The rapid development of single-cell RNA-sequencing (scRNA-seq) technology, with increased sparsity compared to bulk RNA-sequencing (RNA-seq), has led to the emergence of many methods for preprocessing, including imputation methods...
Read More »DeepImpute: scalable deep neural network method to impute single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. Researchers at the University of Hawaii present DeepImpute, a deep neural network-based imputation...
Read More »RESCUE – imputing dropout events in single-cell RNA-sequencing data
Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved. Researchers at ...
Read More »netSmooth – network-smoothing based imputation for single cell RNA-seq
Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high...
Read More »CIDR – Ultrafast and accurate clustering through imputation for single cell RNA-Seq data
Most existing dimensionality reduction and clustering packages for single cell RNA-Seq (scRNASeq) data deal with dropouts by heavy modelling and computational machinery. Here researchers from the Victor Chang Cardiac Research Institute introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ...
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