The acquisition of high-quality data in the biomedical field, particularly in omics studies
\nsuch as proteomics or transcriptomics, poses a significant challenge due to incomplete
\nmeasurements during data acquisition or simply small sample sizes. This issue results in
\ndatasets with low statistical power that are in addition often compromised by missing
\nvalues, which impede downstream analysis and the accurate interpretation of biological
\nphenomena.<\/p>\n
A common approach to mitigate such limitations is data integration, which combines
\nmultiple datasets to increase cohort sizes by incorporating data from different studies or
\nlaboratories. However, this approach introduces new challenges, notably the so-called
\nbatch effect, which introduces internal biases and obscures biological meaning. Moreover,
\ninfrequently measured features (e.g., proteins or genes) create additional gaps in the data
\nduring integration tasks. <\/p>\n
As the volume of available biological data continues to expand, there is an increasing
\nneed for computational methods capable of efficiently processing and analyzing these
\ngrowing datasets. Expected future advancements in data acquisition with regards to
\nthroughput necessitate the development of computationally efficient and robust algorithms.
\nIn addition, to ensure accessibility and broad adoption, it is crucial that bioinformatics
\ntools must be user friendly, allowing researchers with varying levels of technical expertise
\nto effectively utilize them. <\/p>\n
To this end, an integration and batch effect reduction tool has been developed, called
\nthe HarmonizR algorithm. This work features various functionality that has been build
\nto tackle the aforementioned issues. Dataset integration aims for an increase in cohort
\nsizes and sample amounts, which is facilitated by the inclusion of a new unique removal
\napproach. It overcomes prior limitations regarding data retention, greatly increasing
\nHarmonizR\u2019s benefits as a pipeline tool when used prior to data analysis by significantly
\nexpanding the number of considerable features and data points of any given study. This
\nmay be paired with the added functionality of accounting for user-defined experimental
\ninformation such as treatment-groups (i.e., covariate information) during adjustment,
\nleading to more robust and high-quality results. Regarding computational efficiency, a
\nnovel blocking approach exploits the given data structure to brace the algorithm for
\ncurrent and future big data challenges without negatively impacting adjustment quality.
\nFurthermore, the algorithm\u2019s batch effect adjustment capabilities are proven effective
\non various omics types – with a notable extension towards single cell count datasets by
\nemploying further adjustment methodology – as well as non-biological data in the form of
\nan attention-deficit\/hyperactivity disorder study. <\/p>\n
To address remaining challenges, the newly developed BERT algorithm introduces a novel
\narchitectural approach, offering improvements in information retention and computational
\nefficiency. A comparative analysis of BERT and HarmonizR explores the advantages
\nof BERT in terms of feature\/overall data retention and reduced runtimes, providing a
\nvaluable complement to the existing framework. <\/p>\n
Lastly, to enhance accessibility and ease of use, plugins for the popular Perseus software
\nhave been created and are described, enabling seamless integration of both algorithms
\ninto established bioinformatics workflows, specifically aiding researchers less familiar with
\nthe technical aspects of the here shown algorithms and bioinformatics in general.<\/p>\n","protected":false},"excerpt":{"rendered":"
Efficient Algorithms for Improved Information Retention in Integration of Incomplete Omics Datasets The acquisition of high-quality data in the biomedical field, particularly in omics studies such as proteomics or transcriptomics, […]<\/p>\n","protected":false},"author":102,"featured_media":0,"template":"","meta":{"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[],"class_list":["post-1245","tribe_events","type-tribe_events","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/1245","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/users\/102"}],"version-history":[{"count":3,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/1245\/revisions"}],"predecessor-version":[{"id":1259,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/1245\/revisions\/1259"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/media?parent=1245"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tags?post=1245"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events_cat?post=1245"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}