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URSM / Unified RNA-Sequencing Model

Analyzes two types of RNA-seq: single cell data and bulk data. URSM adjusts dropout events in single cell data and achieves simultaneously deconvolution in bulk data. This software doesn’t need to calculate on the same subjects the single cell and bulk data. It can (1) obtain reliable estimation of cell type specific gene expression profiles; (2) infer the dropout entries in single cell data; and (3) infer the mixing proportions of different cell types in bulk samples.

SAUCIE / Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding

Offers a method for handling and extracting structure from single-cell RNA-sequencing and CyTOF data. SAUCIE is a standalone software that provides a deep learning approach developed for the analysis of single-cell data from a cohort of patients. The application is based on different layers able to performs several tasks such as data imputation, clustering, batch correction or visualization. The approach is based on the autoencoder neural network framework for unsupervised learning.