Model-based autoencoders for imputing discrete single-cell RNA-seq data

Keyphrases

• Zero-inflated 100%
• Single-cell RNA Sequencing (scRNA-seq) 100%
• Autoencoder 100%
• Discrete Data 100%
• Single-cell RNA-seq Data 100%
• Single-cell RNA Sequencing Data 100%
• Deep Neural Network 50%
• Bioinformatics 50%
• Genomic Data 50%
• Real-data Experiments 50%
• Continuous Data 50%
• Count Data 50%
• Sequencing Studies 50%
• Zero Counts 50%
• Data Imputation 50%
• Zero-inflated Negative Binomial Model 50%
• Simulation Datasets 50%
• Dropout Events 50%
• Count Observations 50%
• Imputation Accuracy 50%
• Count Matrix 50%
• Zero-inflated Negative Binomial 50%
• Missing Data Imputation 50%
• Downstream Analysis 50%
• Gumbel-Softmax Distribution 50%
• RNA Sequencing Technology 50%
• Binomial Likelihood 50%
• Differential Expression Analysis 50%

Mathematics

• Missing Value 100%
• Matrix (Mathematics) 66%
• Negative Binomial 66%
• Data Imputation 66%
• Real Data 33%
• Differential Expression 33%
• Count Data 33%
• Continuous Data 33%
• Deep Neural Network 33%
• Binomial Likelihood 33%
• Binomial Model 33%

Computer Science

• Autoencoder 100%
• Deep Neural Network 50%
• Preprocessing 50%
• Missing Observation 50%
• Negative Binomial Model 50%
• Continuous Data 50%