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25 results for “outlier detection”
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>
Multi-Domain Outlier Detection Dataset
<p>The Multi-Domain Outlier Detection Dataset contains datasets for conducting outlier detection experiments for four different application domains:</p> <ol> <li>Astrophysics - detecting anomalous observations in the Dark Energy Survey (DES) catalog (data type: feature vectors)</li> <li>Planetary science - selecting novel geologic targets for follow-up observation onboard the Mars Science Laboratory (MSL) rover (data type: grayscale images)</li> <li>Earth science: detecting anomalous samples in satellite time series corresponding to ground-truth observations of maize crops (data type: time series/feature vectors)</li> <li>Fashion-MNIST/MNIST: benchmark task to detect anomalous MNIST images among Fashion-MNIST images (data type: grayscale images)</li> </ol> <p>Each dataset contains a "fit" dataset (used for fitting or training outlier detection models), a "score" dataset (used for scoring samples used to evaluate model performance, analogous to test set), and a label dataset (indicates whether samples in the score dataset are considered outliers or not in the domain of each dataset). </p> <p>To read more about the datasets and how they are used for outlier detection, or to cite this dataset in your own work, please see the following citation:</p> <p>Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). Domain-agnostic Outlier Ranking Algorithms (DORA)-A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Under review for <em>Frontiers in Astronomy and Space Sciences</em>. </p>
Data to accompany the outlier-waveform-detection Github repository (internal globus pallidus, GPi)
<p>This repository contains data based on neuronal recordings from two monkeys (G and I, in the pre- and post-MPTP states) that serve as input to the code provided at <a href="https://github.com/turner-lab-pitt/outlier-waveform-detection">https://github.com/turner-lab-pitt/outlier-waveform-detection</a>. Text files located within that Github repository provide detailed instructions on how these data may be used with that code. As described in those text files, extra data are provided for Monkey G, in the pre-MPTP state.</p> <p>The data-description.txt file provides detailed information regarding the contents of each zipped tar archive. Briefly, the most important components of the files are the "snips" (individual spike waveforms) from the two monkeys and MPTP states, as extracted for each of a series of single sorted units from the internal globus pallidus (GPi). The additional G-Pre data provides examples of the high-pass filtered voltage signals from which these snips were extracted. All data are stored in the Matlab .mat format.</p> <p>All zipped files can be decompressed with 7-zip: <a href="https://www.7-zip.org/" target="_blank" rel="noopener">https://www.7-zip.org/</a></p> <p>These data and the associated Github code were used for analyses reported in an in-preparation manuscript (Kase et al., "Movement-related activity in the internal globus pallidus of the parkinsonian macaque"), and also with a preprint that is currently under review:</p> <div> <div>Detecting rhythmic spiking through the power spectra of point process model residuals</div> </div> <div>Karin M. Cox, Daisuke Kase, Taieb Znati, Robert S. Turner</div> <div>bioRxiv 2023.09.08.556120; doi: <a href="https://doi.org/10.1101/2023.09.08.556120" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.08.556120</a></div> <div> </div> <p>This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset. </p>
Synthetic Dataset for Outlier Detection
<p>This synthetically generated dataset can be used to evaluate outlier detection algorithms. It has 10 attributes and 1000 observations, of which 100 are labeled as outliers. Two-dimensional combinations of attributes form differently shaped clusters.</p> <ul> <li>Attribute 0 & Attribute 1: Two circular clusters</li> <li>Attribute 2 & Attribute 3: Two banana shaped clusters</li> <li>Attribute 4 & Attribute 5: Three point clouds</li> <li>Attribute 6 & Attribute 7: Two point clouds with variances</li> <li>Attribute 8 & Attribute 9: Three anisotropic shaped clusters. </li> </ul> <p>The "outlier" column states whether an observation is an outlier or not. Additionally, the .zip file contains 10 stratified randomized train test splits (70% train, 30% test).</p>
Key triggers of adaptive genetic variability of sessile oak [Q. petraea (Matt.) Liebl.] from the Balkan refugia: outlier detection and association of SNP loci from ddRAD-seq data
<p>Knowledge on the genetic composition of <em>Quercus petraea</em> in south-eastern Europe is limited despite the species' significant role in the re-colonisation of Europe during the Holocene, and the diverse climate and physical geography of the region. Therefore, it is imperative to conduct research on adaptation in sessile oak to better understand its ecological significance in the region. While large sets of SNPs have been developed for the species, there is a continued need for smaller sets of SNPs that are highly informative about the possible adaptation to this varied landscape. By using double digest restriction site associated DNA sequencing data from our previous study, we mapped RAD-tag sequences to the <em>Quercus robur</em> reference genome and identified a set of SNPs putatively related to drought stress-response. A total of 179 individuals from eighteen natural populations at sites covering heterogeneous climatic conditions in the southeastern natural distribution range of <em>Q. petraea</em> were genotyped. The detected highly polymorphic variant sites revealed three genetic clusters with a generally low level of genetic differentiation and balanced diversity among them but showed a north–southeast gradient. Selection tests showed nine outlier SNPs positioned in different functional regions. Genotype-environment association analysis of these markers yielded a total of 53 significant associations, explaining 2.4–16.6% of the total genetic variation. Our work exemplifies that adaptation to drought may be under natural selection in the examined <em>Q. petraea</em> populations.</p>
TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees
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ClinePlotR: Visualizing genomic clines and detecting outliers in R
<p class="Normal1">Patterns of multi-locus differentiation (i.e., genomic clines) often extend broadly across hybrid zones and their quantification can help diagnose how species boundaries are shaped by adaptive processes, both intrinsic and extrinsic. In this sense, the transitioning of loci across admixed individuals can be contrasted as a function of the genome-wide trend, in turn allowing an expansion of clinal theory across a much wider array of biodiversity. However, computational tools that serve to interpret and consequently visualize 'genomic clines' are limited.</p> <p>Here, we introduce the <span class="MsoSubtleReference">ClinePlotR R</span>-package for visualizing genomic clines and detecting outlier loci using output generated by two popular software packages, <span class="MsoSubtleReference">bgc </span>and <span class="MsoSubtleReference">Introgress.</span></p> <p><span class="MsoSubtleReference">ClinePlotR </span>bundles both input generation (i.e, filtering datasets and creating specialized file formats) and output processing (e.g., MCMC thinning and burn-in) with functions that directly facilitate interpretation and hypothesis testing. Tools are also provided for post-hoc analyses that interface with external packages such as <span class="MsoSubtleReference">ENMeval </span>and <span class="MsoSubtleReference">RIdeogram</span></p> <p>Our package increases the reproducibility and accessibility of genomic cline methods, thus allowing an expanded user base and promoting these methods as mechanisms to address diverse evolutionary questions in both model and non-model organisms.</p>
Data from: Outlier SNPs detect weak regional structure against a background of genetic homogeneity in the Eastern Rock Lobster, Sagmariasus verreauxi
Genetic differentiation is characteristically weak in marine species making assessments of population connectivity and structure difficult. However the advent of genomic methods have increased genetic resolution, enabling studies to detect weak, but significant population differentiation within marine species. With an increasing number of studies employing high resolution genome-wide techniques, we are realising the connectivity of marine populations is often complex and quantifying this complexity can provide an understanding of the processes shaping marine species genetic structure and to inform long-term, sustainable management strategies. This study aims to assess the genetic structure, connectivity and local adaptation of the Eastern Rock Lobster (Sagmariasus verreauxi), which has a maximum pelagic larval duration of 12 months and inhabits both subtropical and temperate environments. We used 645 neutral and 15 outlier SNPs to genotype lobsters collected from the only two known breeding populations and a third episodic population — encompassing S. verreauxi's known range. Through examination of the neutral SNP panel, we detected genetic homogeneity across the three regions, which extended across the Tasman Sea encompassing both Australian and New Zealand populations. We discuss differences in neutral genetic signature of S. verreauxi and a closely-related, co-distributed rock lobster, Jasus edwardsii, determining a regional pattern of genetic disparity between the species, which have largely similar life histories. Examination of the outlier SNP panel detected weak genetic differentiation between the three regions. Outlier SNPs showed promise in assigning individuals to their sampling origin and may prove useful as a management tool for species exhibiting genetic homogeneity.
Data from: Outlier loci detect intraspecific biodiversity amongst spring and autumn spawning herring across local scales
Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.
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: Detection of outlier loci and their utility for fisheries management
Genetics-based approaches have informed fisheries management for decades, yet remain challenging to implement within systems involving recently diverged stocks or where gene flow persists. In such cases, genetic markers exhibiting locus-specific ("outlier") effects associated with divergent selection may provide promising alternatives to loci that reflect genome-wide ("neutral") effects for guiding fisheries management. Okanagan Lake kokanee (Oncorhynchus nerka), a fishery of conservation concern, exhibits two sympatric ecotypes adapted to different reproductive environments, however, previous research demonstrated the limited utility of neutral microsatellites for assigning individuals. Here, we investigated the efficacy of an outlier-based approach to fisheries management by screening >11,000 expressed sequence tags for linked microsatellites and conducting genomic scans for kokanee sampled across seven spawning sites. We identified eight outliers among 52 polymorphic loci that detected ecotype-level divergence, whereas there was no evidence of divergence at neutral loci. Outlier loci exhibited the highest self-assignment accuracy to ecotype (92.1%), substantially outperforming 44 neutral loci (71.8%). Results were robust among-sampling years, with assignment and mixed composition estimates for individuals sampled in 2010 mirroring baseline results. Overall, outlier loci constitute promising alternatives for informing fisheries management involving recently diverged stocks, with potential applications for designating management units across a broad range of taxa.
Data from: Detection of outlier loci and their utility for fisheries management
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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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ClinePlotR: Visualizing genomic clines and detecting outliers in R
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Data from: Outlier loci detect intraspecific biodiversity amongst spring and autumn spawning herring across local scales
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Data from: Outlier SNPs detect weak regional structure against a background of genetic homogeneity in the Eastern Rock Lobster, Sagmariasus verreauxi
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Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
EAD: Effortless Anomalies Detection, A deep learning based approach for detecting outliers in textual data
<p>Xiuzhe Wang used this data set for his project</p>
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
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Outlier detection of biologically significant genes from combinatorial microarray data
GEO Series GSE22850. Yersinia pestis. 30 samples. Type: Expression profiling by array.
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