Multisample

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

Info

ID: multisample
Namespace: multiomics

Links

Source

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 0.10.0 -latest \
  -main-script ./workflows/multiomics/multisample/main.nf \
  --help

Run command

Example of params.yaml

# Inputs
id: # please fill in - example: "foo"
input: # please fill in - example: "input.h5mu"

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

# Highly variable gene detection
filter_with_hvg_var_output: "filter_with_hvg"
filter_with_hvg_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 0.10.0 -latest \
  -profile docker \
  -main-script ./workflows/multiomics/multisample/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.	file, required, example: "input.h5mu"

Outputs

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

Highly variable gene detection

Name	Description	Attributes
--filter_with_hvg_var_output	In which .var slot to store a boolean array corresponding to the highly variable genes.	string, default: "filter_with_hvg"
--filter_with_hvg_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 LR
    p0(Input)
    p2(toSortedList)
    p4(flatMap)
    p7(filter)
    p12(split_modalities)
    p14(join)
    p21(concat)
    p17(filter)
    p19(test_wf:run_wf:split_modalities_workflow:splitStub)
    p22(flatMap)
    p23(filter)
    p26(toSortedList)
    p28(flatMap)
    p30(toSortedList)
    p32(Output)
    p38(normalize_total)
    p40(join)
    p48(log1p)
    p50(join)
    p58(delete_layer)
    p60(join)
    p68(filter_with_hvg)
    p70(join)
    p78(rna_calculate_qc_metrics)
    p80(join)
    p121(concat)
    p86(filter)
    p89(toSortedList)
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    p146(pca)
    p148(join)
    p156(find_neighbors)
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    p166(umap)
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    p187(pca)
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    p197(find_neighbors)
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    p207(test_wf:run_wf:integration_setup_workflow:initialize_integration_prot:umap:umap_process1)
    p209(join)
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    p213(filter)
    p220(publish)
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