Scanorama

Use Scanorama to integrate different experiments

Info

ID: scanorama
Namespace: integrate

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 1.0.1 -latest \
  -main-script target/nextflow/integrate/scanorama/main.nf \
  --help

Run command

Example of params.yaml

# Arguments
input: # please fill in - example: "path/to/file"
modality: "rna"
# output: "$id.$key.output.h5ad"
# output_compression: "gzip"
obs_batch: "batch"
obsm_input: "X_pca"
obsm_output: "X_scanorama"
knn: 20
batch_size: 5000
sigma: 15
approx: true
alpha: 0.1

# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"

nextflow run openpipelines-bio/openpipeline \
  -r 1.0.1 -latest \
  -profile docker \
  -main-script target/nextflow/integrate/scanorama/main.nf \
  -params-file params.yaml

Note

Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.

Argument group

Arguments

Name	Description	Attributes
--input	Input h5mu file	file, required
--modality		string, default: "rna"
--output	Output .h5mu file	file, required, default: "output.h5ad"
--output_compression	The compression format to be used on the output h5mu object.	string, example: "gzip"
--obs_batch	Column name discriminating between your batches.	string, default: "batch"
--obsm_input	Basis obsm slot to run scanorama on.	string, default: "X_pca"
--obsm_output	The name of the field in adata.obsm where the integrated embeddings will be stored after running this function. Defaults to X_scanorama.	string, default: "X_scanorama"
--knn	Number of nearest neighbors to use for matching.	integer, default: 20
--batch_size	The batch size used in the alignment vector computation. Useful when integrating very large (>100k samples) datasets. Set to large value that runs within available memory.	integer, default: 5000
--sigma	Correction smoothing parameter on Gaussian kernel.	double, default: 15
--approx	Use approximate nearest neighbors with Python annoy; greatly speeds up matching runtime.	boolean, default: TRUE
--alpha	Alignment score minimum cutoff	double, default: 0.1

Authors

• Dries De Maeyer (author)

• Dries Schaumont (maintainer)