Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation
Processing biological data is a challenge of paramount importance as the amount of accumulated data has been annually increasing along with the emergence of new methods for studying biological objects. Blind application of mathematical methods in biology may lead to erroneous hypotheses and conclusi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1519468/full |
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| author | Mikhail Arbatsky Ekaterina Vasilyeva Veronika Sysoeva Ekaterina Semina Ekaterina Semina Valeri Saveliev Kseniya Rubina |
| author_facet | Mikhail Arbatsky Ekaterina Vasilyeva Veronika Sysoeva Ekaterina Semina Ekaterina Semina Valeri Saveliev Kseniya Rubina |
| author_sort | Mikhail Arbatsky |
| collection | DOAJ |
| description | Processing biological data is a challenge of paramount importance as the amount of accumulated data has been annually increasing along with the emergence of new methods for studying biological objects. Blind application of mathematical methods in biology may lead to erroneous hypotheses and conclusions. Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. The justification for applying these methods in biology is not discussed in methodological articles. The essential aspect of dimensionality reduction is to identify the stable patterns which are deliberately removed upon mathematical data processing as being redundant, albeit containing important minor details for biological interpretation. There are no established rules for integration of datasets obtained at different sampling times or conditions. Clustering calls for reconsidering its application specifically for biological data processing. The novelty of the present study lies in an integrated approach of biology and bioinformatics to elucidate biological insights upon data processing. |
| format | Article |
| id | doaj-art-57378452de2d4057b43b6b447022621b |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-57378452de2d4057b43b6b447022621b2025-02-12T07:26:08ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-02-01510.3389/fbinf.2025.15194681519468Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretationMikhail Arbatsky0Ekaterina Vasilyeva1Veronika Sysoeva2Ekaterina Semina3Ekaterina Semina4Valeri Saveliev5Kseniya Rubina6Faculty of Medicine, Lomonosov Moscow State University, Moscow, RussiaInstitute of Higher Technologies, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaFaculty of Medicine, Lomonosov Moscow State University, Moscow, RussiaFaculty of Medicine, Lomonosov Moscow State University, Moscow, RussiaInstitute of Medicine and Life Science, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaInstitute of Higher Technologies, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaFaculty of Medicine, Lomonosov Moscow State University, Moscow, RussiaProcessing biological data is a challenge of paramount importance as the amount of accumulated data has been annually increasing along with the emergence of new methods for studying biological objects. Blind application of mathematical methods in biology may lead to erroneous hypotheses and conclusions. Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. The justification for applying these methods in biology is not discussed in methodological articles. The essential aspect of dimensionality reduction is to identify the stable patterns which are deliberately removed upon mathematical data processing as being redundant, albeit containing important minor details for biological interpretation. There are no established rules for integration of datasets obtained at different sampling times or conditions. Clustering calls for reconsidering its application specifically for biological data processing. The novelty of the present study lies in an integrated approach of biology and bioinformatics to elucidate biological insights upon data processing.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1519468/fullbiocentric mathematicsScRNA-seqdimension reductioncell clusteringdatasets integration |
| spellingShingle | Mikhail Arbatsky Ekaterina Vasilyeva Veronika Sysoeva Ekaterina Semina Ekaterina Semina Valeri Saveliev Kseniya Rubina Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation Frontiers in Bioinformatics biocentric mathematics ScRNA-seq dimension reduction cell clustering datasets integration |
| title | Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation |
| title_full | Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation |
| title_fullStr | Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation |
| title_full_unstemmed | Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation |
| title_short | Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation |
| title_sort | seurat function argument values in scrna seq data analysis potential pitfalls and refinements for biological interpretation |
| topic | biocentric mathematics ScRNA-seq dimension reduction cell clustering datasets integration |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1519468/full |
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