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Article ; Online: A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data.

Hulot, Audrey / Laloë, Denis / Jaffrézic, Florence

2021 Volume 22, Issue 1, Page(s) 392

Abstract: Background: Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are ... ...

Abstract	Background: Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. Results: To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. Conclusion: Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.
MeSH term(s)	Animals ; Cluster Analysis ; Computational Biology ; Computer Simulation ; Humans ; Mice ; Neoplasm Recurrence, Local ; Proteins
Chemical Substances	Proteins
Language	English
Publishing date	2021-08-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04303-4
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Article ; Online: Fast tree aggregation for consensus hierarchical clustering.

Hulot, Audrey / Chiquet, Julien / Jaffrézic, Florence / Rigaill, Guillem

BMC bioinformatics

2020 Volume 21, Issue 1, Page(s) 120

Abstract: Background: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the ... ...

Abstract	Background: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. Results: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text], n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. Conclusions: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.
MeSH term(s)	Algorithms ; Cluster Analysis ; Gene Expression Profiling ; Humans ; Proteomics
Language	English
Publishing date	2020-03-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-020-3453-6
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Article ; Online: A unified framework for the integration of multiple hierarchical clusterings or networks from multi-source data

Audrey Hulot / Denis Laloë / Florence Jaffrézic

BMC Bioinformatics, Vol 22, Iss 1, Pp 1-

2021 Volume 20

Abstract: Abstract Background Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are ...

Abstract	Abstract Background Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables. However, data types are generally heterogeneous: it is a common place to gather data in the form of trees, networks or factorial maps, as these representations all have an appealing visual interpretation that helps to study grouping patterns and interactions between entities. The question we aim to answer in this paper is that of the integration of such representations. Results To this end, we provide a simple procedure to compare data with various types, in particular trees or networks, that relies essentially on two steps: the first step projects the representations into a common coordinate system; the second step then uses a multi-table integration approach to compare the projected data. We rely on efficient and well-known methodologies for each step: the projection step is achieved by retrieving a distance matrix for each representation form and then applying multidimensional scaling to provide a new set of coordinates from all the pairwise distances. The integration step is then achieved by applying a multiple factor analysis to the multiple tables of the new coordinates. This procedure provides tools to integrate and compare data available, for instance, as tree or network structures. Our approach is complementary to kernel methods, traditionally used to answer the same question. Conclusion Our approach is evaluated on simulation and used to analyze two real-world data sets: first, we compare several clusterings for different cell-types obtained from a transcriptomics single-cell data set in mouse embryos; second, we use our procedure to aggregate a multi-table data set from the TCGA breast cancer database, in order to compare several protein networks inferred for different breast cancer subtypes.
Keywords	Data integration ; Clustering ; Network ; MDS ; MFA ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
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Article ; Online: Assessment of the zoonotic potential of atypical scrapie prions in humanized mice reveals rare phenotypic convergence but not identity with sporadic CJD prions.

Marín-Moreno, Alba / Reine, Fabienne / Herzog, Laetitia / Aron, Naima / Jaffrézic, Florence / Vilotte, Jean-Luc / Rezaei, Human / Andréoletti, Olivier / Martin, Davy / Béringue, Vincent

The Journal of infectious diseases

2024

Abstract: Background: Atypical/Nor98 scrapie (AS) is an idiopathic infectious prion disease affecting sheep and goats. Recent findings suggest that zoonotic prions from bovine spongiform encephalopathy (C-BSE) may co-propagate with atypical/Nor98 prions in AS ... ...

Abstract	Background: Atypical/Nor98 scrapie (AS) is an idiopathic infectious prion disease affecting sheep and goats. Recent findings suggest that zoonotic prions from bovine spongiform encephalopathy (C-BSE) may co-propagate with atypical/Nor98 prions in AS sheep brains. Investigating the risk AS poses to humans is crucial. Methods: To assess the risk of sheep/goat-to-human transmission of AS, we serially inoculated brain tissue from field and laboratory isolates into transgenic mice overexpressing human prion protein (Met129 allele). We studied clinical outcomes as well as presence of prions in brains and spleens. Results: No transmission occurred on the primary passage, with no clinical disease or pathological prion protein in brains and spleens. On subsequent passages, one isolate gradually adapted, manifesting as prions with a phenotype resembling those causing MM1-type sporadic Creutzfeldt-Jakob disease in humans. However, further characterization using in vivo and in vitro techniques confirmed both prion agents as different strains, revealing a case of phenotypic convergence. Importantly, no C-BSE prions emerged in these mice, especially in the spleen, which is more permissive than the brain for C-BSE cross-species transmission. Conclusions: The results obtained suggest a low the zoonotic for AS. Rare adaptation may allow the emergence of prions phenotypically resembling those spontaneously forming in humans.
Language	English
Publishing date	2024-02-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	3019-3
ISSN	1537-6613 ; 0022-1899
ISSN (online)	1537-6613
ISSN	0022-1899
DOI	10.1093/infdis/jiae093
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Article ; Online: Multi-omics data integration for the identification of biomarkers for bull fertility.

Costes, Valentin / Sellem, Eli / Marthey, Sylvain / Hoze, Chris / Bonnet, Aurélie / Schibler, Laurent / Kiefer, Hélène / Jaffrezic, Florence

PloS one

2024 Volume 19, Issue 2, Page(s) e0298623

Abstract: Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, ... ...

Abstract	Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.
MeSH term(s)	Male ; Cattle ; Animals ; Humans ; Semen/physiology ; Multiomics ; Fertility/genetics ; Spermatozoa/physiology ; Semen Analysis ; Infertility ; Biomarkers
Chemical Substances	Biomarkers
Language	English
Publishing date	2024-02-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0298623
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Article ; Online: Fast tree aggregation for consensus hierarchical clustering

Audrey Hulot / Julien Chiquet / Florence Jaffrézic / Guillem Rigaill

BMC Bioinformatics, Vol 21, Iss 1, Pp 1-

2020 Volume 12

Abstract: Abstract Background In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in ...

Abstract	Abstract Background In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. Results We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be O(nqlog(n)) $\mathcal {O}(nq\log (n))$, n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product. Conclusions Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.
Keywords	Hierarchical clustering ; Data integration ; Unsupervised learning ; Consensus clustering ; Omics ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2020-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
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Article ; Online: No-contact microchip measurements of body temperature and behavioural changes prior to foaling.

Auclair-Ronzaud, Juliette / Jousset, Tristan / Dubois, Cédric / Wimel, Laurence / Jaffrézic, Florence / Chavatte-Palmer, Pascale

Theriogenology

2020 Volume 157, Page(s) 399–406

Abstract: Gestational length is highly variable in horses ranging from 320 to 360 days. Thus, determining parturition time is an important challenge for the horse industry. Body temperature can be used in cows and ewes as an indicator of parturition. Thus, the aim ...

Abstract	Gestational length is highly variable in horses ranging from 320 to 360 days. Thus, determining parturition time is an important challenge for the horse industry. Body temperature can be used in cows and ewes as an indicator of parturition. Thus, the aim of this study is to determine if temperature can also be used as indicator of foaling. Thirty-nine mares were monitored over two foaling seasons (2018 and 2019). They were housed in 16 m
MeSH term(s)	Animals ; Body Temperature ; Cattle ; Female ; Horses ; Parturition ; Pregnancy ; Sheep ; Temperature
Language	English
Publishing date	2020-08-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	189232-0
ISSN	1879-3231 ; 0093-691X
ISSN (online)	1879-3231
ISSN	0093-691X
DOI	10.1016/j.theriogenology.2020.08.004
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Article ; Online: Potential genetic robustness of Prnp and Sprn double knockout mouse embryos towards ShRNA-lentiviral inoculation.

Rau, Andrea / Passet, Bruno / Castille, Johan / Daniel-Carlier, Nathalie / Asset, Alexandre / Lecardonnel, Jérome / Moroldo, Marco / Jaffrézic, Florence / Laloë, Denis / Moazami-Goudarzi, Katayoun / Vilotte, Jean-Luc

Veterinary research

2022 Volume 53, Issue 1, Page(s) 54

Abstract: The Shadoo and PrP prion protein family members are thought to be functionally related, but previous knockdown/knockout experiments in early mouse embryogenesis have provided seemingly contradictory results. In particular, Shadoo was found to be ... ...

Abstract	The Shadoo and PrP prion protein family members are thought to be functionally related, but previous knockdown/knockout experiments in early mouse embryogenesis have provided seemingly contradictory results. In particular, Shadoo was found to be indispensable in the absence of PrP in knockdown analyses, but a double-knockout of the two had little phenotypic impact. We investigated this apparent discrepancy by comparing transcriptomes of WT, Prnp
MeSH term(s)	Animals ; Mice ; Mice, Knockout ; Nerve Tissue Proteins/genetics ; Nerve Tissue Proteins/metabolism ; Prion Proteins/genetics ; Prions/genetics ; RNA, Small Interfering ; Recombinant Proteins ; Transcription Factors
Chemical Substances	Nerve Tissue Proteins ; Prion Proteins ; Prions ; Prnp protein, leucyl(101), mouse ; Prnp protein, mouse ; RNA, Small Interfering ; Recombinant Proteins ; Transcription Factors
Language	English
Publishing date	2022-07-07
Publishing country	England
Document type	Journal Article
ZDB-ID	1146298-x
ISSN	1297-9716 ; 0928-4249
ISSN (online)	1297-9716
ISSN	0928-4249
DOI	10.1186/s13567-022-01075-4
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Article ; Online: Mutation of SOCS2 induces structural and functional changes in mammary development.

Ivanova, Elitsa / Hue-Beauvais, Cathy / Castille, Johan / Laubier, Johann / Le Guillou, Sandrine / Aujean, Etienne / Lecardonnel, Jerome / Lebrun, Laura / Jaffrezic, Florence / Rousseau-Ralliard, Delphine / Péchoux, Christine / Letheule, Martine / Foucras, Gilles / Charlier, Madia / Le Provost, Fabienne

Development (Cambridge, England)

2024 Volume 151, Issue 6

Abstract: Lactation is an essential process for mammals. In sheep, the R96C mutation in suppressor of cytokine signaling 2 (SOCS2) protein is associated with greater milk production and increased mastitis sensitivity. To shed light on the involvement of R96C ... ...

Abstract	Lactation is an essential process for mammals. In sheep, the R96C mutation in suppressor of cytokine signaling 2 (SOCS2) protein is associated with greater milk production and increased mastitis sensitivity. To shed light on the involvement of R96C mutation in mammary gland development and lactation, we developed a mouse model carrying this mutation (SOCS2KI/KI). Mammary glands from virgin adult SOCS2KI/KI mice presented a branching defect and less epithelial tissue, which were not compensated for in later stages of mammary development. Mammary epithelial cell (MEC) subpopulations were modified, with mutated mice having three times as many basal cells, accompanied by a decrease in luminal cells. The SOCS2KI/KI mammary gland remained functional; however, MECs contained more lipid droplets versus fat globules, and milk lipid composition was modified. Moreover, the gene expression dynamic from virgin to pregnancy state resulted in the identification of about 3000 differentially expressed genes specific to SOCS2KI/KI or control mice. Our results show that SOCS2 is important for mammary gland development and milk production. In the long term, this finding raises the possibility of ensuring adequate milk production without compromising animal health and welfare.
MeSH term(s)	Animals ; Female ; Mice ; Pregnancy ; Epithelial Cells/metabolism ; Lactation/genetics ; Mammary Glands, Animal/metabolism ; Milk/metabolism ; Mutation/genetics
Chemical Substances	Socs2 protein, mouse
Language	English
Publishing date	2024-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	90607-4
ISSN	1477-9129 ; 0950-1991
ISSN (online)	1477-9129
ISSN	0950-1991
DOI	10.1242/dev.202332
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Article ; Online: Identification of marginal causal relationships in gene networks from observational and interventional expression data.

Monneret, Gilles / Jaffrézic, Florence / Rau, Andrea / Zerjal, Tatiana / Nuel, Grégory

PloS one

2017 Volume 12, Issue 3, Page(s) e0171142

Abstract: Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small ... ...

Abstract	Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub).
MeSH term(s)	Gene Expression ; Gene Knockout Techniques ; Gene Regulatory Networks ; Models, Theoretical ; Reproducibility of Results
Language	English
Publishing date	2017-03-16
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0171142
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