Plot dose response curves

Description

tppccrPlotCurves plots the logistic dose response curves, as well as the underlying fold change measurements for each TPP-CCR experiment in a study.

Usage

tppccrPlotCurves(data = NULL, fcTable = NULL, curvePars = NULL,
  resultPath = NULL, ggplotTheme = tppDefaultTheme(), nCores = "max",
  verbose = FALSE)

Arguments

Details

data is a list of expressionSet objects created by tppccrCurveFit. It contains the isobaric labels and administered drug concentrations in the phenoData and user-defined protein properties (including dose response curve parameters) in the featureData. Protein IDs are stored in the featureNames.

Measurements and compound effects for curve fitting can be provided by the arguments fcTable and cpdEffects, instead of being stored in expressionSets in data.

If specified, fcTable needs to be a long table with column names "id" (the protein names), "concentration" (the fold changes), "labelName" (the isobaric label to each measurement), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

If specified, curvePars needs to be a long table with column names "id" (the protein names), "param" (curve parameter per protein and experiment, see TPP:::drCurveParamNames(names=TRUE, info=FALSE) for possibilities), and "experiment" (e.g. "Vehicle_1" or "Panobinostat_1").

The dose response curve plots will be stored in a subfolder with name DoseResponse_Curves at the location specified by resultPath.

Value

A list of expressionSet objects storing fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration. Paths to the produced plots are stored in codefeatureData(S)$plot.

See Also

tppccrCurveFit,tppDefaultTheme

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, 
                           data=hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
tppccrTransformed <- tppccrTransform(data=tppccrNorm)
tppccrFitted <- tppccrCurveFit(data=tppccrTransformed, nCores=1)
protein1_to_5 <- sapply(tppccrFitted, function(d) d[1:5,])
tppccrPlotted <- tppccrPlotCurves(data=protein1_to_5, resultPath=getwd(), nCores=1)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(TPP)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: openxlsx
Loading required package: ggplot2
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TPP/tppccrPlotCurves.Rd_%03d_medium.png", width=480, height=480)
> ### Name: tppccrPlotCurves
> ### Title: Plot dose response curves
> ### Aliases: tppccrPlotCurves
> 
> ### ** Examples
> 
> data(hdacCCR_smallExample)
> tppccrData <- tppccrImport(configTable=hdacCCR_config, 
+                            data=hdacCCR_data)
Importing data...

The following label columns were detected:
126, 127L, 127H, 128L, 128H, 129L, 129H, 130L, 130H, 131L.
Importing CCR dataset: Panobinostat_1
Removing duplicate identifiers using quality column 'qupm'...
507 out of 507 rows kept for further analysis.
  -> Panobinostat_1 contains 507 proteins.
  -> 494 out of 507 proteins (97.44%) suitable for curve fit (criterion: > 2 valid fold changes per protein).
Importing CCR dataset: Panobinostat_2
Removing duplicate identifiers using quality column 'qupm'...
507 out of 507 rows kept for further analysis.
  -> Panobinostat_2 contains 507 proteins.
  -> 494 out of 507 proteins (97.44%) suitable for curve fit (criterion: > 2 valid fold changes per protein).


Filtering CCR dataset: Panobinostat_1
Removed proteins with zero values in column(s) 'qssm':
	494 out of 507 proteins remaining.
Filtering CCR dataset: Panobinostat_2
Removed proteins with zero values in column(s) 'qssm':
	494 out of 507 proteins remaining.
> tppccrNorm <- tppccrNormalize(data=tppccrData)
Normalizing dataset: Panobinostat_1
Normalizing dataset: Panobinostat_2
Normalization complete.

> tppccrTransformed <- tppccrTransform(data=tppccrNorm)
Transforming dataset: Panobinostat_1
Transforming dataset: Panobinostat_2
Transformation complete.

> tppccrFitted <- tppccrCurveFit(data=tppccrTransformed, nCores=1)
Fitting 169 individual dose response curves to 134 proteins.
Runtime (1 CPUs used): 1.93 secs

Dose response curves fitted sucessfully!
169 out of 169 models with sufficient data points converged (100 %).

> protein1_to_5 <- sapply(tppccrFitted, function(d) d[1:5,])
> tppccrPlotted <- tppccrPlotCurves(data=protein1_to_5, resultPath=getwd(), nCores=1)
Plotting dose response curves for 1 proteins.
Runtime (1 CPUs used): 0.28 secs

Dose response curves plotted sucessfully!
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> dev.off()
null device 
          1 
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