FeatPCA: A feature subspace based principal component analysis technique for enhancing clustering of single-cell RNA-seq data
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| Format: | Preprint |
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2025
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| _version_ | 1866916605680680960 |
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| author | Islam, Md Romizul Shatabda, Swakkhar |
| author_facet | Islam, Md Romizul Shatabda, Swakkhar |
| contents | Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the cellular level. By providing data on gene expression for each individual cell, scRNA-seq generates large datasets with thousands of genes. However, handling such high-dimensional data poses computational challenges due to increased complexity. Dimensionality reduction becomes crucial for scRNA-seq analysis. Various dimensionality reduction algorithms, including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-Distributed Stochastic Neighbor Embedding (t-SNE), are commonly used to address this challenge. These methods transform the original high-dimensional data into a lower-dimensional representation while preserving relevant information. In this paper we propose {\methodname}. Instead of applying dimensionality reduction directly to the entire dataset, we divide it into multiple subspaces. Within each subspace, we apply dimension reduction techniques, and then merge the reduced data. {\methodname} offers four variations for subspacing. Our experimental results demonstrate that clustering based on subspacing yields better accuracy than working with the full dataset. Across a variety of scRNA-seq datasets, {\methodname} consistently outperforms existing state-of-the-art clustering tools. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_05647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FeatPCA: A feature subspace based principal component analysis technique for enhancing clustering of single-cell RNA-seq data Islam, Md Romizul Shatabda, Swakkhar Computational Complexity Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the cellular level. By providing data on gene expression for each individual cell, scRNA-seq generates large datasets with thousands of genes. However, handling such high-dimensional data poses computational challenges due to increased complexity. Dimensionality reduction becomes crucial for scRNA-seq analysis. Various dimensionality reduction algorithms, including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-Distributed Stochastic Neighbor Embedding (t-SNE), are commonly used to address this challenge. These methods transform the original high-dimensional data into a lower-dimensional representation while preserving relevant information. In this paper we propose {\methodname}. Instead of applying dimensionality reduction directly to the entire dataset, we divide it into multiple subspaces. Within each subspace, we apply dimension reduction techniques, and then merge the reduced data. {\methodname} offers four variations for subspacing. Our experimental results demonstrate that clustering based on subspacing yields better accuracy than working with the full dataset. Across a variety of scRNA-seq datasets, {\methodname} consistently outperforms existing state-of-the-art clustering tools. |
| title | FeatPCA: A feature subspace based principal component analysis technique for enhancing clustering of single-cell RNA-seq data |
| topic | Computational Complexity |
| url | https://arxiv.org/abs/2502.05647 |