Unraveling the complexity: understanding the deconvolutions of RNA-seq data
Abstract Deconvolution of RNA sequencing data is a computational method used to estimate the relative proportions of different cell types or subpopulations within a heterogeneous sample based on gene expression profiles. This technique is particularly useful in studies where the goal is to identify...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | Translational Medicine Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41231-023-00154-8 |
| _version_ | 1797452085897199616 |
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| author | Kavoos Momeni Saeid Ghorbian Ehsan Ahmadpour Rasoul Sharifi |
| author_facet | Kavoos Momeni Saeid Ghorbian Ehsan Ahmadpour Rasoul Sharifi |
| author_sort | Kavoos Momeni |
| collection | DOAJ |
| description | Abstract Deconvolution of RNA sequencing data is a computational method used to estimate the relative proportions of different cell types or subpopulations within a heterogeneous sample based on gene expression profiles. This technique is particularly useful in studies where the goal is to identify changes in gene expression that are specific to a particular cell type or subpopulation. The deconvolution process involves using reference gene expression profiles from known cell types or subpopulations to infer the relative abundance of these cells within a mixed sample. This is typically done using linear regression or other statistical methods to model the observed gene expression data as a linear combination of the reference profiles. Once the relative proportions of each cell type or subpopulation have been estimated, downstream analyses can be performed on each component separately, allowing for more precise identification of cell-type-specific changes in gene expression. Overall, deconvolution of RNA sequencing data is a powerful tool for dissecting complex biological systems and identifying cell-type-specific molecular signatures that may be relevant for disease diagnosis and treatment. |
| first_indexed | 2024-03-09T15:02:41Z |
| format | Article |
| id | doaj.art-1af306fd6b6148e9ac90d32a0a40f492 |
| institution | Directory Open Access Journal |
| issn | 2396-832X |
| language | English |
| last_indexed | 2024-03-09T15:02:41Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Translational Medicine Communications |
| spelling | doaj.art-1af306fd6b6148e9ac90d32a0a40f4922023-11-26T13:47:25ZengBMCTranslational Medicine Communications2396-832X2023-09-01811810.1186/s41231-023-00154-8Unraveling the complexity: understanding the deconvolutions of RNA-seq dataKavoos Momeni0Saeid Ghorbian1Ehsan Ahmadpour2Rasoul Sharifi3Department of Molecular Genetics - Ahar Branch, Islamic Azad UniversityDepartment of Molecular Genetics - Ahar Branch, Islamic Azad UniversityTabriz University of Medical SciencesDepartment of Biology, Faculty of Basic Science - Ahar Branch - Islamic, Azad UniversityAbstract Deconvolution of RNA sequencing data is a computational method used to estimate the relative proportions of different cell types or subpopulations within a heterogeneous sample based on gene expression profiles. This technique is particularly useful in studies where the goal is to identify changes in gene expression that are specific to a particular cell type or subpopulation. The deconvolution process involves using reference gene expression profiles from known cell types or subpopulations to infer the relative abundance of these cells within a mixed sample. This is typically done using linear regression or other statistical methods to model the observed gene expression data as a linear combination of the reference profiles. Once the relative proportions of each cell type or subpopulation have been estimated, downstream analyses can be performed on each component separately, allowing for more precise identification of cell-type-specific changes in gene expression. Overall, deconvolution of RNA sequencing data is a powerful tool for dissecting complex biological systems and identifying cell-type-specific molecular signatures that may be relevant for disease diagnosis and treatment.https://doi.org/10.1186/s41231-023-00154-8Deconvolution techniquesRNA-seq data analysisDifferential gene expression analysisTranscriptome profilingCIBERSORTxCell |
| spellingShingle | Kavoos Momeni Saeid Ghorbian Ehsan Ahmadpour Rasoul Sharifi Unraveling the complexity: understanding the deconvolutions of RNA-seq data Translational Medicine Communications Deconvolution techniques RNA-seq data analysis Differential gene expression analysis Transcriptome profiling CIBERSORT xCell |
| title | Unraveling the complexity: understanding the deconvolutions of RNA-seq data |
| title_full | Unraveling the complexity: understanding the deconvolutions of RNA-seq data |
| title_fullStr | Unraveling the complexity: understanding the deconvolutions of RNA-seq data |
| title_full_unstemmed | Unraveling the complexity: understanding the deconvolutions of RNA-seq data |
| title_short | Unraveling the complexity: understanding the deconvolutions of RNA-seq data |
| title_sort | unraveling the complexity understanding the deconvolutions of rna seq data |
| topic | Deconvolution techniques RNA-seq data analysis Differential gene expression analysis Transcriptome profiling CIBERSORT xCell |
| url | https://doi.org/10.1186/s41231-023-00154-8 |
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