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Showing content from https://github.com/HuangLabUMN/oncoPredict below:

HuangLabUMN/oncoPredict: An R package for drug response prediction and drug-gene association prediction.

Note, for questions on oncoPredict, pleast contact us at rshuang at umn.edu . The email associated with this account is not regularly checked.

Please Note the Following Bugs We are Aware of:

Selecting the batch correction option "qn" (microarray vs RNA-seq) will give an error. (caused by line 167 in CALCPHENOTYPE.R seems to lose row names)
Would recommend using normalizeQuantiles from limma package before running the models and then choosing 'none'.

Also, there is another option for microarray to RNA-Seq for batch correction, the 'standardize' option. This is currently not documented, but it works to calculate z-scores on the microarray and RNA-Seq data for integration.

Finally, oncoPredict currently expects a matrix and so it breaks if you try to build a model with just one variable. (E.g. if you were only interested in the lapatinib response, you'd have to run lapatinib and some other drug still. Not the biggest deal, but something to be aware of.

(Predict Response from Expression Data and Identify Cell line/Clinical Targets and Trends)

Additional details about this package can be found in our publication oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data

An R package for drug response prediction and drug-gene association prediction. The prepared GDSC and CTRP matrices for the calcPhenotype() are located in the oncoPredict OSF.

• For drug response prediction, use calcPhenotype.
• For pre-clinical biomarker discovery, use GLDS.
• For clinical biomarker discovery, use IDWAS (for CNV or somatic mutation association with drug response) or indicate cc=TRUE (for gene expression association with drug response) in calcPhenotype().
• The link to updated CCLE gene expression data is found at depmap. We provide GDSC1/GDSC2 pre-processed expression and response data, as well as CTRP response data and depmap's CCLE expression data (18Q2) here.

• This directory contains all the R functions included in this package.

• This directory contains vignettes which display detailed examples of the functionalities available in this package.

• IDWAS This directory contains examples of IDWAS code application for clinical drug-gene association prediction.

  • cnv.Rmd Example as to how to download CNV (copy number variation) data from the GDC database, then apply map_cnv() and idwas().
  • mut.Rmd Example as to how to download stomatic mutation data from the GDC database, then apply idwas().

• GLDS This directory contains examples of GLDS code application for pre-clinical drug-gene association prediction.

  • glds_GDSC.Rmd Example of GLDS application to GDSC data.

• calcPhenotype.Rmd Example of calcPhenotype() application.

• This directory contains .Rd (R documentation) files for each function. These files were automatically generated upon creation of the package.

• This file lists the functions to be imported and exported from this package.

• This file contains the description documentation and metadata for this package.
• Dependencies and packages recommended for oncoPredict are listed here.

Flowchart displaying the 3 primary functionalities available through oncoPredict (calcPhenotype, GLDS, IDWAS) as well as the files generated from each function and parameters. Functions and files generated are bold.

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