Skip to contents

Predict drug senstivity for a drug in CGP,and visualize with ggplot.

drugsensitivity.Rd

Predict drug senstivity for a drug in CGP,and visualize with ggplot.

Usage

drugsensitivity(
  testMatrix = exprData,
  studyResponse = studyResponse,
  bortIndex = bortIndex,
  drug = "Bosutinib",
  tissueType = "all",
  batchCorrect = "eb",
  selection = 1,
  dataset = "cgp2014",
  colours = c("#e94753", "#47a4e9")
)

Arguments

testMatrix

a gene expression matrix with gene names as row ids and sample names as column ids

studyResponse

Drug handling information

bortIndex

Drug handling information

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.

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.

dataset

The datasets you want to choose, including 2014 and 2016

colours

The color you are interested

Value

A figure

References

Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One. 2014 Sep 17;9(9):e107468. doi: 10.1371/journal.pone.0107468. PMID: 25229481; PMCID: PMC4167990.

Examples

data(exprData)
data(studyResponse)
drugsensitivity(testMatrix=exprData,
studyResponse=studyResponse,
bortIndex=bortIndex,
drug="Bosutinib",
tissueType = "all",
batchCorrect = "eb",
selection=1,
dataset = "cgp2014",colours=c('#e94753','#47a4e9'))
#> Error in getCGPinfo(drug, tissueType, dataset): object 'drugSensitivityDataCgp' not found