pRRopheticLogisticPredict: Predict from the CGP data using a logistic model
In xlucpu/MOVICS: Multi-Omics integration and VIsualization in Cancer Subtyping

Description Usage Arguments Value Author(s)

Predict from the CGP data using a logistic model.

pRRopheticLogisticPredict(
  testMatrix,
  drug,
  tissueType = "all",
  batchCorrect = "eb",
  minNumSamples = 10,
  selection = -1,
  printOutput = TRUE,
  numGenesSelected = 1000,
  numSens = 15,
  numRes = 55
)

`testMatrix`	a gene expression matrix with gene names as row ids and sample names as column ids.
`drug`	the name of the drug for which you would like to predict sensitivity, one of A.443654, A.770041, ABT.263, ABT.888, AG.014699, AICAR, AKT.inhibitor.VIII, AMG.706, AP.24534, AS601245, ATRA, AUY922, Axitinib, AZ628, AZD.0530, AZD.2281, AZD6244, AZD6482, AZD7762, AZD8055, BAY.61.3606, Bexarotene, BI.2536, BIBW2992, Bicalutamide, BI.D1870, BIRB.0796, Bleomycin, BMS.509744, BMS.536924, BMS.708163, BMS.754807, Bortezomib, Bosutinib, Bryostatin.1, BX.795, Camptothecin, CCT007093, CCT018159, CEP.701, CGP.082996, CGP.60474, CHIR.99021, CI.1040, Cisplatin, CMK, Cyclopamine, Cytarabine, Dasatinib, DMOG, Docetaxel, Doxorubicin, EHT.1864, Elesclomol, Embelin, Epothilone.B, Erlotinib, Etoposide, FH535, FTI.277, GDC.0449, GDC0941, Gefitinib, Gemcitabine, GNF.2, GSK269962A, GSK.650394, GW.441756, GW843682X, Imatinib, IPA.3, JNJ.26854165, JNK.9L, JNK.Inhibitor.VIII, JW.7.52.1, KIN001.135, KU.55933, Lapatinib, Lenalidomide, LFM.A13, Metformin, Methotrexate, MG.132, Midostaurin, Mitomycin.C, MK.2206, MS.275, Nilotinib, NSC.87877, NU.7441, Nutlin.3a, NVP.BEZ235, NVP.TAE684, Obatoclax.Mesylate, OSI.906, PAC.1, Paclitaxel, Parthenolide, Pazopanib, PD.0325901, PD.0332991, PD.173074, PF.02341066, PF.4708671, PF.562271, PHA.665752, PLX4720, Pyrimethamine, QS11, Rapamycin, RDEA119, RO.3306, Roscovitine, Salubrinal, SB.216763, SB590885, Shikonin, SL.0101.1, Sorafenib, S.Trityl.L.cysteine, Sunitinib, Temsirolimus, Thapsigargin, Tipifarnib, TW.37, Vinblastine, Vinorelbine, Vorinostat, VX.680, VX.702, WH.4.023, WO2009093972, WZ.1.84, X17.AAG, X681640, XMD8.85, Z.LLNle.CHO, ZM.447439.
`tissueType`	specify if you would like to traing the models on only a subset of the CGP cell lines (based on the tissue type from which the cell lines originated). This be one any of "all" (for everything, default option), "allSolidTumors" (everything except for blood), "blood", "breast", "CNS", "GI tract" ,"lung", "skin", "upper aerodigestive"
`batchCorrect`	The type of batch correction to be used. Options are "eb", "none", .....
`minNumSamples`	The minimum number of test samples, print an error if the number of columns of "testExprData" is below this threshold. A large number of test samples may be necessary to correct for batch effects.
`selection`	How should duplicate gene ids be handled. Default is -1 which asks the user. 1 to summarize by their or 2 to disguard all duplicates.
`printOutput`	Set to FALSE to supress output
`numGenesSelected`	Specifies how genes are selected for "variableSelectionMethod". Options are "tTests", "pearson" and "spearman".
`numSens`	The number of sensitive cell lines to be fit in the logistic regression model.
`numRes`	The number of resistant cell lines fit in the logistic regression model.