Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer
Accurate annotation of gene function in individual samples and even in each cell type is essential for understanding the pathogenesis of cancers. Single-cell RNA-sequencing (scRNA-seq) provides unprecedented resolution to decipher gene function. In order to explore how scRNA-seq contributes to the u...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025005985 |
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| author | Huating Yuan Xin Liang Xinxin Zhang Yu Cao |
| author_facet | Huating Yuan Xin Liang Xinxin Zhang Yu Cao |
| author_sort | Huating Yuan |
| collection | DOAJ |
| description | Accurate annotation of gene function in individual samples and even in each cell type is essential for understanding the pathogenesis of cancers. Single-cell RNA-sequencing (scRNA-seq) provides unprecedented resolution to decipher gene function. In order to explore how scRNA-seq contributes to the understanding of gene function in cancers, we constructed an assessment framework based on co-expression network and neighbor-voting method using 116,814 cells. Compared with bulk transcriptome, scRNA-seq recalled more experimentally verified gene functions. Surprisingly, scRNA-seq revealed cell-type-specific functions, especially in immune cells, whose expression profile recalled immune-related functions that were not discovered in cancer cells. Furthermore, scRNA-seq discovered sample-specific functions, highlighting that it provided sample-specific information. We also explored factors affecting the performance of gene function prediction. We found that 500 or more cells should be considered in the prediction with scRNA-seq, and that scRNA-seq datasets generated from 10x Genomics platform had a better performance than those from Smart-seq2. Collectively, we compared the prediction performance of bulk data and scRNA-seq data from multiple perspectives, revealing the irreplaceable role of single-cell sequencing in decoding the biological progresses in which the gene involved. |
| format | Article |
| id | doaj-art-32fc04f1408e4b5a810a65d715a9ed3d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-32fc04f1408e4b5a810a65d715a9ed3d2025-01-31T05:12:03ZengElsevierHeliyon2405-84402025-02-01113e42218Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancerHuating Yuan0Xin Liang1Xinxin Zhang2Yu Cao3College of Biology and Engineering, Guizhou Medical University, Guiyang, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China; Corresponding author.Institute of Big Health, Guizhou Medical University, Guiyang, China; Corresponding author.Accurate annotation of gene function in individual samples and even in each cell type is essential for understanding the pathogenesis of cancers. Single-cell RNA-sequencing (scRNA-seq) provides unprecedented resolution to decipher gene function. In order to explore how scRNA-seq contributes to the understanding of gene function in cancers, we constructed an assessment framework based on co-expression network and neighbor-voting method using 116,814 cells. Compared with bulk transcriptome, scRNA-seq recalled more experimentally verified gene functions. Surprisingly, scRNA-seq revealed cell-type-specific functions, especially in immune cells, whose expression profile recalled immune-related functions that were not discovered in cancer cells. Furthermore, scRNA-seq discovered sample-specific functions, highlighting that it provided sample-specific information. We also explored factors affecting the performance of gene function prediction. We found that 500 or more cells should be considered in the prediction with scRNA-seq, and that scRNA-seq datasets generated from 10x Genomics platform had a better performance than those from Smart-seq2. Collectively, we compared the prediction performance of bulk data and scRNA-seq data from multiple perspectives, revealing the irreplaceable role of single-cell sequencing in decoding the biological progresses in which the gene involved.http://www.sciencedirect.com/science/article/pii/S2405844025005985Single-cell RNA-SequencingCancerGene functionFunctional heterogeneitySample specificityCell-type specificity |
| spellingShingle | Huating Yuan Xin Liang Xinxin Zhang Yu Cao Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer Heliyon Single-cell RNA-Sequencing Cancer Gene function Functional heterogeneity Sample specificity Cell-type specificity |
| title | Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer |
| title_full | Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer |
| title_fullStr | Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer |
| title_full_unstemmed | Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer |
| title_short | Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer |
| title_sort | single cell transcriptomes reveal cell type specific and sample specific gene function in human cancer |
| topic | Single-cell RNA-Sequencing Cancer Gene function Functional heterogeneity Sample specificity Cell-type specificity |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025005985 |
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