IGSimpute is an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer.
Download with
$ git clone https://github.com/ker2xu/IGSimpute
You need to create an enviroment named IGSimpute using conda from environment.yml.
$ conda env create -f environment.yml
You need to first activate the environment by:
conda activate IGSimpute
You can run the command below to perform imputation on the heart-and-aorta tissue in the Tabula Muris atlas:
./run_IGSimpute.sh
If you want to perform imputation on your own dataset, you need to modify parameters defined in run_IGSimpute.sh.
| Name | Default value | Description |
|---|---|---|
| data_dir | - | Path to the directory that contains all datasets. |
| dataset_dir | 'tm_droplet_Heart_and_Aorta' | Directory name of the dataset to be imputed. |
| exp_file_name | 'X.csv' | Expression file name. |
| output_dir | 'imputation_output' | Output directory name. |
| hg | '0.1' | The percentage or the number of used highly variable genes. |
| epochs | 100 | The maximum allowed epochs. |
| split_pct | "0.8" | The percentage of cells used as training dataset, and the left will be used for validtaion. |
| target_format | "count" | The expected output format. |
| ggl_loss_weight | "1" | The weight of |
| gsl_L1_weight | "0.01 | The weight of |
| rec_loss_weight | "0.1 | The weight of |
| batch_size | 256 | Minibatch size. |
| dim | 400 | Size of the innermost embedding. |
| encoder_dropout_rate | "0.2" | Dropout rate of the dropout layer in the encoder part. |
| gpu_node | 0 | The index of GPU to use. |
| low_expression_percentage | 0.80 | If the percentage of low expression neighbor entries exceeds low_expression_percentage, the gene expression target entry will be changed to zero. |
| low_expression_threshold | 0.20 | All zero entries, and non-zero entries with expression less than low_expression_threshold quantile will be taken as low expression entires in the KNN post-processing. |
| lr | "1e-4" | Learning rate. |
| seed | 0 | Seed number. |
| sub_sampling_num | "None" | Randomly select sub_sampling_num cells for training and validation. |
| valid_dropout | "0.2" | The percentage of non-zero entries to be used for validation. |
IGSimpute accepts expression profiles in h5ad or csv format. Each row should correspond to a cell and each column should correspond to a gene.
The output will be put inside "data_dir/dataset_dir/imputation_output" directory. "IGSimpute.name.csv.gz" is the imputed expression matrix without KNN post-processing and "IGSimpute.KNN.name.csv" is the imputed expression matrix wit KNN post-processing.