Software | Long Research Group | Perelman School of Medicine at the University of Pennsylvania

MIHD: R package for multiple imputation for high-dimensional incomplete data.

References:

Y. Zhao and Q. Long, “Multiple imputation in the presence of high-dimensional data,” Statistical methods in medical research, p. 962280213511027, 2013.
Y. Deng, C. Chang, M. S. Ido, and Q. Long, “Multiple imputation for general missing data patterns in the presence of high-dimensional data,” Scientific reports, vol. 6, iss. 21689, 2016.

Package:

MIHD for Windows OS
MIHD for Mac OS

Manual: MIHD

BISS: R package for implementing boostrap imputation with variable selection.

Reference: Q. Long and B. A. Johnson, “Variable selection in the presence of missing data: resampling and imputation,” Biostatistics, vol. 16, iss. 3, pp. 596-610, 2015.

Package: BISSpkg 1.0

Manual: BISS

fgsPCA: matlab code to perform structured sparse PCA

Reference: Z. Li, S. Safo, and Q. Long, "Incorporating Biological Information in Sparse Principal Component Analysis with Application to Genomic Data", BMC bioinformatics 18.1 (2017): 332.

Matlab Code: fgsPCA

EMSHS: R code to perform an EM alrogithm for Bayesian shrinkage approach with the structural information incorporated

Reference: Chang, C., Kundu, S., & Long, Q. (2018). Scalable Bayesian variable selection for structured high‐dimensional data. Biometrics. (Publication)

Package: EMSHS R Package in CRAN

sSLDA: matlab code to perform structured sparse LDA

Reference: Safo, S.E., and Long, Q. (2016) Sparse linear discriminant analysis in structured covariates space. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12(2), pp.56-69.

Matlab Code: sSLDA

sSCCA: matlab code to perform structured sparse CCA

Reference: S. Safo, S. Li and Q. Long, "Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information", Biometrics 74.1 (2018): 300-312.

Matlab Code: sSCCA_v2

pCIA: R package for implementing penalized co-inertia analysis for two datasets.

Reference: E. Min, S. Safo, and Q. Long, “Penalized Co-Inertia Analysis with Applications to –Omics Data”, Bioinformatics, 2019, 35(6):1018-25.

Package: pCIA_0.9

Manual: pCIA

Distributed Learning Predictor: Python library for learning from multiple databases and building predictive models based on Distributed Noise Contrastive Estimation (Distributed NCE)

Reference: Li, Z., Roberts, K.E., Jiang, X., and Long, Q. Distributed Learning from Multiple EHR Databases: Contextual Embedding Models for Medical Events. Journal of Biomedical Informatics, 2019, 92, p.103138.

Link for the software on github: Distributed Learning Predictor

pmCIA: R package to perform the sparse multiple co-inertia analysis for multiple datasets

Reference: Min, E.J. and Long, Q., 2020. Sparse multiple co-Inertia analysis with application to integrative analysis of multi-Omics data. BMC Bioinformatics, 21, pp.1-12.

Package: pmCIA_0.9

Graph-guided Bayesian SVM: Matlab codes for the graph-guided Bayesian SVM

Reference: Wenli Sun, Changgee Chang, and Qi Long, "Graph-guided Bayesian SVM with Adaptive Structured Shrinkage Prior for high-dimensional data"

Package: GSVM-adaptive.zip

Distributed Multiple Imputation: R codes for the simulations reported in the paper

Reference: Changgee Chang, Yi Deng, Xiaoqian Jiang, and Qi Long. (2020) "Multiple Imputation for Analysis of Incomplete Data in Distributed Health Data Networks" Nature Communications, 11(1):5467.

R Codes: Distributed-MI.zip

GitHub: MIDist

Deep Learning with Gaussian Differential Privacy: Python codes

Reference: Bu, Z., Dong, J., Long, Q., and Su, W. (2020) Deep Learning with Gaussian Differential Privacy. Harvard Data Science Review, 2(3):1-48.

GitHub: Deep Learning with Gaussian Differential Privacy

Python Library for TensorFlow Privacy including Gaussian DP: Tensorflow Privacy

Accounting for Technical Noise in Bayesian Graphical Models of Single-Cell RNA-Sequencing Data: Python codes

Reference: Oh, J., Chang, C. and Long, Q. (2021) Accounting for Technical Noise in Bayesian Graphical Models of Single-Cell RNA-Sequencing Data. Biostatistics, in press

GitHub: scLGM

Multiple Imputation with Neural Network Gaussian Process: Python codes

Reference: Oh, J., Chang, C. and Long, Q. (2021) Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. Asian Conference on Machine Learning 2022 (ACML 2022), in press.

GitHub: MI-NNGP