Package: MLSeq 2.25.0

Gokmen Zararsiz

MLSeq: Machine Learning Interface for RNA-Seq Data

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.

Authors:Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths]

MLSeq_2.25.0.tar.gz
MLSeq_2.25.0.zip(r-4.5)MLSeq_2.25.0.zip(r-4.4)MLSeq_2.25.0.zip(r-4.3)
MLSeq_2.25.0.tgz(r-4.4-any)MLSeq_2.25.0.tgz(r-4.3-any)
MLSeq_2.25.0.tar.gz(r-4.5-noble)MLSeq_2.25.0.tar.gz(r-4.4-noble)
MLSeq_2.25.0.tgz(r-4.4-emscripten)MLSeq_2.25.0.tgz(r-4.3-emscripten)
MLSeq.pdf |MLSeq.html✨
MLSeq/json (API)
NEWS

# Install 'MLSeq' in R:

install.packages('MLSeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

cervical - Cervical cancer data

On BioConductor:MLSeq-2.25.0(bioc 3.21)MLSeq-2.24.0(bioc 3.20)

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

immunooncology sequencing rnaseq classification clustering

4.80 score 1 packages 26 scripts 306 downloads 4 mentions 32 exports 134 dependencies

Last updated 2 months agofrom:97e5509afd. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 29 2024
R-4.5-win	NOTE	Nov 29 2024
R-4.5-linux	NOTE	Nov 29 2024
R-4.4-win	NOTE	Nov 29 2024
R-4.4-mac	NOTE	Nov 29 2024
R-4.3-win	NOTE	Nov 29 2024
R-4.3-mac	NOTE	Nov 29 2024

Exports:availableMethods classify confusionMat control control<-discreteControl input isModified isModified<-isUpdated isUpdated<-metaData method method<-modelInfo normalization normalization<-plot predict predictClassify preProcessing preProcessing<-print printAvailableMethods ref ref<-selectedGenes trained trainParameters transformation update voomControl

Dependencies:abind askpass BH Biobase BiocGenerics BiocParallel bitops brio callr caret caTools class cli clock cluster codetools colorspace cpp11 crayon curl data.table DelayedArray desc DESeq2 diagram diffobj digest dplyr e1071 edgeR evaluate fansi farver foreach formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 globals glue gower gtable hardhat httr ipred IRanges isoband iterators jsonlite KernSmooth labeling lambda.r lattice lava lifecycle limma listenv locfit lubridate magrittr MASS Matrix MatrixGenerics matrixStats mgcv mime ModelMetrics munsell nlme nnet numDeriv openssl pamr parallelly pillar pkgbuild pkgconfig pkgload plyr praise pROC processx prodlim progressr proxy ps purrr R6 RColorBrewer Rcpp RcppArmadillo recipes reshape2 rlang rpart rprojroot S4Arrays S4Vectors scales shape snow SparseArray SQUAREM sSeq statmod stringi stringr SummarizedExperiment survival sys testthat tibble tidyr tidyselect timechange timeDate tzdb UCSC.utils utf8 vctrs VennDiagram viridisLite waldo withr xtable XVector zlibbioc

Beginner's guide to the "MLSeq" package

Rendered fromMLSeq.Rnwusingknitr::knitron Nov 29 2024.

Last update: 2021-08-14
Started: 2014-03-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Machine learning interface for RNA-Seq data	MLSeq-package
Available classification/regression methods in 'MLSeq'	Available-classifiers availableMethods printAvailableMethods
Cervical cancer data	cervical
Fitting classification models to sequencing data	classify
Accessors for the 'confusionMat' slot.	confusionMat confusionMat,MLSeq-method confusionMat,MLSeqModelInfo-method
Accessors for the 'control' slot.	control control,MLSeq-method control<- control<-,MLSeq,list-method
'discrete.train' object	discrete.train-class
Define controlling parameters for discrete classifiers (NBLDA and PLDA)	discreteControl
Accessors for the 'inputObject' slot of an 'MLSeq' object	input input,MLSeq-method
Checks if MLSeq object is updated/modified or not.	isModified isModified,MLSeq-method isModified<- isModified<-,MLSeq,logical-method isUpdated isUpdated,MLSeq-method isUpdated<- isUpdated<-,MLSeq,logical-method
Accessors for the 'metaData' slot of an 'MLSeq' object	metaData metaData,MLSeq-method
Accessors for the 'method'.	method method,MLSeq-method method,MLSeqModelInfo-method method<- method<-,MLSeq,character-method
'MLSeq' object	MLSeq-class
'MLSeqMetaData' object	MLSeqMetaData-class
'MLSeqModelInfo' object	MLSeqModelInfo-class
Accessors for the 'modelInfo' slot of an 'MLSeq' object	modelInfo modelInfo,MLSeq-method
Accessors for the 'normalization' slot.	normalization normalization,MLSeq-method normalization,MLSeqModelInfo-method normalization<- normalization<-,MLSeq,character-method
Plot accuracy results from 'MLSeq' object	plot plot,MLSeq,ANY-method plot.MLSeq
Extract predictions from 'classify()' object	predict predict,MLSeq-method predict.MLSeq predictClassify
Accessors for the 'preProcessing' slot of an 'MLSeq' object	preProcessing preProcessing,MLSeq-method preProcessing<- preProcessing<-,MLSeq,character-method
Print method for confusion matrix	print,confMat-method print.confMat
Accessors for the 'ref' slot.	ref ref,MLSeq-method ref,MLSeqModelInfo-method ref<- ref<-,MLSeq,character-method
Accessors for the 'selectedGenes'.	selectedGenes selectedGenes,MLSeq-method
Show method for MLSeq objects	show show,discrete.train-method show,MLSeq-method show,MLSeqMetaData-method show,MLSeqModelInfo-method show,voom.train-method show.MLSeq
Accessors for the 'trainedModel' slot.	trained trained,MLSeq-method trained,MLSeqModelInfo-method
Accessors for the 'trainParameters' slot.	trainParameters trainParameters,MLSeq-method trainParameters,MLSeqModelInfo-method
Accessors for the 'transformation' slot.	transformation transformation,MLSeq-method transformation,MLSeqModelInfo-method
Update 'MLSeq' objects returnd from 'classify()'	update update,MLSeq-method update.MLSeq
'voom.train' object	voom.train-class
Define controlling parameters for voom-based classifiers	voomControl