Package: MiDA 0.1.2

Elena Filatova

MiDA: Microarray Data Analysis

Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers.

Authors:Elena Filatova [aut, cre], Nikolai Sakharnov [dtc], Dmitry Knyazev [dtc], Oleg Utkin [dtc], Blokhina Scientific Research Institute of Epidemiology and Microbiology of Nizhny Novgorod, Russia [fnd]

MiDA_0.1.2.tar.gz
MiDA_0.1.2.tar.gz(r-4.5-noble)MiDA_0.1.2.tar.gz(r-4.4-noble)
MiDA_0.1.2.tgz(r-4.4-emscripten)MiDA_0.1.2.tgz(r-4.3-emscripten)
MiDA.pdf |MiDA.html✨
MiDA/json (API)

# Install 'MiDA' in R:

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

Datasets:

IMexpression - Infectious mononucleosis transcriptome
IMspecimen - Specimen features

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

1.08 score 12 scripts 124 downloads 12 exports 118 dependencies

Last updated 6 years agofrom:e0397db5e0. Checks:OK: 1 NOTE: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 19 2024
R-4.5-linux	NOTE	Dec 19 2024

Exports:MiBiClassGBODT MiDataSample MiFracData MiInflCount MiIntDepthAjust MiNorm MiNTreesAjust MiSelectSignif MiShrinkAjust MiSpecimenSample MiStatCount MiSummarize

Dependencies:annotate AnnotationDbi askpass Biobase BiocGenerics Biostrings bit bit64 blob cachem caret class cli clock codetools colorspace cpp11 crayon curl data.table DBI diagram digest dplyr e1071 fansi farver fastmap foreach future future.apply gbm genefilter generics GenomeInfoDb GenomeInfoDbData ggplot2 globals glue gower gtable hardhat httr ipred IRanges isoband iterators jsonlite KEGGREST KernSmooth labeling lattice lava lifecycle limma listenv lubridate magrittr MASS Matrix MatrixGenerics matrixStats mclust memoise mgcv mime ModelMetrics munsell nlme nnet nor1mix numDeriv openssl parallelly pillar pkgconfig plogr plyr png preprocessCore pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart RSQLite S4Vectors scales shape SQN SQUAREM statmod stringi stringr survival sys tibble tidyr tidyselect timechange timeDate tzdb UCSC.utils utf8 vctrs viridisLite withr XML xtable XVector zlibbioc

Help page	Topics
Infectious mononucleosis transcriptome	IMexpression
Specimen features	IMspecimen
Binary classification using gradient boosting over desicion trees	MiBiClassGBODT
Select matrix columns based on values of attendant vector	MiDataSample
Subset an expression matrix based on probe's feature importance	MiFracData
Mean microarray probes' feature importance from binary classification	MiInflCount
Ajust maximum depth parameter for fitting generalized boosted regression models	MiIntDepthAjust
Microarray data normalization	MiNorm
Ajust number of trees parameter for fitting generalized boosted regression models	MiNTreesAjust
Select biological markers with high fold change and classification importance	MiSelectSignif
Ajust learning rate parameter for fitting generalized boosted regression modelsfor fitting generalized boosted regression models	MiShrinkAjust
Select values from factor vector	MiSpecimenSample
FDR for microarray gene expression data	MiStatCount
Microarray data summarization	MiSummarize

Package: MiDA 0.1.2

MiDA: Microarray Data Analysis

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)