Process batches

This workflow serves as an entrypoint into the ‘full_pipeline’ in order to re-run the multisample processing and the integration setup.

Info

ID: process_batches
Namespace: workflows/multiomics

An input .h5mu file will first be split in order to run the multisample processing per modality. Next, the modalities are merged again and the integration setup pipeline is executed. Please note that this workflow assumes that samples from multiple pipelines are already concatenated.

Example commands

You can run the pipeline using nextflow run.

View help

You can use --help as a parameter to get an overview of the possible parameters.

nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -main-script target/nextflow/workflows/multiomics/process_batches/main.nf \
  --help

Run command

Example of params.yaml 
# Inputs
id: # please fill in - example: "foo"
input: # please fill in - example: ["input.h5mu"]
# rna_layer: "foo"
# prot_layer: "foo"

# Outputs
# output: "$id.$key.output.h5mu"

# Highly variable features detection
highly_variable_features_var_output: "filter_with_hvg"
highly_variable_features_obs_batch_key: "sample_id"

# QC metrics calculation options
var_qc_metrics: ["filter_with_hvg"]
top_n_vars: [50, 100, 200, 500]

# PCA options
pca_overwrite: false

# CLR options
clr_axis: 0

# RNA Scaling options
rna_enable_scaling: false
rna_scaling_output_layer: "scaled"
rna_scaling_pca_obsm_output: "scaled_pca"
rna_scaling_pca_loadings_varm_output: "scaled_pca_loadings"
rna_scaling_pca_variance_uns_output: "scaled_pca_variance"
rna_scaling_umap_obsm_output: "scaled_umap"
# rna_scaling_max_value: 123.0
rna_scaling_zero_center: true

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"

# Arguments
nextflow run openpipelines-bio/openpipeline \
  -r 2.1.0 -latest \
  -profile docker \
  -main-script target/nextflow/workflows/multiomics/process_batches/main.nf \
  -params-file params.yaml
Note

Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.

Argument groups

Inputs

Name Description Attributes
--id ID of the sample. string, required, example: "foo"
--input Path to the sample. List of file, required, example: "input.h5mu", multiple_sep: ";"
--rna_layer Input layer for the gene expression modality. If not specified, .X is used. string
--prot_layer Input layer for the antibody capture modality. If not specified, .X is used. string

Outputs

Name Description Attributes
--output Destination path to the output. file, required, example: "output.h5mu"

Highly variable features detection

Name Description Attributes
--highly_variable_features_var_output In which .var slot to store a boolean array corresponding to the highly variable genes. string, default: "filter_with_hvg"
--highly_variable_features_obs_batch_key If specified, highly-variable genes are selected within each batch separately and merged. This simple process avoids the selection of batch-specific genes and acts as a lightweight batch correction method. string, default: "sample_id"

QC metrics calculation options

Name Description Attributes
--var_qc_metrics Keys to select a boolean (containing only True or False) column from .var. For each cell, calculate the proportion of total values for genes which are labeled ‘True’, compared to the total sum of the values for all genes. List of string, default: "filter_with_hvg", example: "ercc,highly_variable", multiple_sep: ","
--top_n_vars Number of top vars to be used to calculate cumulative proportions. If not specified, proportions are not calculated. --top_n_vars 20,50 finds cumulative proportion to the 20th and 50th most expressed vars. List of integer, default: 50, 100, 200, 500, multiple_sep: ","

PCA options

Name Description Attributes
--pca_overwrite Allow overwriting slots for PCA output. boolean_true

CLR options

Name Description Attributes
--clr_axis Axis to perform the CLR transformation on. integer, default: 0

RNA Scaling options

Options for enabling scaling of the log-normalized data to unit variance and zero mean. The scaled data will be output a different layer and representation with reduced dimensions will be created and stored in addition to the non-scaled data.

Name Description Attributes
--rna_enable_scaling Enable scaling for the RNA modality. boolean_true
--rna_scaling_output_layer Output layer where the scaled log-normalized data will be stored. string, default: "scaled"
--rna_scaling_pca_obsm_output Name of the .obsm key where the PCA representation of the log-normalized and scaled data is stored. string, default: "scaled_pca"
--rna_scaling_pca_loadings_varm_output Name of the .varm key where the PCA loadings of the log-normalized and scaled data is stored. string, default: "scaled_pca_loadings"
--rna_scaling_pca_variance_uns_output Name of the .uns key where the variance and variance ratio will be stored as a map. The map will contain two keys: variance and variance_ratio respectively. string, default: "scaled_pca_variance"
--rna_scaling_umap_obsm_output Name of the .obsm key where the UMAP representation of the log-normalized and scaled data is stored. string, default: "scaled_umap"
--rna_scaling_max_value Clip (truncate) data to this value after scaling. If not specified, do not clip. double
--rna_scaling_zero_center If set, omit zero-centering variables, which allows to handle sparse input efficiently.” boolean_false

Authors

  • Dries Schaumont (author, maintainer)

Visualisation

flowchart TB
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    end
    subgraph group_rna_multisample [rna_multisample]
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    subgraph group_prot_multisample [prot_multisample]
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    end
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    end
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    end
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        v1147(leiden)
        v1178(move_obsm_to_obs)
        v1209(umap)
    end
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