ProFAST - Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
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6.36 score 4 stars 2 dependents 19 scripts 610 downloadsGFM - Generalized Factor Model
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.
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approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factorsopenblascpp
5.86 score 2 stars 3 dependents 10 scripts 243 downloadsDR.SC - Joint Dimension Reduction and Spatial Clustering
Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
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dimension-reductionselfsupervisedspatial-clusteringspatial-transcriptomicsopenblascpp
6.69 score 6 stars 3 dependents 30 scripts 383 downloadsCOAP - High-Dimensional Covariate-Augmented Overdispersed Poisson Factor Model
A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. More details can be referred to Liu et al. (2024) <doi:10.1093/biomtc/ujae031>.
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4.18 score 1 dependents 10 scripts 278 downloadscoFAST - Spatially-Aware Cell Clustering Algorithm with Cluster Significant Assessment
A spatially-aware cell clustering algorithm is provided with cluster significance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.
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4.00 score 150 downloadsSpaCOAP - High-Dimensional Spatial Covariate-Augmented Overdispersed Poisson Factor Model
A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates.
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4.00 score 165 downloadsCMGFM - Interpretable Multi-Omics Representation Learning via Covariate-Augumented Generalized Factor Model
Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.
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3.70 score 1 stars 594 downloadsMultiCOAP - High-Dimensional Covariate-Augmented Overdispersed Multi-Study Poisson Factor Model
We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.
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3.18 score 1 dependents 3 scripts 161 downloadsMultiRFM - High-Dimensional Multi-Study Robust Factor Model
We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) <doi:10.48550/arXiv.2506.18478>.
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2.70 score 171 downloadsRMFM - Robust Matrix Factor Model
We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.
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2.00 score 1 stars 171 downloads