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SIMS (Scalable, Interpretable Modeling for Single-Cell RNA-Seq Data Classification) 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 a sequential transformer, a transformer model specifically built for large-scale tabular datasets. You only need a single cell dataset in the shape of an h5ad file to get started. Upload your file, select the model you want to use to perform your predictions, and download your results!

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SIMS (Scalable, Interpretable Modeling for Single-Cell RNA-Seq Data Classification) simplifies the task of building accurate classifiers for scRNA-seq data. With its sequential transformer model, designed for large-scale tabular datasets, SIMS ensures both accuracy and interpretability. Just upload your h5ad file, choose your model, and download your results. Ideal for biologists and computational biologists alike, SIMS offers a user-friendly interface for extracting insights from scRNA-seq data effortlessly.

See owner's GitHub repository for more information:

This app is based on the paper:

Gonzalez-Ferrer, J., Lehrer, J., O'Farrell, A., Paten, B., Teodorescu, M., Haussler, D., Jonsson, V. D., & Mostajo-Radji, M. A. (2023). Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis. bioRxiv : the preprint server for biology, 2023.02.28.529615.

This application was not uploaded by the author, but through their publicly available Github repository,

MIT License

Copyright (c) 2021, Julian Lehrer


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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J Jesus Gonzalez Ferrer

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