Rna multisample

Processing unimodal multi-sample RNA transcriptomics data.

Info

ID: rna_multisample
Namespace: workflows/rna

Links

Source

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/rna/rna_multisample/main.nf \
  --help

Run command

Example of params.yaml

# Inputs
id: # please fill in - example: "concatenated"
input: # please fill in - example: "dataset.h5mu"
modality: "rna"
# layer: "foo"

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

# Filtering highly variable features
highly_variable_features_var_output: "filter_with_hvg"
highly_variable_features_obs_batch_key: "sample_id"
highly_variable_features_flavor: "seurat"
# highly_variable_features_n_top_features: 123

# QC metrics calculation options
var_qc_metrics: ["filter_with_hvg"]
top_n_vars: [50, 100, 200, 500]
output_obs_num_nonzero_vars: "num_nonzero_vars"
output_obs_total_counts_vars: "total_counts"
output_var_num_nonzero_obs: "num_nonzero_obs"
output_var_total_counts_obs: "total_counts"
output_var_obs_mean: "obs_mean"
output_var_pct_dropout: "pct_dropout"

# RNA Scaling options
enable_scaling: false
scaling_output_layer: "scaled"
# scaling_max_value: 123.0
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/rna/rna_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 concatenated file	`string`, required, example: `"concatenated"`
`--input`	Path to the samples.	`file`, required, example: `"dataset.h5mu"`
`--modality`	Modality to process.	`string`, default: `"rna"`
`--layer`	Input layer to use. If not specified, .X is used.	`string`

Output

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

Filtering highly variable features

Name	Description	Attributes
`--highly_variable_features_var_output`	In which .var slot to store a boolean array corresponding to the highly variable features.	`string`, default: `"filter_with_hvg"`
`--highly_variable_features_obs_batch_key`	If specified, highly-variable features are selected within each batch separately and merged. This simple process avoids the selection of batch-specific features and acts as a lightweight batch correction method. For all flavors, featues are first sorted by how many batches they are highly variable. For dispersion-based flavors ties are broken by normalized dispersion. If flavor = ‘seurat_v3’, ties are broken by the median (across batches) rank based on within-batch normalized variance.	`string`, default: `"sample_id"`
`--highly_variable_features_flavor`	Choose the flavor for identifying highly variable features. For the dispersion based methods in their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_features.	`string`, default: `"seurat"`
`--highly_variable_features_n_top_features`	Number of highly-variable features to keep. Mandatory if filter_with_hvg_flavor is set to ‘seurat_v3’.	`integer`

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: `","`
`--output_obs_num_nonzero_vars`	Name of column in .obs describing, for each observation, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each row the number of columns that contain data.	`string`, default: `"num_nonzero_vars"`
`--output_obs_total_counts_vars`	Name of the column for .obs describing, for each observation (row), the sum of the stored values in the columns.	`string`, default: `"total_counts"`
`--output_var_num_nonzero_obs`	Name of column describing, for each feature, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each column the number of rows that contain data.	`string`, default: `"num_nonzero_obs"`
`--output_var_total_counts_obs`	Name of the column in .var describing, for each feature (column), the sum of the stored values in the rows.	`string`, default: `"total_counts"`
`--output_var_obs_mean`	Name of the column in .obs providing the mean of the values in each row.	`string`, default: `"obs_mean"`
`--output_var_pct_dropout`	Name of the column in .obs providing for each feature the percentage of observations the feature does not appear on (i.e. is missing). Same as `--num_nonzero_obs` but percentage based.	`string`, default: `"pct_dropout"`

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
`--enable_scaling`	Enable scaling for the RNA modality.	`boolean_true`
`--scaling_output_layer`	Output layer where the scaled log-normalized data will be stored.	`string`, default: `"scaled"`
`--scaling_max_value`	Clip (truncate) data to this value after scaling. If not specified, do not clip.	`double`
`--scaling_zero_center`	If set, omit zero-centering variables, which allows to handle sparse input efficiently.”	`boolean_false`

Authors

Dries De Maeyer (author)
Robrecht Cannoodt (author, maintainer)
Dries Schaumont (author)

Visualisation

flowchart TB
    v0(Channel.fromList)
    v2(filter)
    v10(filter)
    v18(normalize_total)
    v25(cross)
    v35(cross)
    v41(filter)
    v49(log1p)
    v56(cross)
    v66(cross)
    v72(filter)
    v80(delete_layer)
    v87(cross)
    v97(cross)
    v106(branch)
    v133(concat)
    v111(scale)
    v118(cross)
    v128(cross)
    v134(filter)
    v142(highly_variable_features_scanpy)
    v149(cross)
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    v165(filter)
    v288(concat)
    v177(branch)
    v204(concat)
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    v325(Output)
    subgraph group_rna_qc [rna_qc]
        v182(grep_mitochondrial_genes)
        v213(grep_ribosomal_genes)
        v244(calculate_qc_metrics)
    end
    v106-->v133
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    v288-->v314
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