Search results for "Feature matrix"

showing 2 items of 2 documents

Assessment of computational methods for the analysis of single-cell ATAC-seq data

2019

Abstract Background Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1–10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10–45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. Results We present a benchmarking framework that …

Epigenomicslcsh:QH426-470Test data generationComputer scienceCellATAC-seqComputational biologyBiologyClusteringTranscriptomeMice03 medical and health scienceschemistry.chemical_compound0302 clinical medicinemedicineAnimalsHumansProfiling (information science)scATAC-seqnatural sciencesEpigeneticsFeature matrixCluster analysislcsh:QH301-705.5GeneTransposaseVisualization030304 developmental biologySparse matrix0303 health sciencesFeaturizationDimensionality reductionResearchComputational BiologySequence Analysis DNADimensionality reductionChromatinBenchmarkinglcsh:Geneticsmedicine.anatomical_structurelcsh:Biology (General)chemistryRegulatory genomicsSingle-Cell AnalysisPeak calling030217 neurology & neurosurgeryDNA
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Anomaly Detection from Network Logs Using Diffusion Maps

2011

The goal of this study is to detect anomalous queries from network logs using a dimensionality reduction framework. The fequencies of 2-grams in queries are extracted to a feature matrix. Dimensionality reduction is done by applying diffusion maps. The method is adaptive and thus does not need training before analysis. We tested the method with data that includes normal and intrusive traffic to a web server. This approach finds all intrusions in the dataset. peerReviewed

Web serverComputer scienceintrusion detectionDimensionality reductionFeature matrixDiffusion mapdiffusion maphyökkäyksen havaitseminenIntrusion detection systemcomputer.software_genreanomaly detectionpoikkeavuuden havaitseminendiffuusiokarttakoneoppiminenAnomaly detectionData miningtiedonlouhintan-grammitcomputern-grams
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