workflows/multiomics/process_samples

Description

A pipeline to analyse multiple multiomics samples.

Type

nextflow_script

License

MIT

Links

Repository

Contributors

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
id: "run"  
output: "$id.$key.output.h5mu"  
add_id_to_obs: [ true ]  
add_id_obs_output: [ "sample_id" ]  
add_id_make_observation_keys_unique: [ true ]  
highly_variable_features_var_output: [ "filter_with_hvg" ]  
highly_variable_features_obs_batch_key: [ "sample_id" ]  
mitochondrial_gene_regex: [ "^[mM][tT]-" ]  
ribosomal_gene_regex: [ "^[Mm]?[Rr][Pp][LlSs]" ]  
top_n_vars: [ 50, 100, 200, 500 ]  
log1p_transform: [ true ]  
clr_axis: [ 0 ]  
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" ]  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.2.0 \  
  -main-script target/nextflow/workflows/multiomics/process_samples/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--id	string required	ID of the sample.
--input -i	file required	Path to the sample.
--rna_layer	string	Input layer for the gene expression modality. If not specified, .X is used.
--prot_layer	string	Input layer for the antibody capture modality. If not specified, .X is used.
--gdo_layer	string	Input layer for the guide-derived oligonucleotide (GDO) data. If not specified, .X is used.

Name

Type & Properties

--id

string

required

--input

-i

file

required

--rna_layer

string

--prot_layer

string

--gdo_layer

string

Outputs

Name	Type & Properties	Description
--output	file required output	Destination path to the output.

Name

Type & Properties

--output

file

required

output

Sample ID options

Name	Type & Properties	Description
--add_id_to_obs	boolean	Add the value passed with --id to .obs.
--add_id_obs_output	string	.Obs column to add the sample IDs to. Required and only used when --add_id_to_obs is set to 'true'
--add_id_make_observation_keys_unique	boolean	Join the id to the .obs index (.obs_names). Only used when --add_id_to_obs is set to 'true'.

Name

Type & Properties

--add_id_to_obs

boolean

--add_id_obs_output

string

--add_id_make_observation_keys_unique

boolean

RNA filtering options

Name	Type & Properties	Description
--rna_min_counts	integer	Minimum number of counts captured per cell.
--rna_max_counts	integer	Maximum number of counts captured per cell.
--rna_min_genes_per_cell	integer	Minimum of non-zero values per cell.
--rna_max_genes_per_cell	integer	Maximum of non-zero values per cell.
--rna_min_cells_per_gene	integer	Minimum of non-zero values per gene.
--rna_min_fraction_mito	double	Minimum fraction of UMIs that are mitochondrial.
--rna_max_fraction_mito	double	Maximum fraction of UMIs that are mitochondrial.
--rna_min_fraction_ribo	double	Minimum fraction of UMIs that are mitochondrial.
--rna_max_fraction_ribo	double	Maximum fraction of UMIs that are mitochondrial.
--skip_scrublet_doublet_detection	boolean_true	Skip the scrublet doublet detection step.
--scrublet_score_threshold	double	Manual doublet score threshold passed to filter_with_scrublet. Cells with a doublet score above this value are classified as doublets. If not provided, the threshold is determined automatically by Scrublet.

Name

Type & Properties

--rna_min_counts

integer

--rna_max_counts

integer

--rna_min_genes_per_cell

integer

--rna_max_genes_per_cell

integer

--rna_min_cells_per_gene

integer

--rna_min_fraction_mito

double

--rna_max_fraction_mito

double

--rna_min_fraction_ribo

double

--rna_max_fraction_ribo

double

--skip_scrublet_doublet_detection

boolean_true

--scrublet_score_threshold

double

CITE-seq filtering options

Name	Type & Properties	Description
--prot_min_counts	integer	Minimum number of counts per cell.
--prot_max_counts	integer	Minimum number of counts per cell.
--prot_min_proteins_per_cell	integer	Minimum of non-zero values per cell.
--prot_max_proteins_per_cell	integer	Maximum of non-zero values per cell.
--prot_min_cells_per_protein	integer	Minimum of non-zero values per protein.

Name

Type & Properties

--prot_min_counts

integer

--prot_max_counts

integer

--prot_min_proteins_per_cell

integer

--prot_max_proteins_per_cell

integer

--prot_min_cells_per_protein

integer

GDO filtering options

Name	Type & Properties	Description
--gdo_min_counts	integer	Minimum number of counts per cell.
--gdo_max_counts	integer	Minimum number of counts per cell.
--gdo_min_guides_per_cell	integer	Minimum of non-zero values per cell.
--gdo_max_guides_per_cell	integer	Maximum of non-zero values per cell.
--gdo_min_cells_per_guide	integer	Minimum of non-zero values per guide.

Name

Type & Properties

--gdo_min_counts

integer

--gdo_max_counts

integer

--gdo_min_guides_per_cell

integer

--gdo_max_guides_per_cell

integer

--gdo_min_cells_per_guide

integer

Cross-modality filtering

Name	Type & Properties	Description
--intersect_obs	boolean_true	After per-modality filtering and multisample processing, remove observations that are not present in all processed modalities so that each modality shares the same set of cells.

Name

Type & Properties

--intersect_obs

boolean_true

Highly variable features detection

Name	Type & Properties	Description
--highly_variable_features_var_output --filter_with_hvg_var_output	string	In which .var slot to store a boolean array corresponding to the highly variable genes.
--highly_variable_features_obs_batch_key --filter_with_hvg_obs_batch_key	string	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.

Name

Type & Properties

--highly_variable_features_var_output

--filter_with_hvg_var_output

string

--highly_variable_features_obs_batch_key

--filter_with_hvg_obs_batch_key

string

Mitochondrial & Ribosomal Gene Detection

Name	Type & Properties	Description
--var_gene_names	string	.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.
--var_name_mitochondrial_genes	string	In which .var slot to store a boolean array corresponding the mitochondrial genes.
--obs_name_mitochondrial_fraction	string	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.
--mitochondrial_gene_regex	string	Regex string that identifies mitochondrial genes from --var_gene_names. By default will detect human and mouse mitochondrial genes from a gene symbol.
--var_name_ribosomal_genes	string	In which .var slot to store a boolean array corresponding the ribosomal genes.
--obs_name_ribosomal_fraction	string	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.
--ribosomal_gene_regex	string	Regex string that identifies ribosomal genes from --var_gene_names. By default will detect human and mouse ribosomal genes from a gene symbol.

Name

Type & Properties

--var_gene_names

string

--var_name_mitochondrial_genes

string

--obs_name_mitochondrial_fraction

string

--mitochondrial_gene_regex

string

--var_name_ribosomal_genes

string

--obs_name_ribosomal_fraction

string

--ribosomal_gene_regex

string

QC metrics calculation options

Name	Type & Properties	Description
--var_qc_metrics	string multiple	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.
--top_n_vars	integer multiple	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.
--log1p_transform	boolean	Compute log1p-transformed versions of the calculated QC metrics. For each emitted metric column `<col>`, an additional column `log1p_<col>` is added containing `log1p(<col>)`. Mirrors scanpy's `log1p` flag.

Name

Type & Properties

--var_qc_metrics

string

multiple

--top_n_vars

integer

multiple

--log1p_transform

boolean

PCA options

Name	Type & Properties	Description
--pca_overwrite	boolean_true	Allow overwriting slots for PCA output.

Name

Type & Properties

--pca_overwrite

boolean_true

CLR options

Name	Type & Properties	Description
--clr_axis	integer	Axis to perform the CLR transformation on.

Name

Type & Properties

--clr_axis

integer

RNA Scaling options

Name	Type & Properties	Description
--rna_enable_scaling	boolean_true	Enable scaling for the RNA modality.
--rna_scaling_output_layer	string	Output layer where the scaled log-normalized data will be stored.
--rna_scaling_pca_obsm_output	string	Name of the .obsm key where the PCA representation of the log-normalized and scaled data is stored.
--rna_scaling_pca_loadings_varm_output	string	Name of the .varm key where the PCA loadings of the log-normalized and scaled data is stored.
--rna_scaling_pca_variance_uns_output	string	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.
--rna_scaling_umap_obsm_output	string	Name of the .obsm key where the UMAP representation of the log-normalized and scaled data is stored.
--rna_scaling_max_value	double	Clip (truncate) data to this value after scaling. If not specified, do not clip.
--rna_scaling_zero_center	boolean_false	If set, omit zero-centering variables, which allows to handle sparse input efficiently."

Name

Type & Properties

--rna_enable_scaling

boolean_true

--rna_scaling_output_layer

string

--rna_scaling_pca_obsm_output

string

--rna_scaling_pca_loadings_varm_output

string

--rna_scaling_pca_variance_uns_output

string

--rna_scaling_umap_obsm_output

string

--rna_scaling_max_value

double

--rna_scaling_zero_center

boolean_false