SIMS: Scalable, Interpretable Modeling for Single-Cell RNA-Seq Data Classification

SIMS is a pipeline for building interpretable and accurate classifiers for identifying any target on single-cell rna-seq data. The SIMS model is based on TabNet, a self-attention based model specifically built for large-scale tabular datasets.

SIMS takes in a list of arbitrarily many expression matrices along with their corresponding target variables. The expression matrices may be AnnData objects with format h5ad, or .csv. They must be in the matrix form cell x gene, and NOT gene x cell, since our training samples are the transcriptomes of individual cells.

The data is formatted like so:

• All matrices are cell x expression
• All label files contain a common column, known as the class_label, on which to train the model
• datafiles and labelfiles are the absolute paths to the expression matrices and labels, respectively

A call to generate and train the SIMS model could look like the following:

from scsims import SIMS

sims = SIMS(adata=adata, labels_key='class_label')
sims.train()

To customize the underlying pl.Trainer and Tabnet model params, we can initialize the SIMS model like

from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from scsims import SIMS

wandb_logger = WandbLogger(project=f"My Project", name=f"SIMS Model Training") # set up the logger to log data to Weights and Biases

sims = SIMS(adata=adata, labels_key='class_label')
sims.setup_model(n_a=64, n_d=64, weights=sims.weights)  # weighting loss by label freq
sims.setup_trainer(
    logger=wandb_logger,
    callbacks=[
        EarlyStopping(
            monitor="val_loss",
            patience=50,
        ),
        LearningRateMonitor(logging_interval="epoch"),
    ],
    num_epochs=100,
)
sims.train()

This will train the TabNet model on the given expression matrices with target variable given by the class_label column in each label file.