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5 results for “Outlier Analysis”
Lipidomics LC-MS analysis support tools for outlier detection
<p>Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.</p> <p>The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.</p>
Data from: Genetic architecture in a marine hybrid zone: comparing outlier detection and genomic clines analysis in the bivalve Macoma balthica
The role of natural selection in speciation has received increasing attention and support in recent years. Different types of approaches have been developed that can detect genomic regions influenced by selection. Here we address the question whether two highly different methods - Fst outlier analysis and admixture analysis - detect largely the same set of non-neutral genomic elements or, instead, complementary sets. We study genetic architecture in a natural secondary contact zone where extensive admixture occurs. The marine bivalves Macoma balthica rubra and M. b. balthica descend from two independent trans-Arctic invasions of the north Atlantic and hybridize extensively where they meet, for example in the Kattegat - Danish Straits - Baltic Sea region. The Kattegat - Danish Straits region forms a steep salinity cline and is the only entrance to the recently (ca 8000 years ago) established brackish water basin the Baltic Sea. Salinity along the contact zone drops from 30‰ (Skagerrak, M.b.rubra) to 3‰ (Baltic, M.b.balthica). Both outlier analysis and genomic clines analysis suggest that large parts of the genome are influenced by non-neutral effects. Contrasting samples from well outside the hybrid zone, outlier analysis detects 16 of 84 amplified fragment length polymorphism (AFLP) markers as significant Fst outliers. Genomic clines analysis detects 31 out of 84 markers as non-neutral inside the hybrid zone. Remarkably, only three markers are detected by both methods. We conclude that the two methods together identify a suite of markers that are under the influence of non-neutral effects.
Data from: Genetic architecture in a marine hybrid zone: comparing outlier detection and genomic clines analysis in the bivalve Macoma balthica
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Data from: Thermal tolerance in the keystone species Daphnia magna –a candidate gene and an outlier analysis approach
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Transcriptome-wide gene expression outlier analysis pinpoints therapeutic vulnerabilities in colorectal cancer
GEO Series GSE220197. Homo sapiens. 88 samples. Type: Methylation profiling by array.
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