pRRopheticPredict: Given a gene expression matrix, predict drug senstivity for a...

In xlucpu/MOVICS: Multi-Omics integration and VIsualization in Cancer Subtyping

Description

Given a gene expression matrix, predict drug senstivity for a drug in CGP.

Usage

pRRopheticPredict(
  testMatrix,
  drug,
  tissueType = "all",
  batchCorrect = "eb",
  powerTransformPhenotype = TRUE,
  removeLowVaryingGenes = 0.2,
  minNumSamples = 10,
  selection = -1,
  printOutput = TRUE,
  removeLowVaringGenesFrom = "homogenizeData",
  dataset = "cgp2014"
)

Arguments

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	How should training and test data matrices be homogenized. Choices are "eb" (default) for ComBat, "qn" for quantiles normalization or "none" for no homogenization.
powerTransformPhenotype	Should the phenotype be power transformed before we fit the regression model? Default to TRUE, set to FALSE if the phenotype is already known to be highly normal.
removeLowVaryingGenes	What proportion of low varying genes should be removed? 20 percent be default
minNumSamples	How many training and test samples are requried. Print an error if below this threshold
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
removeLowVaringGenesFrom	what kind of genes should be removed
dataset	version of GDSC dataset

Value

a gene expression matrix that does not contain duplicate gene ids

Author(s)

Paul Geeleher, Nancy Cox, R. Stephanie Huang

xlucpu/MOVICS documentation built on July 24, 2021, 9:23 p.m.