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erhard-lab
grandR:Comprehensive Analysis of Nucleotide Conversion Sequencing Data
Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for downstream analyses. 'grandR' provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.
Maintained by Florian Erhard. Last updated 2 months ago.
11 stars 7.03 score 18 scripts 1 dependents
bioc
ADImpute:Adaptive Dropout Imputer (ADImpute)
Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.
Maintained by Ana Carolina Leote. Last updated 5 months ago.
geneexpressionnetworkpreprocessingsequencingsinglecelltranscriptomics
4.30 score 7 scripts