ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction
Abstract Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13059-023-03139-w |
| _version_ | 1797376969155805184 |
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| author | Shi-Tong Yang Xiao-Fei Zhang |
| author_facet | Shi-Tong Yang Xiao-Fei Zhang |
| author_sort | Shi-Tong Yang |
| collection | DOAJ |
| description | Abstract Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets. |
| first_indexed | 2024-03-08T19:46:15Z |
| format | Article |
| id | doaj.art-4b39ebd79c314bbc81a130787452b427 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-03-08T19:46:15Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-4b39ebd79c314bbc81a130787452b4272023-12-24T12:20:19ZengBMCGenome Biology1474-760X2023-12-0124112810.1186/s13059-023-03139-wENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression predictionShi-Tong Yang0Xiao-Fei Zhang1School of Mathematics and Statistics, Central China Normal UniversitySchool of Mathematics and Statistics, Central China Normal UniversityAbstract Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.https://doi.org/10.1186/s13059-023-03139-wSpatial transcriptomicsscRNA-seqGene expression prediction |
| spellingShingle | Shi-Tong Yang Xiao-Fei Zhang ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction Genome Biology Spatial transcriptomics scRNA-seq Gene expression prediction |
| title | ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| title_full | ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| title_fullStr | ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| title_full_unstemmed | ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| title_short | ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| title_sort | engep advancing spatial transcriptomics with accurate unmeasured gene expression prediction |
| topic | Spatial transcriptomics scRNA-seq Gene expression prediction |
| url | https://doi.org/10.1186/s13059-023-03139-w |
| work_keys_str_mv | AT shitongyang engepadvancingspatialtranscriptomicswithaccurateunmeasuredgeneexpressionprediction AT xiaofeizhang engepadvancingspatialtranscriptomicswithaccurateunmeasuredgeneexpressionprediction |