Comprehensive Evaluation of Human Brain Gene Expression Deconvolution Methods
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE175772 | GEO | 2021/08/28
REPOSITORIES: GEO
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Project description:Comprehensive Evaluation of Human Brain Gene Expression Deconvolution Methods
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Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220607 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
2023-04-28 | GSE220651 | GEO
Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
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Project description:RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key healthy and disease functions. However, the use of these technologies is challenging because of their relatively high costs and exacting sample collection requirements. Computational deconvolution methods that infer the composition of RNA-Seq-profiled samples using scnRNA-Seq-characterized cell types can expand the benefit of these technologies, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known compositions or based on concurrent RNA-Seq and scnRNA-Seq profiles. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and a dampened weighted least squares deconvolution approach to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Moreover, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subtypes in pediatric acute myeloid leukemia and neuroblastoma datasets.
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Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.
2024-01-12 | PXD044157 | Pride
Project description:The project aimed to investigate the possibility to use proteomics data to deconvolute cell line proportions in mixed samples. Samples containing either HEK 293, Caco-2, or A549 cells and mixtures of the three cell lines was analysed using the total protein approach. This was then used for proteomics informed deconvolution. The results show that proteome deconvolution provides an effective tool for investigating cellular composition in mixed samples. This was later applied also to in silico mixtures of primary human liver cells and liver tissue. However, those data are presented elsewhere.
2023-10-24 | PXD027282 | Pride
Project description:Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes [cell mixtures bulk]
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Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. Its ability to discriminate related human cells is unknown. Here we test the ability of this technique to determine the fractions of transformed cells of immune origin in mixed samples. Experiment Overall Design: Four immune cell lines were grown and run on microarrays either by themselves or in mixtures of various relative proportions. Mixtures of cells were performed in triplicate. Experiment Overall Design: MixA (Jurkat: 2.5, IM-9: 1.25, Raji: 2.5, THP-1: 3.75) Experiment Overall Design: MixB (Jurkat: 0.5, IM-9: 3.17, Raji: 4.75, THP-1: 1.58) Experiment Overall Design: MixC (Jurkat: 0.1, IM-9: 4.95, Raji: 1.65, THP-1: 3.3) Experiment Overall Design: MixD (Jurkat: 0.02, IM-9: 3.33, Raji: 3.33, THP-1: 3.33)
2009-07-19 | E-GEOD-11058 | biostudies-arrayexpress
Project description:The cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell-type composition and compared its performance to the performance of two state-of-the-art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-type-specific gene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue .
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