Software

Single-cell RNA-seq

• scImpute (imputation method for single-cell RNA-Seq data)
  • Citation: Li, W.V. and Li, J.J. (2018). An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature Communications 9:997.
• scDesign (a statistical simulator for rational scRNA-seq experimental design)
  • Citation: Li, W.V. and Li, J.J. (2019). A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics 35(14):i41–i50.
• scDesign2 (a simulator that generates single-cell gene expression counts with gene correlations captured)
  • Citation 1: Sun, T., Song, D., Li, W.V., and Li, J.J. (2021). scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Genome Biology 22:163.
  • Citation 2: Sun, T., Song, D., Li, W.V., and Li, J.J. (2022). Simulating single-cell gene expression count data with preserved gene correlations by scDesign2. Journal of Computational Biology 29(1):23–26 (software article).
• scDesign3 (a simulator that generates realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data)
  • Citation: Song, D., Wang, Q., Yan, G., Liu, T., and Li, J.J. (2023). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology.
• PseudotimeDE (inference of differential gene expression along cell pseudotime)
  • Citation: Song, D. and Li, J.J. (2021). PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome Biology 22:124.
• scGTM (a flexible and interpretable model of gene expression trend along cell pseudotime)
  • Citation: Cui, E.H., Song, D., Wong, W.K., and Li, J.J. (2022). Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime. Bioinformatics 38(16):3927–3934.
• scPNMF (single-cell projective non-negative matrix factorization for selecting informative features that distinguish cell types)
  • Citation: Song, D., Li, K.A., Hemminger, Z., Wollman, R., and Li, J.J. (2021). scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling. Bioinformatics 37(Supplement_1):i358-i366.
• scSampler (fast diversity-preserving subsampling of large-scale single-cell transcriptomic data)
  • Citation: Song, D., Xi, N.M., Li, J.J., and Wang, L. (2022). scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data. Bioinformatics 38(11):3126–3127.
• DoubletCollection (R package that integrates the execution and benchmark of doublet-detection methods)
  • Citation: Xi, N.M. and Li, J.J. (2021). Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis. STAR Protocols 2(3):100699.

Bulk RNA-seq isoform discovery and quantification

• SLIDE (Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation)
  • Citation: Li, J.J., Jiang, C.-R., Brown, B.J., Huang, H., and Bickel, P.J. (2011). Sparse linear modeling of RNA-seq data for isoform discovery and abundance estimation. Proc Natl Acad Sci. USA 108(50):19867-19872.
  • Important note: SLIDE is compatible with RNA-seq .bam files mapped by TopHat and TopHat2.
  • (Updates on Jan 30th, 2018 — Several bugs have been fixed for RNA-seq .bam files with more than one read lengths.)
  • (Updates on May 7th, 2012 — A feature was added for estimating the annotated isoform abundance without doing isoform discovery.)
  • (Updates on Apr 18th, 2012 — A feature was added for removing erroneously mapped reads; a bug of multiprocessing was fixed.)
  • (Updates on Apr 5th, 2012 — A feature was added for handling single-end RNA-Seq reads or a mixture of single-end and paired-end reads.)
• NMFP (Non-negative Matrix Factorization based Preselection)
  • Please cite the following paper in any research that uses this software package
    • Ye, Y. and Li, J.J. (2016). NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data. BMC Genomics 17(Supp 1):11.
• MSIQ (joint modeling of Multiple RNA-seq Samples for accurate Isoform Quantification)
  • Citation: Li, W.V., Zhao, A., Zhang, S., and Li, J.J. (2018). MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification. Annals of Applied Statistics 12(1):510-539.
• AIDE (annotation-assisted isoform discovery)
  • Citation: Li, W.V., Li, S., Tong, X., Deng, L., Shi, H., and Li, J.J. (2019). AIDE: annotation-assisted isoform discovery with high precision. Genome Research 29:2056-2072.

Comparative genomics

• TROM (TRanscriptome Overlap Measure)
  • Citation: Li, W.V., Chen, Y., and Li, J.J. (2017). TROM: a testing-based method for finding transcriptomic similarity of biological samples. Statistics in Biosciences 9(1):105-136.
• EPOM (EPigenome Overlap Measure)
  • Citation: Li, W.V., Razaee, Z.S., and Li, J.J. (2016). Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states. BMC Genomics 17(Supp 1):10.
• EpiAlign (an alignment-based bioinformatic tool for comparing chromatin state sequences)
  • Citation: Ge, X., Zhang, H., Xie, L., Li, W.V., Kwon, S.B., and Li, J.J. (2019). EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences. Nucleic Acids Research gkz287.
• BiTSC (bipartite tight spectral clustering)
  • Citation: Sun, Y.E., Zhou, H.J., and Li, J.J. (2020). Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species. Bioinformatics btaa741.

Microbiomics

• mbImpute (imputation method for microbiome data)
  • Citation: Jiang, R., Li, W.V., and Li, J.J. (2021). mbImpute: an accurate and robust imputation method for microbiome data. Genome Biology accepted after minor revision.

Classification

• NPROC (Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC))
  • Citation: Tong, X., Feng, Y., and Li, J.J. (2018). Neyman-Pearson (NP) classification algorithms and NP receiver operating characteristics (NP-ROC). Science Advances 4(2):eaao1659.
• frc (feature ranking for classification)
  • Citation: Li, J.J., Chen, Y., and Tong, X. (2021). A flexible model-free prediction-based framework for feature ranking. Journal of Machine Learning Research 22:1-54.
• ITCA (combination of ambiguous outcome labels in multi-class classification)
  • Citation: Zhang, C., Chen, Y.E., Zhang, S., and Li, J.J. (2022). Information-theoretic classification accuracy: a criterion that guides data-driven combination of ambiguous outcome labels in multi-class classification. Journal of Machine Learning Research 23(341):1−65.

Association measure

• gR2 (generalized R squares measures)
  • Citation: Li, J.J., Tong, X., and Bickel, P.J. (2019). Generalized R2 measures for a mixture of bivariate linear dependences. arXiv:1811.09965.

High-dimensional model inference

• HDCI (high-dimensional linear model coefficient confidence interval)
  • Citation: Liu, H., Xu, X., and Li, J.J. (2020). A bootstrap lasso + partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models. Statistica Sinica 30:1333-1355.

False discovery rate control

• Clipper (p-value-free FDR control in high-throughput genomics data analysis)
  • Citation: Ge, X., Chen, Y.E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, L.D., Li, W., and Li, J.J. (2021). Clipper: p-value-free FDR control on high-throughput data from two conditions. Genome Biology 22:288.