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 1.0.1 -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

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
  -r 1.0.1 -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

Authors

  • Dries Schaumont (author, maintainer)

Visualisation

flowchart TB
    v55(flatMap)
    v370(mix)
    v372(mix)
    v388(merge)
    v404(branch)
    v478(concat)
    v482(branch)
    v556(concat)
    v565(publish)
    v593(Output)
    subgraph group_split_modalities_workflow [split_modalities_workflow]
        v0(Channel.fromList)
        v11(filter)
        v27(split_modalities_component)
    end
    subgraph group_rna_multisample [rna_multisample]
        v78(normalize_total)
        v98(log1p)
        v118(delete_layer)
        v138(highly_variable_features_scanpy)
        v150(filter)
        v168(grep_annotation_column)
        v181(mix)
        v190(calculate_qc_metrics)
        v210(publish)
    end
    subgraph group_prot_multisample [prot_multisample]
        v263(clr)
        v275(filter)
        v293(grep_annotation_column)
        v306(mix)
        v315(calculate_qc_metrics)
        v335(publish)
    end
    subgraph group_dimensionality_reduction_rna [dimensionality_reduction_rna]
        v417(pca)
        v437(find_neighbors)
        v457(umap)
    end
    subgraph group_dimensionality_reduction_prot [dimensionality_reduction_prot]
        v495(pca)
        v515(find_neighbors)
        v535(umap)
    end
    v370-->v372
    v404-->v478
    v482-->v556
    v0-->v11
    v11-->v27
    v27-->v55
    v55-->v78
    v78-->v98
    v98-->v118
    v118-->v138
    v138-->v150
    v150-->v168
    v168-->v181
    v150-->v181
    v181-->v190
    v190-->v210
    v210-->v370
    v55-->v263
    v263-->v275
    v275-->v293
    v293-->v306
    v275-->v306
    v306-->v315
    v315-->v335
    v335-->v370
    v55-->v372
    v372-->v388
    v388-->v404
    v404-->v417
    v417-->v437
    v437-->v457
    v457-->v478
    v478-->v482
    v482-->v495
    v495-->v515
    v515-->v535
    v535-->v556
    v556-->v565
    v565-->v593
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    style group_rna_multisample fill:#F0F0F0,stroke:#969696;
    style group_prot_multisample fill:#F0F0F0,stroke:#969696;
    style group_dimensionality_reduction_rna fill:#F0F0F0,stroke:#969696;
    style group_dimensionality_reduction_prot fill:#F0F0F0,stroke:#969696;
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