Process samples

A pipeline to analyse multiple multiomics samples.

Info

ID: process_samples
Namespace: workflows/multiomics

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_samples/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"
# gdo_layer: "foo"

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

# Sample ID options
add_id_to_obs: true
add_id_obs_output: "sample_id"
add_id_make_observation_keys_unique: true

# RNA filtering options
# rna_min_counts: 200
# rna_max_counts: 5000000
# rna_min_genes_per_cell: 200
# rna_max_genes_per_cell: 1500000
# rna_min_cells_per_gene: 3
# rna_min_fraction_mito: 0.0
# rna_max_fraction_mito: 0.2
# rna_min_fraction_ribo: 0.0
# rna_max_fraction_ribo: 0.2

# CITE-seq filtering options
# prot_min_counts: 3
# prot_max_counts: 5000000
# prot_min_proteins_per_cell: 200
# prot_max_proteins_per_cell: 100000000
# prot_min_cells_per_protein: 3

# GDO filtering options
# gdo_min_counts: 3
# gdo_max_counts: 5000000
# gdo_min_guides_per_cell: 200
# gdo_max_guides_per_cell: 100000000
# gdo_min_cells_per_guide: 3

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

# Mitochondrial & Ribosomal Gene Detection
# var_gene_names: "gene_symbol"
# var_name_mitochondrial_genes: "foo"
# obs_name_mitochondrial_fraction: "foo"
mitochondrial_gene_regex: "^[mM][tT]-"
# var_name_ribosomal_genes: "foo"
# obs_name_ribosomal_fraction: "foo"
ribosomal_gene_regex: "^[Mm]?[Rr][Pp][LlSs]"

# QC metrics calculation options
# var_qc_metrics: ["ercc,highly_variable"]
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_samples/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"
--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
--gdo_layer Input layer for the guide-derived oligonucleotide (GDO) data. If not specified, .X is used. string

Outputs

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

Sample ID options

Options for adding the id to .obs on the MuData object. Having a sample id present in a requirement of several components for this pipeline.

Name Description Attributes
--add_id_to_obs Add the value passed with –id to .obs. boolean, default: TRUE
--add_id_obs_output .Obs column to add the sample IDs to. Required and only used when –add_id_to_obs is set to ‘true’ string, default: "sample_id"
--add_id_make_observation_keys_unique Join the id to the .obs index (.obs_names). Only used when –add_id_to_obs is set to ‘true’. boolean, default: TRUE

RNA filtering options

Name Description Attributes
--rna_min_counts Minimum number of counts captured per cell. integer, example: 200
--rna_max_counts Maximum number of counts captured per cell. integer, example: 5000000
--rna_min_genes_per_cell Minimum of non-zero values per cell. integer, example: 200
--rna_max_genes_per_cell Maximum of non-zero values per cell. integer, example: 1500000
--rna_min_cells_per_gene Minimum of non-zero values per gene. integer, example: 3
--rna_min_fraction_mito Minimum fraction of UMIs that are mitochondrial. double, example: 0
--rna_max_fraction_mito Maximum fraction of UMIs that are mitochondrial. double, example: 0.2
--rna_min_fraction_ribo Minimum fraction of UMIs that are mitochondrial. double, example: 0
--rna_max_fraction_ribo Maximum fraction of UMIs that are mitochondrial. double, example: 0.2

CITE-seq filtering options

Name Description Attributes
--prot_min_counts Minimum number of counts per cell. integer, example: 3
--prot_max_counts Minimum number of counts per cell. integer, example: 5000000
--prot_min_proteins_per_cell Minimum of non-zero values per cell. integer, example: 200
--prot_max_proteins_per_cell Maximum of non-zero values per cell. integer, example: 100000000
--prot_min_cells_per_protein Minimum of non-zero values per protein. integer, example: 3

GDO filtering options

Name Description Attributes
--gdo_min_counts Minimum number of counts per cell. integer, example: 3
--gdo_max_counts Minimum number of counts per cell. integer, example: 5000000
--gdo_min_guides_per_cell Minimum of non-zero values per cell. integer, example: 200
--gdo_max_guides_per_cell Maximum of non-zero values per cell. integer, example: 100000000
--gdo_min_cells_per_guide Minimum of non-zero values per guide. integer, example: 3

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"

Mitochondrial & Ribosomal Gene Detection

Name Description Attributes
--var_gene_names .var column name to be used to detect mitochondrial/ribosomal genes instead of .var_names (default if not set). Gene names matching with the regex value from –mitochondrial_gene_regex or –ribosomal_gene_regex will be identified as mitochondrial or ribosomal genes, respectively. string, example: "gene_symbol"
--var_name_mitochondrial_genes In which .var slot to store a boolean array corresponding the mitochondrial genes. string
--obs_name_mitochondrial_fraction When specified, write the fraction of counts originating from mitochondrial genes (based on –mitochondrial_gene_regex) to an .obs column with the specified name. Requires –var_name_mitochondrial_genes. string
--mitochondrial_gene_regex Regex string that identifies mitochondrial genes from –var_gene_names. By default will detect human and mouse mitochondrial genes from a gene symbol. string, default: "^[mM][tT]-"
--var_name_ribosomal_genes In which .var slot to store a boolean array corresponding the ribosomal genes. string
--obs_name_ribosomal_fraction When specified, write the fraction of counts originating from ribosomal genes (based on –ribosomal_gene_regex) to an .obs column with the specified name. Requires –var_name_ribosomal_genes. string
--ribosomal_gene_regex Regex string that identifies ribosomal genes from –var_gene_names. By default will detect human and mouse ribosomal genes from a gene symbol. string, default: "^[Mm]?[Rr][Pp][LlSs]"

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. Defaults to the combined values specified for –var_name_mitochondrial_genes and –highly_variable_features_var_output. List of string, 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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    subgraph group_split_modalities_workflow [split_modalities_workflow]
        v59(split_modalities_component)
    end
    subgraph group_rna_singlesample [rna_singlesample]
        v141(grep_mitochondrial_genes)
        v172(grep_ribosomal_genes)
        v203(calculate_qc_metrics)
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        v318(rna_filter_with_counts)
        v349(rna_do_filter)
        v380(filter_with_scrublet)
    end
    subgraph group_prot_singlesample [prot_singlesample]
        v446(prot_filter_with_counts)
        v477(prot_do_filter)
    end
    subgraph group_gdo_singlesample [gdo_singlesample]
        v543(gdo_filter_with_counts)
        v574(gdo_do_filter)
    end
    subgraph group_process_batches [process_batches]
        v689(split_modalities_component)
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        subgraph group_rna_multisample [rna_multisample]
            v763(normalize_total)
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            v856(scale)
            v887(highly_variable_features_scanpy)
            v927(grep_mitochondrial_genes)
            v958(grep_ribosomal_genes)
            v989(calculate_qc_metrics)
        end
        subgraph group_prot_multisample [prot_multisample]
            v1077(clr)
            v1117(grep_mitochondrial_genes)
            v1148(grep_ribosomal_genes)
            v1179(calculate_qc_metrics)
        end
        subgraph group_neighbors_leiden_umap [neighbors_leiden_umap]
            v1346(find_neighbors)
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            v1408(move_obsm_to_obs)
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            v1871(umap)
        end
    end
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    v1672-->v1677
    v1677-->v1684
    v1545-->v1684
    v1545-->v1694
    v1694-->v1699
    v1699-->v1706
    v1509-->v1706
    v1509-->v1716
    v1716-->v1721
    v1725-->v1730
    v1730-->v1738
    v1738-->v1745
    v1730-->v1745
    v1730-->v1755
    v1761-->v1770
    v1770-->v1778
    v1778-->v1785
    v1770-->v1785
    v1770-->v1795
    v1804-->v1809
    v1809-->v1816
    v1804-->v1816
    v1804-->v1826
    v1826-->v1831
    v1835-->v1840
    v1840-->v1847
    v1835-->v1847
    v1835-->v1857
    v1857-->v1862
    v1866-->v1871
    v1871-->v1878
    v1866-->v1878
    v1866-->v1888
    v1888-->v1893
    v1893-->v1900
    v1761-->v1900
    v1761-->v1910
    v1910-->v1915
    v1915-->v1922
    v1725-->v1922
    v1725-->v1932
    v1932-->v1937
    v1937-->v1944
    v664-->v1944
    v664-->v1954
    v1954-->v1959
    v1959-->v1966
    v2-->v1966
    v1966-->v1973
    v2-->v1973
    v2-->v1985
    v1985-->v1992
    v2-->v1992
    v1992-->v1996
    v2-->v15
    v37-->v43
    v20-->v37
    v103-->v109
    v43-->v54
    v76-->v86
    v59-->v76
    v86-->v103
    v109-->v115
    v419-->v621
    v124-->v136
    v141-->v158
    v163-->v167
    v172-->v189
    v203-->v220
    v225-->v242
    v242-->v251
    v256-->v273
    v278-->v282
    v287-->v304
    v335-->v341
    v318-->v335
    v366-->v372
    v349-->v366
    v380-->v397
    v402-->v419
    v109-->v430
    v516-->v621
    v463-->v469
    v446-->v463
    v477-->v494
    v499-->v516
    v109-->v527
    v613-->v621
    v560-->v566
    v543-->v560
    v574-->v591
    v596-->v613
    v109-->v623
    v623-->v630
    v638-->v655
    v655-->v664
    v664-->v673
    v733-->v739
    v673-->v684
    v706-->v716
    v689-->v706
    v716-->v733
    v739-->v747
    v1050-->v1248
    v780-->v786
    v763-->v780
    v811-->v817
    v794-->v811
    v825-->v842
    v842-->v851
    v856-->v873
    v904-->v910
    v887-->v904
    v910-->v922
    v927-->v944
    v949-->v953
    v958-->v975
    v989-->v1006
    v1011-->v1028
    v1033-->v1050
    v739-->v1061
    v1240-->v1248
    v1094-->v1100
    v1077-->v1094
    v1100-->v1112
    v1117-->v1134
    v1139-->v1143
    v1148-->v1165
    v1179-->v1196
    v1201-->v1218
    v1223-->v1240
    v739-->v1250
    v1250-->v1258
    v1266-->v1283
    v1283-->v1293
    v1323-->v1329
    v1306-->v1323
    v1346-->v1363
    v1363-->v1372
    v1377-->v1394
    v1399-->v1403
    v1408-->v1425
    v1430-->v1434
    v1439-->v1456
    v1461-->v1478
    v1483-->v1500
    v1505-->v1509
    v1539-->v1545
    v1522-->v1539
    v1562-->v1579
    v1579-->v1588
    v1593-->v1610
    v1615-->v1619
    v1624-->v1641
    v1646-->v1650
    v1655-->v1672
    v1677-->v1694
    v1699-->v1716
    v1721-->v1725
    v1755-->v1761
    v1738-->v1755
    v1778-->v1795
    v1795-->v1804
    v1809-->v1826
    v1831-->v1835
    v1840-->v1857
    v1862-->v1866
    v1871-->v1888
    v1893-->v1910
    v1915-->v1932
    v1937-->v1954
    v1959-->v1985
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