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1. SSA: a novel method for Single-cell and Spatial transcriptomics Alignment

   https://doi.org/10.29007/9cr1  

   Tran, Bang; Tran, Dao; Nguyen, Tin (July 2024, EPiC Series in Computing)

   Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcrip- tomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spa- tial locations in actual tissue based on their expression profiles. SSA has demonstrated su- perior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression. 

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2. DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data

   https://doi.org/10.3390/biom14070766  

   Zhou, Xiguo; Pan, Jingyi; Chen, Liang; Zhang, Shaoqiang; Chen, Yong (July 2024, Biomolecules)

   Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types. 

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3. Domain adaptation for supervised integration of scRNA-seq data

   https://doi.org/10.1038/s42003-023-04668-7  

   Sun, Yutong; Qiu, Peng (December 2023, Communications Biology)

   Abstract Large-scale scRNA-seq studies typically generate data in batches, which often induce nontrivial batch effects that need to be corrected. Given the global efforts for building cell atlases and the increasing number of annotated scRNA-seq datasets accumulated, we propose a supervised strategy for scRNA-seq data integration called SIDA ( S upervised I ntegration using D omain A daptation), which uses the cell type annotations to guide the integration of diverse batches. The supervised strategy is based on domain adaptation that was initially proposed in the computer vision field. We demonstrate that SIDA is able to generate comprehensive reference datasets that lead to improved accuracy in automated cell type mapping analyses. 

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4. A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data

   https://doi.org/10.1093/pcmedi/pbaf011  

   Chen, Xin; Fang, Li Tai; Chen, Zhong; Chen, Wanqiu; Wu, Hongjin; Zhu, Bin; Moos, Malcolm; Farmer, Andrew; Zhang, Xiaowen; Xiong, Wei; et al (April 2025, Precision Clinical Medicine)

   Abstract BackgroundSingle-cell RNA-sequencing (scRNA-seq) has emerged as a powerful tool for cancer research, enabling in-depth characterization of tumor heterogeneity at the single-cell level. Recently, several scRNA-seq copy number variation (scCNV) inference methods have been developed, expanding the application of scRNA-seq to study genetic heterogeneity in cancer using transcriptomic data. However, the fidelity of these methods has not been investigated systematically. MethodsWe benchmarked five commonly used scCNV inference methods: HoneyBADGER, CopyKAT, CaSpER, inferCNV, and sciCNV. We evaluated their performance across four different scRNA-seq platforms using data from our previous multicenter study. We evaluated scCNV performance further using scRNA-seq datasets derived from mixed samples consisting of five human lung adenocarcinoma cell lines and also sequenced tissues from a small cell lung cancer patient and used the data to validate our findings with a clinical scRNA-seq dataset. ResultsWe found that the sensitivity and specificity of the five scCNV inference methods varied, depending on the selection of reference data, sequencing depth, and read length. CopyKAT and CaSpER outperformed other methods overall, while inferCNV, sciCNV, and CopyKAT performed better than other methods in subclone identification. We found that batch effects significantly affected the performance of subclone identification in mixed datasets in most methods we tested. ConclusionOur benchmarking study revealed the strengths and weaknesses of each of these scCNV inference methods and provided guidance for selecting the optimal CNV inference method using scRNA-seq data. 

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5. Random Projection Methods Outperform Principal Component Analysis for Dimensionality Reduction in Single Cell RNA-Seq

   https://doi.org/10.1177/15578666261436821  

   Abdelnaby, Mohamed; Moussa, Marmar R (April 2026, Journal of Computational Biology)

   Principal component analysis (PCA) is one of the most frequently used dimensionality reduction methods for high-dimensional datasets, especially single-cell RNA sequencing (scRNA-seq). Despite its popularity, PCA faces challenges, particularly related to its performance degrading as the dataset size increases. Additionally, PCA is sensitive to outliers and assumes linearity. Random projection (RP) methods have emerged as a promising alternative to address several of PCA’s limitations. In this study, we conduct a systematic and comprehensive evaluation of PCA and RP methods, including singular value decomposition (SVD) and randomized SVD approaches, against multiple RP methods including sparse random projection, Gaussian random projection, and we introduce a Matching Sparsity Random Projection algorithm that adaptively calibrates projection matrix density according to input data sparsity patterns, emphasizing both computational scalability and effectiveness in downstream analytical tasks. We evaluated these methods on multiple publicly available scRNA-seq datasets that include both labeled and unlabeled scenarios. Clustering performance is assessed using Hierarchical Clustering and Spherical K-Means algorithms, with labeled datasets evaluated through Hungarian algorithm accuracy and Mutual Information metrics. For unlabeled datasets, we used the Dunn Index and Gap Statistic to quantify cluster separation quality. Across both dataset types, the Within-Cluster Sum of Squares metric is used to assess variability. Moreover, locality preservation is examined, with RP methods, including our adaptive sparsity approach, outperforming PCA in several of the evaluated metrics. Our experimental results show that RP methods not only deliver substantial computational speed improvements over PCA but also rival, and in some cases, exceed PCA in preserving data variability and clustering quality. Through this comprehensive methodological comparison, our work provides critical guidance for selecting appropriate dimensionality reduction strategies that effectively balance computational demands, scalability requirements, and analytical quality in downstream analyses. 

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