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[Page no. 9] "statistically significant pathways related to the cellular systems studied. The improved sensitivity in the analysis can be attrib- uted to larger cluster sizes and that all genes highly correlated to a cluster centroid will be assigned to that cluster. The noise reduction property of MAsC further improves the resulting partitions, by removal of genes with low similarity to all of the clusters, rather than forcing them into the closest cluster. We believe MAsC is a powerful comple- ment to existing clustering methods and can support data interpretation in future high-throughput profiling studies. Data statement The datasets analyzed during the current study are available in the ArrayExpress repository, namely the Mesoderm dataset with accession number E-MTAB-5219 (https://www.ebi.ac.uk/arrayexpress/exper iments/E-MTAB-5219/) and the Hepatocyte dataset with accession number E-MTAB-5367 (https://www.ebi.ac.uk/arrayexpress/experime nts/ E-MTAB-5367/). CRediT authorship contribution statement Benjamin Ulfenborg: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing. Alexander Karlsson: Investigation, Methodology, Formal analysis, Visualization, Writing - original draft, Writing - review & editing. Maria Riveiro: Investigation, Methodology, Formal analysis, Visualization, Writing - original draft, Writing - review & editing. Christian X. Andersson: Writing - original draft, Writing - review & editing. Peter Sartipy: Writing - original draft, Writing - review & editing. Jane Synnergren: Investigation, Methodology, Formal anal- ysis, Funding acquisition, Project administration, Resources, Visualiza- tion, Writing - original draft, Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the University of Sk¨ovde, Sweden, the Knowledge Foundation [2014/0301 and 2017/0302], and Takara Bio Europe, Gothenburg. The Knowledge Foundation had no role in the study. Takara Bio Europe contributed with data interpretation and writing of"

[Page no. 2] "highly similar expression profiles may be involved in the same molecular processes. Thus, when interpreting clustering results, it is relevant to explore over- representation of genes in known pathways, or enrichment for specific functional annotations. However, since the majority of clustering ana- lyses today do not consider multiple assignment of objects, the down- stream analysis may become less informative, since genes of a specific pathway can be split into several clusters. Many clusters lack objects that participate in similar biological processes, as the other members in these processes may have been assigned and locked to other clusters. This results in lower statistical power in subsequent enrichment analyses and affects the correct mapping of clusters to their true biological functions. The problem that genes need to be part of multiple clusters may be handled through different types of fuzzy clustering techniques, e.g. Ferraro & Giordani (Ferraro and Giordani, 2015), which allow objects to belong to more than one cluster with a certain degree (Bandyopadhyay et al., 2007; Fu and Medico, 2007; Wu et al., 2011; Zhang et al., 2011). There are also other approaches for imprecise clustering based on the assignment of meta clusters (Liu et al., 2015; Wu et al., 2011), and model-based approaches using mixture models (Mitchell, 1999), where one can obtain probabilistic membership information to the different clusters. These approaches provide alternative perspectives on the clustering, but do not assign objects into multiple clusters simultaneously. In contrast to previous research and given that a substantial portion of gene expression profiling analyses are performed under the assump- tion of mutually exclusive assignment to clusters, our aim was to investigate the effects of multi-assignment clustering on the downstream analysis. We hypothesized that annotation enrichment analysis of clus- ters could be rendered more sensitive and biologically meaningful by performing it on"

[Page no. 1] "Journal of Biotechnology 326 (2021) 1–10 Available online 4 December 2020 0168-1656/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Multi-assignment clustering: Machine learning from a biological perspective Benjamin Ulfenborg a,*, Alexander Karlsson b, Maria Riveiro b,c, Christian X. Andersson d, Peter Sartipy a, Jane Synnergren a a School of Bioscience, University of Sk¨ovde, Sk¨ovde, Sweden b School of Informatics, University of Sk¨ovde, Sk¨ovde, Sweden c Department of Computer Science and Informatics, School of Engineering, J¨onk¨oping University, J¨onk¨oping, Sweden d Takara Bio Europe AB, Gothenburg, Sweden A R T I C L E I N F O Keywords: Clustering Multiple cluster assignment K-means Transcriptomics Annotation enrichment Pathways A B S T R A C T A common approach for analyzing large-scale molecular data is to cluster objects sharing similar characteristics. This assumes that genes with highly similar expression profiles are likely participating in a common molecular process. Biological systems are extremely complex and challenging to understand, with proteins having multiple functions that sometimes need to be activated or expressed in a time-dependent manner. Thus, the strategies applied for clustering of these molecules into groups are of key importance for translation of data to biologically interpretable findings. Here we implemented a multi-assignment clustering (MAsC) approach that allows mol- ecules to be assigned to multiple clusters, rather than single ones as in commonly used clustering techniques. When applied to high-throughput transcriptomics data, MAsC increased power of the downstream pathway analysis and allowed identification of pathways with high biological relevance to the experimental setting and the biological systems studied. Multi-assignment clustering also reduced noise in the clustering partition by excluding genes with a low correlation to all of the resulting clusters. Together, these findings suggest that our methodology facilitates translation of large-scale"

[Page no. 1] "molecular data into biological knowledge. The method is made available as an R package on GitLab (https://gitlab.com/wolftower/masc). 1. Introduction Over the last two decades, the amount of available molecular data has increased dramatically. State-of-the-art techniques such as sequencing, mass spectrometry and other high-throughput ’omics’ technologies enable efficient generation of extensive experimental datasets that provide unique resources for advanced data mining. These datasets provide unprecedented insights into molecular pathways in cells and their role in various diseases (Meng et al., 2016). However, many challenges remain on how to best perform the analysis of this large-scale molecular data (Li and Chen, 2014; Sulakhe et al., 2014). One of the most common strategies for analyzing high-dimensional data is to find clusters of objects that share similar characteristics. Many clustering algorithms are based on machine learning techniques designed to discover hidden structures in the data (Xu and Wunsch, 2010). Unlabeled data objects are grouped into clusters so that objects in the same cluster are more similar to each other than to objects assigned to other clusters. A cluster is inherently a subjective structure that does not have a precise and formal definition. In theory, data points that are in the same group should have similar properties and/or features, while data points in separate groups should have different properties and/or features (Gan et al., 2007). Along with the large number of clustering algorithms available, a rich literature on cluster analysis has developed over the years and several reviews account for the variety of algorithms, application fields, types, weaknesses, and strengths (Aggarwal, 2014; Jain, 2010; Wen- skovitch et al., 2017). Examples of clustering algorithms commonly used in biomedical research are presented in the review by Xu and Wunsch Abbreviations: ECM, Extracellular matrix; HTA, Human Transcriptome Array; KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, Mitogen-activated protein"

[Page no. 2] "kinase; MAsC, Multi-assignment clustering; NAFLD, Non-alcoholic fatty liver disease; PPAR, Peroxisome proliferator-activated receptor; RMA, Robust Multichip Average; SPIA, Signaling Pathway Impact Analysis; TGF, Transforming growth factor. * Corresponding author. E-mail address: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> (B. Ulfenborg). Contents lists available at ScienceDirect Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec https://doi.org/10.1016/j.jbiotec.2020.12.002 Received 17 June 2020; Accepted 3 December 2020  Journal of Biotechnology 326 (2021) 1–10 2 (Xu and Wunsch, 2010). Conventional clustering algorithms have been used or adapted to gene expression data, and algorithms have also been developed specifically for gene expression data analysis (Si et al., 2014). K-means (Hartigan and Wong, 1979) is one of the most well-known clustering algorithms and frequently used in biomedical data analysis (Jain, 2010). The main advantages of K-means are that it is transparent, relatively fast, and has low memory requirement since it only computes the distances between the data point and the centroid (group center) of the clusters until convergence. In most clustering analyses of large-scale molecular data that are performed today, the objects are restricted to be assigned to a single cluster, potentially disregarding high similarity to several other clusters. The cluster with the highest similarity based on a selected similarity measure is the one that the object will be assigned to. From a modeling perspective, this treats the clusters as being mutually exclusive, which is a poor description of the underlying biological context where genes or proteins may be involved in several processes and signaling pathways simultaneously (Fallah et al., 2019; Nikolaou et al., 2013). These path- ways control much of the functions that take place in cells and tissues where gene transcripts, enzymes, proteins, and other molecules serve as key components (Devkota and Wuchty, 2020). One of the basic assumptions when using clustering techniques in the analysis of molecular data is that genes with"

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