correction/cellbender_remove_background_v0_2

Description

Eliminating technical artifacts from high-throughput single-cell RNA sequencing data.

This module removes counts due to ambient RNA molecules and random barcode swapping from (raw) UMI-based scRNA-seq count matrices.
At the moment, only the count matrices produced by the CellRanger count pipeline is supported. Support for additional tools and protocols
will be added in the future. A quick start tutorial can be found here.

Fleming et al. 2022, bioRxiv.

Type

python_script

License

MIT

Links

Repository

Launch

Run this component

Run the following command to execute this component with Nextflow:

cat > params.yaml <<'EOM'  
modality: [ "rna" ]  
output: "$id.$key.output.h5mu"  
layer_output: [ "corrected" ]  
obs_latent_rt_efficiency: [ "latent_rt_efficiency" ]  
obs_latent_cell_probability: [ "latent_cell_probability" ]  
obs_latent_scale: [ "latent_scale" ]  
var_ambient_expression: [ "ambient_expression" ]  
obsm_latent_gene_encoding: [ "cellbender_latent_gene_encoding" ]  
expected_cells_from_qc: [ true ]  
model: [ "full" ]  
epochs: [ 150 ]  
low_count_threshold: [ 15 ]  
z_dim: [ 100 ]  
z_layers: [ 500 ]  
training_fraction: [ 0.9 ]  
empty_drop_training_fraction: [ 0.5 ]  
fpr: [ 0.01 ]  
id: "run"  
publish_dir: "output/"  
EOM

nextflow run https://packages.viash-hub.com/vsh/openpipeline.git \  
  -revision 2.1.2 \  
  -main-script target/nextflow/correction/cellbender_remove_background_v0_2/main.nf \  
  -params-file params.yaml

Inputs

Name	Type & Properties	Description
--input -i	file required	Input h5mu file.
--modality	string	List of modalities to process.

Name

Type & Properties

--input

-i

file

required

--modality

string

Outputs

Name	Type & Properties	Description
--output -o	file required output	Full count matrix as an h5mu file, with background RNA removed. This file contains all the original droplet barcodes.
--output_compression	string
--layer_output	string	Output layer
--obs_latent_rt_efficiency	string
--obs_latent_cell_probability	string
--obs_latent_scale	string
--var_ambient_expression	string
--obsm_latent_gene_encoding	string

Name

Type & Properties

--output

-o

file

required

output

--output_compression

string

--layer_output

string

--obs_latent_rt_efficiency

string

--obs_latent_cell_probability

string

--obs_latent_scale

string

--var_ambient_expression

string

--obsm_latent_gene_encoding

string

Arguments

Name	Type & Properties	Description
--expected_cells	integer	Number of cells expected in the dataset (a rough estimate within a factor of 2 is sufficient).
--total_droplets_included	integer	The number of droplets from the rank-ordered UMI plot that will be analyzed. The largest 'total_droplets' droplets will have their cell probabilities inferred as an output.
--expected_cells_from_qc	boolean	Will use the Cell Ranger QC to determine the estimated number of cells
--model	string	Which model is being used for count data. 'simple' does not model either ambient RNA or random barcode swapping (for debugging purposes -- not recommended). 'ambient' assumes background RNA is incorporated into droplets. 'swapping' assumes background RNA comes from random barcode swapping. 'full' uses a combined ambient and swapping model.
--epochs	integer	Number of epochs to train.
--low_count_threshold	integer	Droplets with UMI counts below this number are completely excluded from the analysis. This can help identify the correct prior for empty droplet counts in the rare case where empty counts are extremely high (over 200).
--z_dim	integer	Dimension of latent variable z.
--z_layers	integer multiple	Dimension of hidden layers in the encoder for z.
--training_fraction	double	Training detail: the fraction of the data used for training. The rest is never seen by the inference algorithm. Speeds up learning.
--empty_drop_training_fraction	double	Training detail: the fraction of the training data each epoch that is drawn (randomly sampled) from surely empty droplets.
--fpr	double multiple	Target false positive rate in (0, 1). A false positive is a true signal count that is erroneously removed. More background removal is accompanied by more signal removal at high values of FPR. You can specify multiple values, which will create multiple output files.
--exclude_antibody_capture	boolean_true	Including the flag --exclude-antibody-capture will cause remove-background to operate on gene counts only, ignoring other features.
--learning_rate	double	Training detail: lower learning rate for inference. A OneCycle learning rate schedule is used, where the upper learning rate is ten times this value. (For this value, probably do not exceed 1e-3).
--cuda	boolean_true	Including the flag --cuda will run the inference on a GPU.

Name

Type & Properties

--expected_cells

integer

--total_droplets_included

integer

--expected_cells_from_qc

boolean

--model

string

--epochs

integer

--low_count_threshold

integer

--z_dim

integer

--z_layers

integer

multiple

--training_fraction

double

--empty_drop_training_fraction

double

--fpr

double

multiple

--exclude_antibody_capture

boolean_true

--learning_rate

double

--cuda

boolean_true