Digitaldlsorter

Digitaldlsorter is a software tool that uses Deep Learning (DL) algorithms to quantify immune cell infiltration in bulk RNA-Seq colorectal and breast cancer samples. The tool uses single-cell RNA sequencing (scRNA-Seq) data, which provides high-dimensional information about individual cell types within a tumor.

Key features of Digitaldlsorter:

1. Utilizes a Deep Neural Network (DNN) model to enumerate and quantify various immune cell subpopulations, including CD8+, CD4Tmem, CD4Th, CD4Tregs, and B-cells, as well as stromal content.

2. Builds immune cell signatures from scRNA-Seq data derived directly from the tumor, preserving the specific characteristics of the tumor microenvironment.

3. Applies the DNN model to synthetic bulk RNA-Seq data and samples from The Cancer Genome Atlas (TCGA) project, yielding accurate immune cell quantification and survival prediction results.

Topic

Immunology;RNA-Seq;Machine learning;Oncology;Transcriptomics

Detail

  • Operation: RNA-Seq quantification;Deisotoping;DNA vaccine design

  • Software interface: Workflow

  • Language: R,Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: European Union, Ministerio de Ciencia, Innovación, y Universidades, MEIC, Pro CNIC Foundation.

  • Input: -

  • Output: -

  • Contact: Fatima Sanchez-Cabo fscabo@cnic.es

  • Collection: -

  • Maturity: -

Publications

  • Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data.
  • Torroja C and Sanchez-Cabo F. Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data. Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data. 2019; 10:978. doi: 10.3389/fgene.2019.00978
  • https://doi.org/10.3389/FGENE.2019.00978
  • PMID: 31708961
  • PMC: PMC6824295

Download and documentation

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