Scanorama leiden

Run scanorama integration followed by neighbour calculations, leiden clustering and run umap on the result.

Info

ID: scanorama_leiden
Namespace: workflows/integration

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/workflows/integration/scanorama_leiden/main.nf \
  --help

Run command

Example of params.yaml

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

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

# Neighbour calculation
uns_neighbors: "scanorama_integration_neighbors"
obsp_neighbor_distances: "scanorama_integration_distances"
obsp_neighbor_connectivities: "scanorama_integration_connectivities"

# Scanorama integration options
obs_batch: "sample_id"
obsm_input: "X_pca"
obsm_output: "X_scanorama"
knn: 20
batch_size: 5000
sigma: 15
approx: true
alpha: 0.1

# Clustering options
obs_cluster: "scanorama_integration_leiden"
leiden_resolution: [1]

# Umap options
obsm_umap: "X_leiden_scanorama_umap"

# 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/workflows/integration/scanorama_leiden/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: `"dataset.h5mu"`
`--layer`	use specified layer for expression values instead of the .X object from the modality.	`string`, default: `"log_normalized"`
`--modality`	Which modality to process.	`string`, default: `"rna"`

Outputs

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

Neighbour calculation

Name	Description	Attributes
`--uns_neighbors`	In which .uns slot to store various neighbor output objects.	`string`, default: `"scanorama_integration_neighbors"`
`--obsp_neighbor_distances`	In which .obsp slot to store the distance matrix between the resulting neighbors.	`string`, default: `"scanorama_integration_distances"`
`--obsp_neighbor_connectivities`	In which .obsp slot to store the connectivities matrix between the resulting neighbors.	`string`, default: `"scanorama_integration_connectivities"`

Scanorama integration options

Name	Description	Attributes
`--obs_batch`	Column name discriminating between your batches.	`string`, default: `"sample_id"`
`--obsm_input`	.obsm slot that points to embedding 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`

Clustering options

Name	Description	Attributes
`--obs_cluster`	Prefix for the .obs keys under which to add the cluster labels. Newly created columns in .obs will be created from the specified value for ‘–obs_cluster’ suffixed with an underscore and one of the resolutions specified in ‘–leiden_resolution’.	`string`, default: `"scanorama_integration_leiden"`
`--leiden_resolution`	Control the coarseness of the clustering. Higher values lead to more clusters.	List of `double`, default: `1`, multiple_sep: `";"`

Umap options

Name	Description	Attributes
`--obsm_umap`	In which .obsm slot to store the resulting UMAP embedding.	`string`, default: `"X_leiden_scanorama_umap"`

Authors

Mauro Saporita (author)
Povilas Gibas (author)

Visualisation

flowchart TB
    v0(Channel.fromList)
    v18(scanorama)
    v49(concat)
    v38(find_neighbors)
    v59(leiden)
    v79(move_obsm_to_obs)
    v92(mix)
    v101(umap)
    v128(Output)
    v0-->v18
    v18-->v38
    v38-->v49
    v49-->v59
    v59-->v79
    v79-->v92
    v49-->v92
    v92-->v101
    v101-->v128
    style v0 fill:#e3dcea,stroke:#7a4baa;
    style v18 fill:#e3dcea,stroke:#7a4baa;
    style v49 fill:#e3dcea,stroke:#7a4baa;
    style v38 fill:#e3dcea,stroke:#7a4baa;
    style v59 fill:#e3dcea,stroke:#7a4baa;
    style v79 fill:#e3dcea,stroke:#7a4baa;
    style v92 fill:#e3dcea,stroke:#7a4baa;
    style v101 fill:#e3dcea,stroke:#7a4baa;
    style v128 fill:#e3dcea,stroke:#7a4baa;