Software Programs from Hongzhe Li's Group
Supported by NIH grants ES009911, CA127334, GM097505, GM129781 and GM123056, we have developed statistical and computational methods for analysis of genetic, genomics and microbiome data. We list here the programs that implement some of our latest
statistical methods for analysis of genetivcs, genomics and microbiome data sets, most are written in R/Mathlab or C,
and are available as Cran-R packages. Some datasets can also be found here.
You can also find documentation about he programs and software installation.
Note that these program are constantly beining updated.
Some packages are provided as stand-alone package and are available on other public sites.
For questions and comments, please email Dr. Hongzhe Li,
(hongzhe@pennmedicine.upenn.edu).
Standalone Software Developed by Our Group and are Available at Public Domains (GitHub)
TransLasso package implmenting the transfer learnong methods based on
high dimensional linear regression models "
- Sai Li, Tony Cai and Hongzhe Li
- Source code language: R
TransCLIME package implmenting the transfer learnong methods Gaussian grahical models
- Sai Li, Tony Cai and Hongzhe Li
- Source code language: R
PermReover package implmenting the spectral permutation recovery method for permuted monotone matrix model
for estimating the bacterial growth rate.
- Rong Ma, Tony Cai and Hongzhe Li
- Source code language: R
HighD-logistic package implmenting the global and simultaneous hypothesis testing methods
for high-dimensional logistic regression models.
- Rong Ma, Tony Cai and Hongzhe Li
- Source code language: R
MaxBlock package implmenting the sparse block signal detection and identification
methods for studying shared genetic variants between two traits.
- Jianqiao Wang and Hongzhe Li
- Source code language: R
GeneCorr package implmenting the moment estimation of genetic correlationb bassed
on summary GWAS statistics.
- Jianqiao Wang and Hongzhe Li
- Source code language: R
DAFOT package implmenting the detector of active flow that can be used to
identify bacterial taxa along the phylogenetic tree.
- Shulei Wang and Hongzhe Li
- Source code language: R and Python
DEMIC package for estimating the bacterial growth rates based on genome assemblies.
- Yuan Gao, Hongzhe Li and Scott Daniel
- Source code language: Python
Micropower package for microbiome power calculation based on PERMANOVA.
- Brandon Kelly et al.
- Source code language: R
ZIBR package for fitting zero-inflated mixed-effects models for
repeated measureed microbiome data.
- Eric Zhang Chen, Hongzhe Li
- Source code language: R
Program for identifying the breakpoints and CNVs based on the next generation sequence data using CIGAR strings.
- Wu Y, Tian L, Pirastu M, Stambolian D, Li H(2013, Frontiers in Genetics): MATCHCLIP: Locate precise break points for copy number variation using CIGAR strings.
Cran R codes - Programs and Data Sets Used in Our papers
This program is available as a cran-R package (link here) that implements a generalized
UniFrac distance for analysis of microbiome data.
- Chen J and Li H (2012).
- Source code language: R
MiRKAT package for testing microbiome and outcome association, adjusting for possible covariates.
- Ni Zhao, Michael Wu et al.
- Source code language: R
R/Mathlab Codes for Download - Programs and Data Sets Used in Our papers
This program .R file includes R codes for inference of high dimensional linear
mixed-effects models.
- Sai Li, Tony Cai and Hongzhe Li (2022, JASA).
- Source code language: R
This program .zip file includes R codes for compositional data regression
in paper "Variable selection in regression with compositional covariates".
- Wei Lin and Li H (2014, Biometrika).
- Source code language: R
This program .zip file includes Matlab codes and real data sets used in paper "Generalized Linear Models with Linear Constraints for Microbiome Compositional Data".
- Jiaru Lu, Pixu Shi and Li H (2019 Biometrics).
- Source code language: Mathlab
This program .zip file includes Matlab codes and real data sets used in paper "Optimal estimation of genetic relatedness in high-dimensional linear models.
- Guo Z, Wang W, Cai, TT and Li H (2019JASA).
- Source code language: Mathlab
This program .zip file includes R codes and real data sets used in
paper on modified rank tests for data with excessive zeros.
- Wang W, Chen EZ and Li H.
- Source code language: R