Prediction of cell types using single-cell mRNA profiles
Конференцијски прилог (Објављена верзија)
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© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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Приказ свих података о документуАпстракт
Single cell transcriptomics is a rapidly growing area with an urgent need for new analytical
tools to complement and supersede unsupervised clustering. We defined a new method
for deriving gene expression profiles from single-cell gene expression matrices. We
named these profiles the “single-cell-derived-class” (SCDC) profiles. We developed SCDC
profiles for multiple cell types and subtypes of peripheral blood mononuclear cells (PBMC)
using the results of single cell transcriptomics (SCT) experiments. SCDC profiles represent
characteristic patterns of gene expressions of the types and subtypes of healthy human
PBMC. We studied the reproducibility of SCDC profiles, their robustness, and their
applications in classifying healthy human PBMC types and subtypes. SCDC profiles are
efficient and convenient tools for the analysis of SCT data derived from PBMC samples.
These profiles are highly reproducible, even when derived from unrelated studies,
provided that the sample processin...g steps are comparable and the same SCT technology
is used. The classification accuracy of SCDC profiles is high. SCDC profiles can be used for
supervised classification and the discovery of new subtypes of PBMC.
Кључне речи:
single-cell-derived-class / mRNA profilesИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 33-33Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Brusić, Vladimir PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1968 AB - Single cell transcriptomics is a rapidly growing area with an urgent need for new analytical tools to complement and supersede unsupervised clustering. We defined a new method for deriving gene expression profiles from single-cell gene expression matrices. We named these profiles the “single-cell-derived-class” (SCDC) profiles. We developed SCDC profiles for multiple cell types and subtypes of peripheral blood mononuclear cells (PBMC) using the results of single cell transcriptomics (SCT) experiments. SCDC profiles represent characteristic patterns of gene expressions of the types and subtypes of healthy human PBMC. We studied the reproducibility of SCDC profiles, their robustness, and their applications in classifying healthy human PBMC types and subtypes. SCDC profiles are efficient and convenient tools for the analysis of SCT data derived from PBMC samples. These profiles are highly reproducible, even when derived from unrelated studies, provided that the sample processing steps are comparable and the same SCT technology is used. The classification accuracy of SCDC profiles is high. SCDC profiles can be used for supervised classification and the discovery of new subtypes of PBMC. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Prediction of cell types using single-cell mRNA profiles EP - 33 SP - 33 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1968 ER -
@conference{ author = "Brusić, Vladimir", year = "2023", abstract = "Single cell transcriptomics is a rapidly growing area with an urgent need for new analytical tools to complement and supersede unsupervised clustering. We defined a new method for deriving gene expression profiles from single-cell gene expression matrices. We named these profiles the “single-cell-derived-class” (SCDC) profiles. We developed SCDC profiles for multiple cell types and subtypes of peripheral blood mononuclear cells (PBMC) using the results of single cell transcriptomics (SCT) experiments. SCDC profiles represent characteristic patterns of gene expressions of the types and subtypes of healthy human PBMC. We studied the reproducibility of SCDC profiles, their robustness, and their applications in classifying healthy human PBMC types and subtypes. SCDC profiles are efficient and convenient tools for the analysis of SCT data derived from PBMC samples. These profiles are highly reproducible, even when derived from unrelated studies, provided that the sample processing steps are comparable and the same SCT technology is used. The classification accuracy of SCDC profiles is high. SCDC profiles can be used for supervised classification and the discovery of new subtypes of PBMC.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Prediction of cell types using single-cell mRNA profiles", pages = "33-33", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1968" }
Brusić, V.. (2023). Prediction of cell types using single-cell mRNA profiles. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 33-33. https://hdl.handle.net/21.15107/rcub_imagine_1968
Brusić V. Prediction of cell types using single-cell mRNA profiles. in 4th Belgrade Bioinformatics Conference. 2023;4:33-33. https://hdl.handle.net/21.15107/rcub_imagine_1968 .
Brusić, Vladimir, "Prediction of cell types using single-cell mRNA profiles" in 4th Belgrade Bioinformatics Conference, 4 (2023):33-33, https://hdl.handle.net/21.15107/rcub_imagine_1968 .