Eleven grand challenges in single-cell data science
The recent boom in microfluidics and combinatorial indexing strategies, combined with low
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as
biological signals representing no or low gene expression, while others regard zeros as …
biological signals representing no or low gene expression, while others regard zeros as …
A systematic evaluation of single-cell RNA-sequencing imputation methods
Background The rapid development of single-cell RNA-sequencing (scRNA-seq)
technologies has led to the emergence of many methods for removing systematic technical …
technologies has led to the emergence of many methods for removing systematic technical …
Alignment and integration of spatial transcriptomics data
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …
DeepImpute: an accurate, fast, and 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. We present DeepImpute, a deep neural …
of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural …
Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis
A Sarkar, M Stephens - Nature genetics, 2021 - nature.com
The high proportion of zeros in typical single-cell RNA sequencing datasets has led to
widespread but inconsistent use of terminology such as dropout and missing data. Here, we …
widespread but inconsistent use of terminology such as dropout and missing data. Here, we …
A review of integrative imputation for multi-omics datasets
Multi-omics studies, which explore the interactions between multiple types of biological
factors, have significant advantages over single-omics analysis for their ability to provide a …
factors, have significant advantages over single-omics analysis for their ability to provide a …
Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
Understanding how gene expression programs are controlled requires identifying regulatory
relationships between transcription factors and target genes. Gene regulatory networks are …
relationships between transcription factors and target genes. Gene regulatory networks are …
scIGANs: single-cell RNA-seq imputation using generative adversarial networks
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic
profiles at the single-cell resolution with increasingly high throughput. However, it suffers …
profiles at the single-cell resolution with increasingly high throughput. However, it suffers …
CMF-Impute: an accurate imputation tool for single-cell RNA-seq data
Motivation Single-cell RNA-sequencing (scRNA-seq) technology provides a powerful tool for
investigating cell heterogeneity and cell subpopulations by allowing the quantification of …
investigating cell heterogeneity and cell subpopulations by allowing the quantification of …