feature_annotation/highly_variable_features_scanpy

Description

Annotate highly variable features [Satija15] [Zheng17] [Stuart19].

Expects logarithmized data, except when flavor='seurat_v3' in which count data is expected.

Depending on flavor, this reproduces the R-implementations of Seurat [Satija15], Cell Ranger [Zheng17], and Seurat v3 [Stuart19].

For the dispersion-based methods ([Satija15] and [Zheng17]), the normalized dispersion is obtained by scaling with the mean and standard deviation of the dispersions for features falling into a given bin for mean expression of features. This means that for each bin of mean expression, highly variable features are selected.

For [Stuart19], a normalized variance for each feature is computed. First, the data are standardized (i.e., z-score normalization per feature) with a regularized standard deviation. Next, the normalized variance is computed as the variance of each feature after the transformation. Features are ranked by the normalized variance.

Arguments

Name	Type & Properties	Description
--input	file required	Input h5mu file
--modality	string	Which modality from the input MuData file to process.
--layer	string	use adata.layers[layer] for expression values instead of adata.X.
--var_input	string	If specified, use boolean array in adata.var[var_input] to calculate hvg on subset of vars.
--output	file output	Output h5mu file.
--var_name_filter	string	In which .var slot to store a boolean array corresponding to which observations should be filtered out.
--varm_name	string	In which .varm slot to store additional metadata.
--flavor	string	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.
--n_top_features	integer	Number of highly-variable features to keep. Mandatory if flavor='seurat_v3'.
--min_mean	double	If n_top_features is defined, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.
--max_mean	double	If n_top_features is defined, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.
--min_disp	double	If n_top_features is defined, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.
--max_disp	double	If n_top_features is defined, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'. Default is +inf.
--span	double	The fraction of the data (cells) used when estimating the variance in the loess model fit if flavor='seurat_v3'.
--n_bins	integer	Number of bins for binning the mean feature expression. Normalization is done with respect to each bin. If just a single feature falls into a bin, the normalized dispersion is artificially set to 1.
--obs_batch_key	string	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, features 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.
--output_compression	string	Compression format to use for the output AnnData and/or Mudata objects. By default no compression is applied.

Name

Type & Properties

--input

file

required

--modality

string

--layer

string

--var_input

string

--output

file

output

--var_name_filter

string

--varm_name

string

--flavor

string

--n_top_features

integer

--min_mean

double

--max_mean

double

--min_disp

double

--max_disp

double

--span

double

--n_bins

integer

--obs_batch_key

string

--output_compression

string

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
output: "$id.$key.output.h5mu"  
var_name_filter: [ "filter_with_hvg" ]  
varm_name: [ "hvg" ]  
flavor: [ "seurat" ]  
min_mean: [ 0.0125 ]  
max_mean: [ 3 ]  
min_disp: [ 0.5 ]  
span: [ 0.3 ]  
n_bins: [ 20 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision v4.0.0 \  
  -main-script target/nextflow/feature_annotation/highly_variable_features_scanpy/main.nf \  
  -params-file params.yaml

feature_annotation/highly_variable_features_scanpy

Arguments

Run this component

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