Rna multisample
Processing unimodal multi-sample RNA transcriptomics data.
Info
ID: rna_multisample
Namespace: multiomics
Links
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 0.12.0 -latest \
-main-script ./workflows/multiomics/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"
# Output
# output: "$id.$key.output.h5mu"
# Filtering highly variable genes
filter_with_hvg_var_output: "filter_with_hvg"
filter_with_hvg_obs_batch_key: "sample_id"
filter_with_hvg_flavor: "seurat"
# filter_with_hvg_n_top_genes: 123
# QC metrics calculation options
var_qc_metrics: ["filter_with_hvg"]
top_n_vars: [50, 100, 200, 500]
# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
nextflow run openpipelines-bio/openpipeline \
-r 0.12.0 -latest \
-profile docker \
-main-script ./workflows/multiomics/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" |
Output
Name | Description | Attributes |
---|---|---|
--output |
Destination path to the output. | file , required, example: "output.h5mu" |
Filtering highly variable genes
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. For all flavors, genes are first sorted by how many batches they are a HVG. 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" |
--filter_with_hvg_flavor |
Choose the flavor for identifying highly variable genes. For the dispersion based methods in their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_genes. | string , default: "seurat" |
--filter_with_hvg_n_top_genes |
Number of highly-variable genes 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: "," |
Visualisation
flowchart LR v0(Input) v3(toSortedList) v5(flatMap) v7(toSortedList) v9(Output) v15(normalize_total) v17(join) v26(log1p) v28(join) v37(delete_layer) v39(join) v48(filter_with_hvg) v50(join) v59(rna_calculate_qc_metrics) v61(join) v68(Output) v0-->v3 v3-->v5 v5-->v7 v7-->v9 v5-->v17 v5-->v15 v15-->v17 v17-->v28 v17-->v26 v26-->v28 v28-->v39 v28-->v37 v37-->v39 v39-->v50 v39-->v48 v48-->v50 v50-->v61 v50-->v59 v59-->v61 v61-->v68