Software

COTree provides a novel statistical framework for deciphering cell-resolved multi-omics trajectories, which enables a broad range of downstream analyses, including cell classification, fate prediction, developmental time detection, and driver species identification.

[Github link]

MetaDICT provides a novel data integration method that can better avoid the overcorrection of batch effects and preserve biological variation when unobserved confounding variables exist or data sets are highly heterogeneous across studies. 

[Github link] and [Bioconductor]

RSim provides a novel reference-based normalization method called normalization via rank similarity that corrects sample-specific biases, even in the presence of many zero counts. 

[Github link]

RDB provides a new and robust differential abundance test for compositional data, which is robust to both constant-sum constraint and prevalent zero counts in the dataset. This package implements this new differential abundance test (RDB).

[Github link] and [Slides]

WUNT provides a new framework for average treatment effect estimation. This package implements the new weighting framework by uniform transformer (WUNT), proposed by Yu and Wang (2020).

[Github link]

DAFOT provides a new two-sample testing method for microbial compositional data by leveraging the phylogenetic tree information. This package implements a new maximum type test, Detector of Active Flow on a Tree (DAFOT), for quantitative comparison of microbial composition from different populations.

[Github link]

RKColocal provides baisc functions for dual-channel image input/output and colocalization analysis. RKColocal includes tools to display joint distribution of pixel intensities for colocalization analysis, evaluate the average degree of colocalization in a given region, qunatify the degree of colocalization at each pixel and identify colocalized regions.

[Github link] and [Vignettes]

NEB-FLIM provides an empirical bayesian analysis framework for time domain fluorescence lifetime imaging microscopy (FLIM) data analysis. This framework is developed based on a hierarchical statistical model for FLIM data and adopts a nonparametric maximum likelihood estimator to estimate the prior distribution.

Coming soon