Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex
Abstract Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefro...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03552-3 |
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| author | Louise A. Huuki-Myers Kelsey D. Montgomery Sang Ho Kwon Sophia Cinquemani Nicholas J. Eagles Daianna Gonzalez-Padilla Sean K. Maden Joel E. Kleinman Thomas M. Hyde Stephanie C. Hicks Kristen R. Maynard Leonardo Collado-Torres |
| author_facet | Louise A. Huuki-Myers Kelsey D. Montgomery Sang Ho Kwon Sophia Cinquemani Nicholas J. Eagles Daianna Gonzalez-Padilla Sean K. Maden Joel E. Kleinman Thomas M. Hyde Stephanie C. Hicks Kristen R. Maynard Leonardo Collado-Torres |
| author_sort | Louise A. Huuki-Myers |
| collection | DOAJ |
| description | Abstract Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package. |
| format | Article |
| id | doaj-art-1619e61dabde4e159e2ba7aea247a097 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-1619e61dabde4e159e2ba7aea247a0972025-08-20T02:12:02ZengBMCGenome Biology1474-760X2025-04-0126113510.1186/s13059-025-03552-3Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortexLouise A. Huuki-Myers0Kelsey D. Montgomery1Sang Ho Kwon2Sophia Cinquemani3Nicholas J. Eagles4Daianna Gonzalez-Padilla5Sean K. Maden6Joel E. Kleinman7Thomas M. Hyde8Stephanie C. Hicks9Kristen R. Maynard10Leonardo Collado-Torres11Lieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthLieber Institute for Brain Development, Johns Hopkins Medical CampusLieber Institute for Brain Development, Johns Hopkins Medical CampusAbstract Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package.https://doi.org/10.1186/s13059-025-03552-3DeconvolutionTranscriptomicsRNA-seqsnRNA-seqsmFISHRNAScope |
| spellingShingle | Louise A. Huuki-Myers Kelsey D. Montgomery Sang Ho Kwon Sophia Cinquemani Nicholas J. Eagles Daianna Gonzalez-Padilla Sean K. Maden Joel E. Kleinman Thomas M. Hyde Stephanie C. Hicks Kristen R. Maynard Leonardo Collado-Torres Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex Genome Biology Deconvolution Transcriptomics RNA-seq snRNA-seq smFISH RNAScope |
| title | Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex |
| title_full | Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex |
| title_fullStr | Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex |
| title_full_unstemmed | Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex |
| title_short | Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex |
| title_sort | benchmark of cellular deconvolution methods using a multi assay dataset from postmortem human prefrontal cortex |
| topic | Deconvolution Transcriptomics RNA-seq snRNA-seq smFISH RNAScope |
| url | https://doi.org/10.1186/s13059-025-03552-3 |
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