits the login node environment


# ------------------
## 1. Specify input
# ------------------

# With the preprocess_spatial workflow, one or multiple MuData objects can be preprocessed in one run. 
# For the preprocessing, it reads in all '.h5mu' objects of a directory, for example:
# ./qc.data
# ├── Human_LN_alt_unfilt.h5mu
# ├── Human_LN_unfilt.h5mu
# └── Human_heart_unfilt.h5mu

# The workflow then runs the preprocessing for each MuData object separately with the same parameters that are specified in this yaml file. 
# Note, that the MuData objects in the directory need to be of the same assay (Vizgen or Visium).

input_dir: ../ingestion/qc.data/
assay: visium # Specify the spatial transcriptomics assay, one of "visium" or "vizgen". If left blank, will use default "visium"


# --------------
## 2. Filtering
# --------------

# The filtering is fully customisable to any columns in the .obs or .var. You are not restricted by the columns given as default. When specifying a column name, make sure it exactly matches the column name in the h5mu object.
# General structure: 
# spatial:
  # obs: <- spot/cell level filtering
    # min: <-- Any column for which you want to run a minimum filter, 
     # n_genes_by_counts: 500 <--- i.e. each spot/cell must have a minimum of 500 in the n_genes_by_counts column
    # max: <-- Any column for which you want to run a maxiumum filter
     # pct_counts_mt: 20 <-- i.e. each spot/cell may have a maximum of 20 in the pct_counts_mt column
    # bool: 
      # highly_variable: True  <--- boolean columns you want to filter on, in this case any obs['highly_variable'] that are True will be retained in the dataset.
  # var: <- gene level filtering
    # min:
    # max:
    # bool

filtering:
  run: True
  keep_barcodes: # A file containing only barcodes you want to keep, leave blank if not applicable

  #------------------------------------------------------
  spatial:
  #------------------------------------------------------ 
  ## Obs filtering: spot/cell level filtering here
    obs:
      min:
        total_counts: 
      max:
        total_counts: 30000
        n_genes_by_counts: 
        pct_counts_mt: # Percent filtering: this should be a value between 0 and 100
        pct_counts_rp:
      bool: 
  ## Var filtering: gene level filtering here
    var:
      min:
        n_cells_by_counts: 
      max:
        total_counts:
        n_cells_by_counts:
        


# -------------------------
## 3. Post-filter plotting
# -------------------------

# All metrics should be comma-separated without spaces, e.g. a,b,c 
# If the metric is in both, .obs and .var, then both are plotted 
plotqc:
  grouping_var:  # Categorical variables to plot by. Not mandatory, can be left empty
  spatial_metrics: total_counts



# -----------------------------------------
# 4. Spatial transcriptomics preprocessing
# Normalization + HVG selection + PCA
# -----------------------------------------

spatial:
  # How to perform normalization and HVG selection:
  norm_hvg_flavour: seurat # ['seurat', 'squidpy']
  # 'seurat': HVG selection and normalization by analytic Pearson residuals using sc.experimental.pp.normalize_pearson_residuals() and sc.experimental.pp.highly_variable_genes()
  # 'squidpy': HVG selection and normalization using the standard scanpy functions (sc.pp.normalize_total(),sc.pp.log1p(), sc.pp.highly_variable_genes())

  # The following parameters are only for the case norm_hvg_flavour == squidpy:
  # Parameters for the HVG selection using sc.pp.highly_variable_genes():
  squidpy_hvg_flavour: #seurat #['seurat','cellranger','seurat_v3'], leave blank to use default ('seurat'), flavour for the sc.pp.highly_variable_genes() function
  min_mean:  # Leave blank to use default (0.05), parameter in sc.pp.highly_variable_genes()
  max_mean:  # Leave blank to use default (1.5), parameter in sc.pp.highly_variable_genes()
  min_disp: # Leave blank to use default (0.5), parameter in sc.pp.highly_variable_genes()

  # The following parameters are only for the case norm_hvg_flavour == seurat:
  theta: # Leave blank to use default (100), the negative binomial overdispersion parameter for Pearson residuals
  clip: # Leave blank to use default (None)
  # 'clip' can be specified as:
  #    None: residuals are clipped to the interval [-sqrt(n_obs), sqrt(n_obs)]
  #    a float value: if float c specified: clipped to the interval [-c, c]
  #    np.Inf: no clipping

  # Parameters for both cases norm_hvg_flavour = "seurat" and "squidpy":
  n_top_genes: 2000 # Default is None; mandatory for norm_hvg_flavour = "seurat" and squidpy_hvg_flavor: "seurat_v3"
  filter_by_hvg: False # Leave blank to use default (False); if True, subset the data to highly-variable genes after finding them
  hvg_batch_key: # Leave blank to use default (None); if specified, highly-variable genes are selected within each batch separately and merged

  n_pcs: 50 # Leave blank to use default (50); how many PCs to compute with sc.pp.PCA()

# -----------------------------------------
# 4. Concatenation
# -----------------------------------------

concat: False # Leave blank to use default (False); whether to conatenate all preprocessed SpatialDatas in ./filtered_data