SUN Lab

SPARK is a method for detecting genes with spatial expression patterns in spatially resolved transcriptomic studies. SPARK directly models count data generated from various spatial resolved transcriptomic techniques through generalized spatial linear models. With a new efficient penalized quasi-likelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on tens of thousands of samples. Importantly, SPARK relies on newly developed statistical formulas for hypothesis testing, producing well-calibrated p-values and yielding high statistical power. The software is distributed under the GNU General Public License.

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The package is currently available at github.

Citations

Shiquan Sun*, Jiaqiang Zhu* and Xiang Zhou (2020). Statistical analysis of spatial expression pattern for spatially resolved transcriptomic studies. Nature Methods. 17: 193-200.

All analysis scripts used in the paper is available at github.

Contact me or Jiaqiang Zhu with any questions, comments, or bugs reports.

SpatialMap is a computational method, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue. SpatialMap directly models the count nature of spatial gene expression data through generalized linear spatial models, which accounts for the spatial correlation among spatial locations using conditional autoregressive (CAR) prior. The software is distributed under the GNU General Public License.

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The package is currently available at github.

Citations

Dalong Gao*, Jin Ning*, Gang Liu, Shiquan Sun# and Xiaoqian Dang# (2022). SpatialMap: Spatial mapping of unmeasured gene expression profiles in spatial transcriptomic data using generalized linear spatial models. Front. Genet. 13:893522. doi: 10.3389/fgene.2022.893522.

All analysis scripts used in the paper is available at github.

Contact Jin Ning with any questions, comments, or bugs reports.

TDEseq is a computational method for detecting temporal gene expression patterns, including growth, recession, peak, and trough four possible temporal dynamic expression patterns in time-resolved or time-course scRNA-seq transcriptomic studies. TDEseq directly models transformed scRNA-seq data and its corresponding time points through constrained additive mixed models, which can produce well-calibrated p-values as well as powerful temporal gene detection. The software is distributed under the GNU General Public License.

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The package is currently available at github.

Update Log

• 12/22/2023: version 1.1.1. Yue made the software scalable for large-scale data, and the package now allows 'Seurat' or 'SingleCellExperiment' objects as input.
• 08/22/2023: version 1.1.0. Yue extended the model with pseudo-cell options.

Citations

Yue Fan, Lei Li, and Shiquan Sun# (2024). Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq. Genome Biology. 25: 96.

• All analysis scripts used in the paper is available at github.
• The detailed tutorials are currently available at here.
• The web server to access is currently available at here.

Contact Yue Fan with any questions, comments, or bugs reports.

iDEA is a method for performing joint differential expression (DE)and gene set enrichment (GSE) analysis. iDEA builds upon a hierarchical Bayesian model for joint modeling of DE and GSE analyses. It uses only summary statistics as input, allowing for effective data modeling through complementing and pairing with various existing DE methods. It relies on an efficient expectation-maximization algorithm with internal Markov Chain Monte Carlo steps for scalable inference. By integrating DE and GSE analyses, iDEA can improve the power and consistency of DE analysis and the accuracy of GSE analysis over common existing approaches. The software is distributed under the GNU General Public License.

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The package is currently available at github.

Citations

Ying Ma*, Shiquan Sun*, Xuequn Shang, Evan T. Keller, Mengjie Chen and Xiang Zhou. (2020) Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies.Nature Communications. 11: 1585.

All analysis scripts used in the paper is available at github.

Contact me or Ying Ma with any questions, comments, or bugs reports.

MACAU is the software implementing the Mixed model Association for Count data via data AUgmentation algorithm. It fits a binomial mixed model to perform differential methylation analysis for bisulfite sequencing studies. It fits a Poisson mixed model to perform differential expression analysis for RNA sequencing studies. It is computationally efficient for large scale sequencing studies and uses freely available open-source numerical libraries. It is distributed under the GNU General Public License.

C/C++ Version Download

• Executable, Version 1.40, for x86 64-bit Linux, compiled on 08/09/2019
• Source Code,Version 1.40, last modified on 08/09/2019
• Example Data Sets: a bisulfite sequencing data with 438,865 methylation sites for 50 baboons; and an RNA squencing data with 12,018 genes for 63 baboons.
• User Manual, last edited on 03/15/2017
• R script to fit a beta-binomial model

R Version Download

• User Manual, last edited on 04/09/2017
• R Reference Manual, last edited on 04/08/2017
• Source Code,Version 2.00, last modified on 04/09/2017

Update Log

• 08/09/2019: version 1.40. Shiquan extended the model to unrelated individuals. PMM can now fit data without providing a kinship matrix.
• 05/27/2019: version 1.30. Shiquan fixed a bug on reading methylation count data.
• 03/22/2018: version 1.20. Shiquan fixed a bug, where the heritability estimate always equals 0.5 when the sample size is larger than 200.
• 03/15/2017: version 1.10. implements the Poisson mixed model for differentia expression analysis in RNA sequencing studies.
• 12/09/2015: version 1.00. implements the Binomial mixed model for differentia methylation analysis in bisulfite sequencing studies.
• 06/05/2015: version 1.00alpha, initial release.

Citations

• Binomial mixed models for bisulfite sequencing analysis
      Amanda J. Lea, Jenny Tung and Xiang Zhou (2015). A flexible, effcient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. PLoS Genetics. 11: e1005650.
• Poisson mixed models for RNA sequencing analysis
     Shiquan Sun, Michelle Hood, Laura Scott, Qinke Peng, Sayan Mukherjee, Jenny Tung, and Xiang Zhou. (2017) Differential expression analysis for RNAseq using Poisson mixed models. Nucleic Acids Research 45: e106.

Contact me with any questions, comments, or bugs reports.

PQLseq is a method that fits generalized linear mixed models for analyzing RNA sequencing and bisulfite sequencing data. It estimates gene expression or methylation heritability for count data. It performs differential expression analysis in the presence of individual relatedness or population stratificaiton. The software is distributed under the GNU General Public License.

The package is available in CRAN

Download

• User Manual, last edited on 03/20/2018
• CRAN R Package,Version 1.0, last modified on 06/10/2018

Update Log

• 03/20/2018: version 0.10, initial release; now available in CRAN.

Citations

Shiquan Sun*, Jiaqiang Zhu*, Sahar Mozaffari, Carole Ober, Mengjie Chen, and Xiang Zhou. (2019) Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies. Bioinformatics 35: 487-496.

Contact me with any questions, comments, or bugs reports.