dataesr/scientific-paragraphs-categorization huggingface dataset:
Input clusterings were computed with several single and multi-omic clustering methods and then were fused in a consensus clustering. Further details on the strategy implemented in ClustOmics are given in section Methods. In order to assess the benefit of this novel method, we further explored the robustness of our consensus clusterings with respect to the input clusterings, as well as their biological relevance, based on clinical and survival metadata available for each patient. We compare ClustOmics results with those of COCA  [5] , a well-known co-occurrence based consensus clustering tool that has already been used to combine multiple omic datasets to reveal cancer subtypes  [18].
Single-omic clusterings to multi-omic multi-method clustering Integration of single-omic clusterings from multiple single-omic methods Below, we analyse and compare ClustOmics and COCA consensus multi-omic clusterings (produced using the same set of input clusterings) for the two integration scenarios, on TCGA data from ten different cancer types, three omic datatypes (gene expression, miRNA expression and methylation) and various input clustering strategies (described in section Methods -Datasets and tools used for computing input clusterings). We further focus on breast cancer and analyse ClustOmics results for the Single-to-Multi scenario on the breast dataset. Results overview upon the ten cancer types for the two integration scenarios Running ClustOmics and COCA on the ten cancer datasets with respect to the different integration scenarios implies starting by computing single-and multi-omic input clusterings to group patients according to their single-and multi-omic profiles.
For the Multi-to-Multi scenario (MtoM), multi-omic input clusterings were obtained with existing multi-omic clustering tools: PINS  [6] , SNF  [7] , NEMO  [8] , rMKL  [9]  and MultiCCA  [10]  (see Table  6 ). For the Single-to-Multi (StoM) scenario, the same tools listed above were applied, except for the MultiCCA tool that can only be used in a multi-omic context and that was replaced with the simple yet robust state-of-the-art method, k-means clustering  [19]. In this scenario, the tools were applied on each omic dataset independently. Moreover, to evaluate the benefits of including patients with missing data (that were not measured for all of the three omics), two different runs were performed. In the first one, referred to as StoM OnlyMulti, only patients measured for the three omics were considered, that is patients with no missing data.
Integration of multi-omic clusterings (Multi-to-Multi scenario). Input multi-omic clusterings were computed with the five multi-omic clustering methods presented in section Methods -Datasets and tools used for computing input clusterings using default parameters and following authors' recommendations. The clusterings were produced using the multi-omic patients exclusively (those for which all three omic data are available). In order to make all input clusterings comparable, we run NEMO in the same way though, compared to the other five tools, NEMO is able to handle partial data. ClustOmics was run with the min size cluster parameter arbitrarily set to 8 nodes for all cancer types, meaning that clusters of size below 8 were removed from the consensus clustering, the corresponding individuals being reassigned to consensus clusters exceeding the size threshold.
When comparing input and ClustOmics consensus clusterings, we observe a certain consistency in terms of the number of clusters. COCA on the other hand, resulted in 2 to 3 clusters independently from the cancer type, which suggests a lower sensitivity to input clustering dissimilarities compared to ClustOmics. Two cancer datasets, COAD and LUSC, showed the lower consistency between the input predictions and clustered with a number of supports of 2. For LUSC cancer type, the consensus clustering resulted in only 2 clusters, despite the large size of the available cohort (341 individuals). The computation of the Adjusted Rand Index (ARI)  [20]  between input clusterings, a measure of similarity between partitions, showed that for these two cancer types, SNF and NEMO clusterings were very similar (with an ARI index of 0.
Integration of single-omic clusterings (Single-to-Multi scenario). To assess ClustOmics performance when fusing simultaneously input clusterings computed from different omic data and with different clustering methods, we investigated a second integration scenario, combining single-omic clusterings produced independently on each omic dataset. Overall cancer consensus results for this scenario are displayed in Figure  2 , but discussed in detail in this section. As stated above, single-omic clusterings were computed using the following five clustering tools : PINS  [6] , SNF  [7] , NEMO  [8] , rMKL  [9]  and k-means clustering  [19]  (with an optimal number of clusters computed with the Silhouette index  [24] ). To assess the benefit of including individuals with missing data (not measured for all omics), two analysis were performed for this scenario: • For StoM OnlyMulti, input clusterings were computed using exclusively multiomic patients.
Moreover, for 3 cancer types, BIC, GBM and LUSC, capitalising on all available individuals resulted in increasing the number of supports used to filter the integration graph. The largest increase in the number of supports threshold was observed for BIC, i. e. , from 7 supports in the StoM OnlyMulti up to 11 in the StoM All run. For LIHC, OV and SKCM on the other hand, we observe a decrease of, respectively, -2, -1 and -1 in the number of supports. For the other cancer types, the threshold on the number of supports is identical between the two runs. In the following of this study, we will focus on the results of the StoM All run. In this scenario, the maximum possible number of supports is 15 as the five clustering methods were run on three omic datasets for each cancer. Note that for this scenario the threshold on the number of supports used to filter the integration graph has great influence on the capacity of ClustOmics to produce consensus clusters across omics and on the interpretation of the results.
In that respect, the ARI of a consensus clustering in relation to its inputs should be maximized for a maximum of input clusterings, including for those coming from different omics. The highest similarity between consensus and input clusterings from different omic sources is observed for the SARC cancer dataset (see Figure  4 ), with ARI values ranging from 0. 4 up to 0. 9 for at least one input clustering computed from each of the three omics datasets, suggesting similar associations at different molecular levels. For the other cancer types, the agreement between omic sources is less straightforward. Interestingly, for all cancer types, COCA and ClustOmics consensus clusterings resemble to the same input clusterings (computed from the same set of omic sources), thus suggesting that some omics are more appropriate to explain molecular differences between individuals.
For the cancer datasets showing higher heterogeneity among input clusterings survival quality, ClustOmics found significant survival p-values for AML, KIRC, LIHC and SKCM, despite some low quality clusterings that were given as input. Clinical labels found enriched in consensus clusters are listed in Table  2. AML clusters were found enriched for both CALGB and FAB classifications, KIRC clusters for histologic grade and pathologic M and T (referring to the TNM classification  of tumors  [25] ), LIHC clusters for gender, age at diagnosis and fetoprotein outcome value, while SKCM clusters showed no enriched clinical parameters. While BIC consensus clustering did not show good survival-wise results, pathologic M, N and T labels were found enriched in clusters, as well as pathologic stage, histological type, PAM50 call, and estrogen and progesterone receptor status.
Study case: BIC Single-to-Multi consensus clustering In this section we focus on the consensus clustering of the 15 single-omic clusterings for the BIC dataset (five clustering methods, listed in the previous section, applied on three omic data types) and analyse these results in parallel to the PAM50 classification. As the PAM50 classification is computed from the expression of 50 specific genes, while in this work we capitalise on three different omics, a certain heterogeneity in the clusters when compared to the PAM50 prediction is expected. Moreover, this heterogeneity is to be further explored, as it could reveal subtypes that are not distinguishable when considering only PAM50 genes, but that are heterogeneous when integrating other data sources. From the 1096 patients available in BIC dataset (of which only 621 patients are measured for the three omics), ClustOmics succeeded to primarily classify 600 multiomic patients in consensus clusters, with a number of supports threshold of 11 (see Table  3 ).
More particularly, in this work we address the cancer subtyping problem from a personalised medicine related perspective that is gaining increasing attention. In order to treat patients according to their disease profile, one should be able to distinguish between disease subtypes. These can be predicted from omic data (traditionally gene expression, but also methylation, miRNA, etc. ) by performing patient clustering (hierarchical clustering, density-based clustering, distribution-based clustering, etc. ). Our novel graph-based multi-integration method, can fuse multiple input clustering results (obtained with existing clustering methods on diverse omic datasets) into one consensus clustering, whatever the number of input clusters, the number of objects clustered, the omics and the methods used to generate the input clusterings.
It should be noted that though our method does not formally weight input datasets (e. g, according to their level of confidence), one can artificially enhance the impact of one or several omic sources by providing supplementary single-omic input clusterings. In the same way, when dealing with missing data, patients measured in all omics are more likely to accumulate supports and therefore more likely to cluster together. In a context of multi-source integration, favoring individuals with the least amount of missing data makes it possible to highlight the predictions supported by several data sources, which is the desired behavior. In a context of single-source integration, the same set of objects is usually used in all input clusterings (apart from a few specificities of the input clustering tools used).
TCGA real datasets from three different omics and ten cancer types were analysed with respect to two integration scenarios : (1) fusing multi-omic clusterings obtained with existing integrative clustering tools and (2) fusing omic-specific input clusterings. In both cases, ClustOmics succeeded to compute high quality multi-omic consensus clusterings with clusters showing different survival curves and enriched for clinical labels of interest, coherent with what could be found in the cancer literature. Moreover, results indicate that ClustOmics is robust to heterogeneous input clustering qualities (reconciling and smoothing the disparities of partition) and in comparison with a state-of-the-art consensus based integration method, COCA. Notably, our method is not meant to compete with existing single or integrative omics clustering methods but, on the contrary, it aims at capitalising on the preliminary input predictions in order to increase their robustness by taking advantage of accumulating evidence to reveal sharper patterns in the data.
Finally, though presented in a disease subtyping context, one should grasp that our method is not limited to this application case. Indeed taking partition results (instead of raw data) as input, makes it generic and adaptable to a wide range of biological questions, as one can use any kind of partitioning of the data, e. g. clinical labels, groups of genes of interest, etc. , as an input clustering. A major strength of ClustOmics resides in its exploratory aspect, resulting on one side from a flexible intrinsic model that gives the user complete power on the integration scenario to investigate and, on the other side, from the use of the graph-oriented database Neo4j. All input data and metadata are stored in this kind of database, which may be easily queried and visualised by a non-specialist with the Neo4j browser.
Computation of COCA consensus clusterings To assess ClustOmics performance with respect to a state-of-the-art integration method based on consensus clustering, we applied COCA (Cluster-Of-Clusters Analysis)  [5]  on each integration scenario and from the same set of input clusterings as for ClustOmics. At the more so that COCA has already been applied to cancer-subtyping in a multi-omic context  [43, 44]. COCA is an integrative clustering tool based on the Consensus Clustering (CC) algorithm introduced by Monti et al. [45]. The CC algorithm implements a resampling and co-occurrences-based strategy to assess the stability of clusters when analyzing a single dataset. By re-sampling multiple times a single dataset and applya clustering algorithm on each perturbed dataset, and from the co-occurrences counts of samples in clusters, a consensus matrix is computed and used as a similarity matrix to compute a final consensus clustering.
One has to keep in mind that molecular data does not always explain survival or clinical differences between groups of samples. Therefore, in the discussion of the results we consider survival and clinical analysis as ways to interpret patterns captured by the various clustering results, and do not favor one metric over the other. Finally, to evaluate differentially expressed genes across consensus clusters generated using StoM scenario on BIC study case, we applied the Kruskal-Wallis test on each gene available from the BIC expression dataset. P-values were adjusted to control the False Discovery Rate (FDR)  [50] , filtered with a 0. 001 significance threshold, and top 1000 most significant genes were kept for further analysis. Using Hierarchical Clustering  [51] , we clustered the top gene list and investigated clusters for enriched Gene Ontology  [52]  Biological Process terms with Cluster Profiler  [53].
Implementation ClustOmics is implemented based on the Neo4j graph database management system and uses APOC and Graph Data Science Neo4j libraries. Queries on the graph database are performed in Cypher, Neo4j's graph query language, and are encapsulated in Python scripts. To facilitate its use, ClustOmics can be run through the Snakemake workflow management system. ClustOmics was tested on a desktop-computer with an Intel Xeon processor (2. 70GHz, 62GB of RAM) running on Ubuntu 18. 04. For the TCGA real datasets it was applied to, ClustOmics runtimes range from a few minutes for small datasets (AML, Multi-to-Multi scenario) up to 2 hours for the largest dataset (BIC, Single-to-Multi scenario), most of the computation time being consumed for the construction of the integration graph. The graph being stored in a Neo4j database, this step is only to be performed once for each integration scenario and parameters for graph filtering can be further set and tuned without re-computing the graph.
This ontology is registered in the Bioportal web portal  25. Moreover, representative 3D morphological reconstructions and optic cross-section images implement the ontology and are available in the web-based database FABA (https://www. bpni. bio. keio. ac. jp/chordate/faba/1. 4/top. html). In FABA, information about cell lineages in early development was annotated based on previous investigations  [26] [27] [28] [29]. Considering that the ascidian embryogenesis is stereotyped, this ontology provides a standardized resource of spatial and temporal information for both C. robusta and C. intestinalis, as well as other solitary ascidians. After larval hatching, ascidian larvae disperse, swimming freely and searching for a suitable substrate on which to metamorphose. The metamorphosis is deep and transforms the larva with the chordate body plan into a sessile, filter-feeding adult (Supplementary Figure  S1 )  30.
Because the same anatomical structure was sometimes called with different names by researchers in different periods or belonging to different biological fields, we also annotated synonyms. All this information, together with stereomicroscopy time-lapse movies, are consultable in the webbased resource called TunicAnatO (Tunicate Anatomical and developmental Ontology) (https://www. bpni. bio. keio. ac. jp/tunicanato/3. 0/). TunicAnatO includes the former FABA database  24  , therefore covering, in total, 37 developmental stages of Ciona development, from the unfertilized egg to the juvenile. Features exhibited in the newly defined Stages 26 to 37 are described in Supplementary Data S2. TunicAnatO is also reachable via the Bioportal (http://bioportal. bioontology. org/), which is the most comprehensive repository of biomedical ontologies, and via the Tunicate Web Portal (http://www.
Discussion The ontology of Ciona: a powerful tool for developmental biology studies Ciona is considered a valuable model for studying the developmental biology of tunicates and the evolution of chordates. For this species, several databases have contributed as resources for genome and gene expression information (e. g. , Ghost, http://ghost. zool. kyoto-u. ac. jp/indexr1. html; CITRES, https://marinebio. nbrp. jp/ciona/) and proteomic studies (e. g. , CIPRO, http://cipro. ibio. jp/), or provide technical information (e. g. , ACBD, the Ascidians Chemical Biological Database, https://www. bpni. bio. keio. ac. jp/chordate/acbd/top. html). All the available databases are easily consultable via the Tunicate Web Portal (https://tunicate-portal. org/ ). In this study, we present the database TunicAnatO, which implements the previous database FABA  24  and is devoted to anatomy and development.
Moreover, the ADO built here is combined with the ANISEED database (https://www. aniseed. cnrs. fr), which provides high-throughput data and in situ experiment data from the literature for ascidian species. Therefore, we integrate the panorama of actual databases and offer a tool that will help researchers in the recognition the anatomical structures of their interests. This will allow for the standardization of data underpinning an accurate annotation of gene expression and the comprehension of mechanisms of differentiation.
We also consulted the glossary TaxaGloss (https://stricollections. org/portal/index. php). The AO is documented by the database TunicAnatO, which is an anatomical atlas of original images readily available via the internet and easily accessible from any standard web browser (https://www. bpni. bio. keio. ac. jp/tunicanato/3. 0/). This database contains information from both z-slice sections and 3D reconstruction images, and histological sections at each time point along the developmental course of C. robusta (C. intestinalis type A). In images, the anatomical entities were labeled, providing a guide for tissue recognition.
Data integration in anatomical and developmental ontology In this work, we linked DO and AO in a comprehensive DAO, as we defined, for each entity, the relations Develops from, Start stage, and End stage. These relations were determined at the cell level when cell lineage data were available and at the tissue/organ level where complexity did not allow for the following of cell genealogy. Lastly, when possible, we also annotated features linked to organ functionality (swimming, feeding, respiration, or heart beating). Some entities showed multiple possibilities to be defined, while others had uncertain/controversial definitions. In some cases, and where possible, we defined the anatomical entities according to multiple organizational levels. For example, the "atrial siphon muscle" Start stage is Stage 10 if we refer to cell lineage  [44] [45] [46]  , while it is Stage 33 if we refer to histology (muscles recognizable on sections) and Stage 36 if we refer to the functional state of the muscles (ability to contract).
Preparation of embryos for time-lapse imaging Eggs and sperm were obtained surgically from gonoducts. After insemination, eggs were maintained in agarose-coated dishes with Millipore-filtered seawater (MFSW) containing 50 µg/ml streptomycin sulfate, and the early cleavages were uniformly synchronized (data not shown). To keep the temperature stable, we used a Peltier-type incubator (CN-25B, Mitsubishi, Japan) without any vibration to prevent embryo fusion. Embryos developed in hatched larvae approximately 18 h after insemination. The naturally hatched larvae derived from egg with chorion were maintained in plastic dishes on the thermo-plate at 20°C to acquire images. Using a digital camera (Olympus SP-350) mounted on a microscope, images were acquired every 3 to 10 min for 7 days (Supplementary Video S4).
Alexa 546 phalloidin was used to visualize cell membranes because it stains mainly cortical actin filaments. Images were collected with a CLSM on a Zeiss LSM510 META with 40X oil objective or on an OLYMPUS fv1000. To reconstruct the 3D images, 100 cross-section images from top to bottom per sample were acquired (LSM image browser, Zeiss, Germany). The focus interval depended on the sample (from 0. 5 to 1. 2 µm). The resulting stacks were then exported to raw image series or to 3D image data for database integration. Although the timing of metamorphosis showed a huge deviation depending on the timing of adhesion, we considered an average timing, looking at animals exhibiting a representative morphology. Lastly, these stacks were integrated into the database TunicAnatO.
Availability of data and material The datasets generated and/or analyzed during the current study are available in the following repositories, as well as our database, TunicAnatO (https://www. bpni. bio. keio. ac. jp/tunicanato/3. 0/). -Tunicate portal: https://tunicate-portal. org/resources/standards -Bioportal: https://bioportal. bioontology. org/ontologies/CIROBUADO -ANISEED: https://www. aniseed. cnrs. fr/aniseed/download/download_data -FAIRsharing: https://fairsharing. org/bsg-s001475/ and literature were analyzed. After that, entities in hierarchical order, definitions, synonyms, developmental information, and literature were annotated in an Excel file. These data were edited using OBO-Edit (http://oboedit. org/), allowing for the visualization of relationships among entities. D. Lastly, data were associated with images and movies in the web-based resource TunicAnatO (https://www.
name: Supplementary_File_11_Stage_29 Title of data: Late swimming larva (Stage 29) Description of data: A. Larva, left view. CLSM. ATEN, DCEN, RTEN, VCEN: cilium of an anterior trunk, dorsal caudal, rostral trunk, and ventral caudal epidermal neuron, respectively. B. Medial sagittal optic section of the larval trunk. CLSM. C-C I. Two frontal sections of the same larva, at the level of atrial siphon primordia (C) and endodermal strand (esr) (C I ). Light microscopy, Toluidine blue. Enlargement is the same in C-C I. D-D VII. Eight selected transverse sections from a complete dataset of a serially sectioned larva from anterior (D) to posterior (D VII ). Light microscopy, Toluidine blue.
Acknowledgments Authors thank  Yutaka Satou Lab  at  Kyoto University, Manabu Yoshida Lab at the University of Tokyo and Onagawa Marine Station  for providing individuals of Ciona samples with support by the  National Bio-Resource Project of AMED, Japan ;  Akitsu Fukuzawa  for collection of CLSM image stacks;  Takumi T. Shito  for the construction of the flash-independent version (version 3. 0) of TunicAnatO;  Paolo Burighel  for the excellent comments and suggestions regarding the ontology.