Li‐Fang Chu

This paper introduces SINGE, a method for gene regulatory network inference from ordered single-cell transcriptomic data, showing improved performance over existing methods in mouse stem cell differentiation studies.

This study shows that equalizing cDNA library concentrations before pooling improves gene detection rates and reduces technical artifacts in single-cell RNA-seq data.

This study evaluates the reproducibility of spatial ordering analysis across different single-cell RNA-seq protocols, finding high reproducibility despite differences in sequencing depth and computational methods.

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.

SCnorm provides a quantile-regression based normalization method for scRNA-seq data to reduce artifacts and improve downstream analysis accuracy.