scDHA - Single-Cell Decomposition using Hierarchical Autoencoder
Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
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cpp
6.23 score 42 stars 20 scripts 247 downloadsSCFA - SCFA: Subtyping via Consensus Factor Analysis
Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients.
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survivalclusteringclassification
4.48 score 3 stars 8 scripts 314 downloadsscISR - Single-Cell Imputation using Subspace Regression
Provides an imputation pipeline for single-cell RNA sequencing data. The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).
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4.18 score 3 stars 8 scripts 173 downloads