Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex Article Swipe
Louise A. Huuki-Myers
,
Kelsey D. Montgomery
,
Sang Ho Kwon
,
Sophia Cinquemani
,
Nicholas J. Eagles
,
Daianna González‐Padilla
,
Sean K. Maden
,
Joel E. Kleinman
,
Thomas M. Hyde
,
Stephanie C. Hicks
,
Kristen R. Maynard
,
Leonardo Collado‐Torres
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.09.579665
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.02.09.579665
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
Related Topics
Concepts
Deconvolution
RNA
Computational biology
Biology
RNA-Seq
Cell type
Dorsolateral prefrontal cortex
Gene expression
Algorithm
Gene
Transcriptome
Cell
Prefrontal cortex
Computer science
Genetics
Neuroscience
Cognition
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.02.09.579665
- https://www.biorxiv.org/content/biorxiv/early/2024/04/07/2024.02.09.579665.full.pdf
- OA Status
- green
- Cited By
- 9
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391756636
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391756636Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2024.02.09.579665Digital Object Identifier
- Title
-
Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortexWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-12Full publication date if available
- Authors
-
Louise A. Huuki-Myers, Kelsey D. Montgomery, Sang Ho Kwon, Sophia Cinquemani, Nicholas J. Eagles, Daianna González‐Padilla, Sean K. Maden, Joel E. Kleinman, Thomas M. Hyde, Stephanie C. Hicks, Kristen R. Maynard, Leonardo Collado‐TorresList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.02.09.579665Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/04/07/2024.02.09.579665.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/04/07/2024.02.09.579665.full.pdfDirect OA link when available
- Concepts
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Deconvolution, RNA, Computational biology, Biology, RNA-Seq, Cell type, Dorsolateral prefrontal cortex, Gene expression, Algorithm, Gene, Transcriptome, Cell, Prefrontal cortex, Computer science, Genetics, Neuroscience, CognitionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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78Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.were | 181, 190, 200, 205 |
| abstract_inverted_index.with | 68, 115 |
| abstract_inverted_index.(mRNA | 108 |
| abstract_inverted_index.DLPFC | 184 |
| abstract_inverted_index.RNA), | 103 |
| abstract_inverted_index.Ratio | 161 |
| abstract_inverted_index.broad | 156 |
| abstract_inverted_index.data, | 45, 48 |
| abstract_inverted_index.data. | 52 |
| abstract_inverted_index.genes | 224 |
| abstract_inverted_index.human | 32, 130 |
| abstract_inverted_index.major | 54 |
| abstract_inverted_index.types | 107, 212 |
| abstract_inverted_index.using | 9 |
| abstract_inverted_index.whole | 101 |
| abstract_inverted_index.Assays | 139 |
| abstract_inverted_index.Bisque | 197 |
| abstract_inverted_index.across | 93, 227 |
| abstract_inverted_index.brain. | 33 |
| abstract_inverted_index.cortex | 133 |
| abstract_inverted_index.genes. | 176 |
| abstract_inverted_index.impact | 237 |
| abstract_inverted_index.marker | 175, 223 |
| abstract_inverted_index.nuclei | 13 |
| abstract_inverted_index.robust | 206 |
| abstract_inverted_index.showed | 218 |
| abstract_inverted_index.single | 10, 117 |
| abstract_inverted_index.tissue | 83, 137 |
| abstract_inverted_index.types. | 158 |
| abstract_inverted_index.(DLPFC) | 134 |
| abstract_inverted_index.(across | 146 |
| abstract_inverted_index.RNA-seq | 14, 145, 149 |
| abstract_inverted_index.Results | 121 |
| abstract_inverted_index.against | 43 |
| abstract_inverted_index.blocks. | 138 |
| abstract_inverted_index.current | 241 |
| abstract_inverted_index.dataset | 125, 217 |
| abstract_inverted_index.library | 105, 211 |
| abstract_inverted_index.matched | 116 |
| abstract_inverted_index.method, | 162 |
| abstract_inverted_index.methods | 35, 97 |
| abstract_inverted_index.sample. | 84 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Cellular | 2 |
| abstract_inverted_index.accuracy | 239 |
| abstract_inverted_index.accurate | 203 |
| abstract_inverted_index.compared | 191 |
| abstract_inverted_index.datasets | 67 |
| abstract_inverted_index.improved | 58 |
| abstract_inverted_index.included | 140 |
| abstract_inverted_index.methods, | 204 |
| abstract_inverted_index.methods. | 243 |
| abstract_inverted_index.nuclear, | 99 |
| abstract_inverted_index.package, | 168 |
| abstract_inverted_index.strategy | 21 |
| abstract_inverted_index.tissues, | 29 |
| abstract_inverted_index.(RNA-seq) | 7 |
| abstract_inverted_index.algorithm | 86 |
| abstract_inverted_index.datasets. | 120 |
| abstract_inverted_index.developed | 40, 170 |
| abstract_inverted_index.different | 94, 104 |
| abstract_inverted_index.estimates | 75 |
| abstract_inverted_index.evaluated | 92, 182 |
| abstract_inverted_index.generated | 127 |
| abstract_inverted_index.important | 20 |
| abstract_inverted_index.predicted | 186 |
| abstract_inverted_index.reference | 16, 51, 119, 235 |
| abstract_inverted_index.ribosomal | 111 |
| abstract_inverted_index.selecting | 172 |
| abstract_inverted_index.simulated | 44 |
| abstract_inverted_index.snRNA-seq | 236 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.algorithms | 60, 180 |
| abstract_inverted_index.developing | 57 |
| abstract_inverted_index.enrichment | 109 |
| abstract_inverted_index.estimating | 23 |
| abstract_inverted_index.expression | 73 |
| abstract_inverted_index.extraction | 96 |
| abstract_inverted_index.integrated | 66 |
| abstract_inverted_index.libraries, | 229 |
| abstract_inverted_index.limitation | 55 |
| abstract_inverted_index.orthogonal | 69, 193 |
| abstract_inverted_index.postmortem | 129 |
| abstract_inverted_index.prefrontal | 132 |
| abstract_inverted_index.quantified | 226 |
| abstract_inverted_index.snRNA-seq, | 143 |
| abstract_inverted_index.(cytosolic, | 98 |
| abstract_inverted_index.Conclusions | 196 |
| abstract_inverted_index.RNAScope/IF | 194 |
| abstract_inverted_index.benchmarked | 42 |
| abstract_inverted_index.composition | 26, 232 |
| abstract_inverted_index.depletion), | 113 |
| abstract_inverted_index.differences | 208 |
| abstract_inverted_index.implemented | 163 |
| abstract_inverted_index.multi-assay | 124, 216 |
| abstract_inverted_index.performance | 87 |
| abstract_inverted_index.preparation | 106 |
| abstract_inverted_index.proportions | 79, 189 |
| abstract_inverted_index.variability | 233 |
| abstract_inverted_index.differences, | 222 |
| abstract_inverted_index.dorsolateral | 131 |
| abstract_inverted_index.extractions. | 214 |
| abstract_inverted_index.measurements | 70 |
| abstract_inverted_index.pseudobulked | 46 |
| abstract_inverted_index.sc/snRNA-seq | 47 |
| abstract_inverted_index.(RNAScope/IF) | 153 |
| abstract_inverted_index.Computational | 34 |
| abstract_inverted_index.Deconvolution | 85 |
| abstract_inverted_index.computational | 178 |
| abstract_inverted_index.deconvolution | 3, 37, 59, 179, 242 |
| abstract_inverted_index.heterogeneous | 28 |
| abstract_inverted_index.measurements. | 195 |
| abstract_inverted_index.(sc/snRNA-seq) | 15 |
| abstract_inverted_index.DeconvoBuddies | 166 |
| abstract_inverted_index.RNA-sequencing | 6 |
| abstract_inverted_index.combinations), | 150 |
| abstract_inverted_index.differentially | 225 |
| abstract_inverted_index.transcriptomics, | 142 |
| abstract_inverted_index.library/extraction | 148 |
| abstract_inverted_index.spatially-resolved | 141 |
| abstract_inverted_index.immunohistochemistry | 50 |
| abstract_inverted_index.RNAScope/Immunofluorescence | 152 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5040039946, https://openalex.org/A5075979341 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I145311948, https://openalex.org/I2799853436, https://openalex.org/I4210151493 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.90648555 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |