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bioc
omada:Machine learning tools for automated transcriptome clustering analysis
Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found.
Maintained by Sokratis Kariotis. Last updated 5 months ago.
softwareclusteringrnaseqgeneexpression
3.60 score 5 scriptsbioc
KnowSeq:KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline
KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study.
Maintained by Daniel Castillo-Secilla. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentdataimportclassificationfeatureextractionsequencingrnaseqbatcheffectnormalizationpreprocessingqualitycontrolgeneticstranscriptomicsmicroarrayalignmentpathwayssystemsbiologygoimmunooncology
3.30 score 5 scripts