Package: KnowSeq 1.21.0

Daniel Castillo-Secilla

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.

Authors:Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb]

KnowSeq_1.21.0.tar.gz
KnowSeq_1.21.0.zip(r-4.5)KnowSeq_1.21.0.zip(r-4.4)KnowSeq_1.21.0.zip(r-4.3)
KnowSeq_1.21.0.tgz(r-4.4-any)KnowSeq_1.21.0.tgz(r-4.3-any)
KnowSeq_1.21.0.tar.gz(r-4.5-noble)KnowSeq_1.21.0.tar.gz(r-4.4-noble)
KnowSeq_1.21.0.tgz(r-4.4-emscripten)KnowSeq_1.21.0.tgz(r-4.3-emscripten)
KnowSeq.pdf |KnowSeq.html✨
KnowSeq/json (API)
NEWS

# Install 'KnowSeq' in R:
install.packages('KnowSeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On BioConductor:KnowSeq-1.21.0(bioc 3.21)KnowSeq-1.20.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

geneexpressiondifferentialexpressiongenesetenrichmentdataimportclassificationfeatureextractionsequencingrnaseqbatcheffectnormalizationpreprocessingqualitycontrolgeneticstranscriptomicsmicroarrayalignmentpathwayssystemsbiologygoimmunooncology

3.30 score 5 scripts 170 downloads 1 mentions 26 exports 162 dependencies

Last updated 22 days agofrom:5a6069470d. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winNOTENov 18 2024
R-4.5-linuxNOTENov 18 2024
R-4.4-winNOTENov 18 2024
R-4.4-macNOTENov 18 2024
R-4.3-winNOTENov 18 2024
R-4.3-macNOTENov 18 2024

Exports:batchEffectRemovalcalculateGeneExpressionValuescountsToMatrixdataPlotDEGsEvidencesDEGsExtractionDEGsToDiseasesDEGsToPathwaysdownloadPublicSeriesfeatureSelectionfileMovegdcClientDownloadgeneOntologyEnrichmentgetGenesAnnotationhisatAlignmentknn_testknn_trnknowseqReportplotConfMatrixrawAlignmentrf_testrf_trnRNAseqQAsraToFastqsvm_testsvm_trn

Dependencies:annotateAnnotationDbiaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobbslibcachemcaretcheckmateclasscliclockclustercodetoolscolorspacecpp11cqncrayoncurldata.tableDBIdiagramdigestdplyre1071edgeRevaluatefansifarverfastmapfontawesomeforeachforeignformatRFormulafsfutile.loggerfutile.optionsfuturefuture.applygenefiltergenericsGenomeInfoDbGenomeInfoDbDataggplot2globalsgluegowergridExtragtablehardhathighrHmischtmlTablehtmltoolshtmlwidgetshttripredIRangesisobanditeratorsjquerylibjsonliteKEGGRESTkernlabKernSmoothknitrlabelinglambda.rlatticelavalifecyclelimmalistenvlocfitlubridatemagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmclustmemoisemgcvmimeModelMetricsmunsellnlmennetnor1mixnumDerivopensslparallellypillarpkgconfigplogrplyrpngpraznikpROCprodlimprogressrproxypurrrquantregR.methodsS3R.ooR.utilsR6randomForestrappdirsRColorBrewerRcpprecipesreshape2rlangrlistrmarkdownrpartRSQLiterstudioapiS4VectorssassscalesshapesnowSparseMSQUAREMstatmodstringistringrsurvivalsvasystibbletidyrtidyselecttimechangetimeDatetinytextzdbUCSC.utilsutf8vctrsviridisviridisLitewithrxfunXMLxtableXVectoryamlzlibbioc

Transcriptomic Intelligent Pipeline: The KnowSeq user guide

Daniel Castillo-Secilla, Juan Manuel Galvez, Marta Verona-Almeida, Francisco Carrillo-Perez, Francisco Manuel Ortuno, Luis Javier Herrera and Ignacio Rojas

Rendered fromKnowSeq.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2021-04-22
Started: 2019-05-15

Readme and manuals

Help Manual

Help pageTopics
Corrects the batch effect of the data by using the selected method.batchEffectRemoval
Calculates the gene expression values by using a matrix of counts from RNA-seq.calculateGeneExpressionValues
countsToMatrix merges in a matrix the information in the count files.countsToMatrix
Plot different graphs depending on the current step of KnowSeq pipeline.dataPlot
DEGsEvidences function returns for each DEG a list of evidences that correlate it with the studied disease.DEGsEvidences
DEGsExtraction performs the analysis to extract the Differentially Expressed Genes (DEGs) among the classes to compare.DEGsExtraction
DEGsToDiseases obtains the information about what diseases are related to the DEGs indicated by parameter.DEGsToDiseases
The function uses the DEGs to retrieves the different pathways in which those DEGs involve any interaction.DEGsToPathways
Download automatically samples from NCBI/GEO and ArrayExpress public databases.downloadPublicSeries
featureSelection function calculates the optimal order of DEGs to achieve the best result in the posterior machine learning process by using mRMR algorithm or Random Forest. Furthermore, the ranking is returned and can be used as input of the parameter vars_selected in the machine learning functions.featureSelection
This function is used to move files to other locations.fileMove
This function downloads a list of controlled files from GDC Portal with the user token and the manifest with the information about the desired controlled files.gdcClientDownload
geneOntologyEnrichment obtains the information about what Gene Ontology terms are related to the DEGs.geneOntologyEnrichment
getGenesAnnotation returns the required information about a list of genes from Ensembl biomart.getGenesAnnotation
hisatAlignment allows downloading and processing the fastq samples in a CSV file by using hisat2 aligner.hisatAlignment
knn_test allows assessing the final DEGs through a machine learning step by using k-NN with a test dataset.knn_test
knn_trn allows assessing the final DEGs through a machine learning step by using k-NN in a cross validation process.knn_trn
knowseqReport creates a report for a given set of genes which their label.knowseqReport
plotConfMatrix plots a confusion matrix with some statistics.plotConfMatrix
rawAlignment allows downloading and processing the fastq samples in a CSV file.rawAlignment
rf_test allows assessing the final DEGs through a machine learning step by using Random Forest with a test dataset.rf_test
rf_trn allows assessing the final DEGs through a machine learning step by using Random Forest in a cross validation process.rf_trn
RNAseqQA performs the quality analysis of an expression matrix.RNAseqQA
sraToFastq downloads and converts the sra files to fastq files. The function admits both gz and sra formats.sraToFastq
svm_test allows assessing the final DEGs through a machine learning step by using SVM with a test dataset.svm_test
svm_trn allows assessing the final DEGs through a machine learning step by using svm in a cross validation process.svm_trn