R Under development (unstable) (2024-01-29 r85841 ucrt) -- "Unsuffered Consequences"
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> # Test DRomics on datasets with NA values
> # possible especially for apical data
> library(DRomics)
Loading required package: limma
Loading required package: DESeq2
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following object is masked from 'package:limma':

    plotMA

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

    IQR, mad, sd, var, xtabs

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

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


Attaching package: 'S4Vectors'

The following object is masked from 'package:utils':

    findMatches

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

    I, expand.grid, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

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Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: Biobase
Welcome to Bioconductor

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


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

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

    anyMissing, rowMedians


DRomics has been loaded.
!!!! IMPORTANT CHANGES IN DEFAULT PLOT ARGUMENTS !!!!
Now all the plot functions use by default a log10 scale
for dose and BMD values, except curvesplot(),
for which the use of a dose log scale requires the specification
by the user of a non null minimal value (xmin).
We also put the default value of the argument scaling at TRUE
in curvesplot() and bmdplotwithgradient(),
to focus on shapes of dose-responses rather than on their amplitude.

> visualize <- FALSE # put to TRUE for a manual check of plots
> 
> if (visualize)
+ {
+   ########### Example on apical data ################
+   ###################################################
+   data(Scenedesmus_apical)
+   head(Scenedesmus_apical)
+   set.seed(1234)
+   
+   # build of a dataset with NA values
+   Scenedesmus_apical2 <- Scenedesmus_apical
+   Scenedesmus_apical2[3, ] <- Scenedesmus_apical2[2, ]
+   Scenedesmus_apical2[2, 2] <- NA
+   Scenedesmus_apical2[3, 27] <- NA
+   Scenedesmus_apical2[3, 1] <- "growthbis"
+   head(Scenedesmus_apical2)
+   
+   (o <- continuousanchoringdata(Scenedesmus_apical2, backgrounddose = 0.1))
+   plot(o)
+   o$data
+   complete.cases(o$data)
+   sum(complete.cases(o$data))
+   o$data.mean
+   (s <- itemselect(o, select.method = "quadratic"))
+   (f <- drcfit(s))
+   plot(f)
+   (r <- bmdcalc(f))
+   r$res
+   (b <- bmdboot(r))
+   b$res
+  
+   (f.AIC <- drcfit(s, information.criterion = "AIC"))
+   (f.BIC <- drcfit(s, information.criterion = "BIC"))
+   
+   ## comparison with  the individual fit for growth
+   Scenedesmus_apical3 <- Scenedesmus_apical2[1:2, ]
+   Scenedesmus_apical3 <- Scenedesmus_apical3[, -2] # remove of the column with NA
+   head(Scenedesmus_apical3)
+   
+   (o3 <- continuousanchoringdata(Scenedesmus_apical3, backgrounddose = 0.1))
+   plot(o3)
+   (s3 <- itemselect(o3, select.method = "quadratic"))
+   (f3 <- drcfit(s3))
+   plot(f3)
+   (r3 <- bmdcalc(f3))
+   r3$res
+   
+   (b3 <- bmdboot(r3))
+   b3$res
+   b$res
+   
+   ########### Example on metabolomic data ################
+   ########################################################
+   data(Scenedesmus_metab)
+   head(Scenedesmus_metab)
+   set.seed(1234)
+   
+   # build of a dataset with NA values
+   Scenedesmus_metab2 <- Scenedesmus_metab1 <- Scenedesmus_metab[1:50, ]
+   Scenedesmus_metab2[, 1]
+   # Put NA values on a non selected item (line)
+   # and on a selected item (line 23 item "NAP_24")
+   Scenedesmus_metab2[2, 2] <- NA
+   Scenedesmus_metab2[23, 5] <- NA
+   Scenedesmus_metab2[23, 4] <- NA
+   
+   (o1 <- continuousomicdata(Scenedesmus_metab1))
+   plot(o1)
+   (o2 <- continuousomicdata(Scenedesmus_metab2, check = FALSE))
+   plot(o2)
+ 
+   (s1 <- itemselect(o1, select.method = "quadratic"))
+   (s2 <- itemselect(o2, select.method = "quadratic"))
+ 
+   (f1 <- drcfit(s1))
+   plot(f1, items = "NAP_24")
+   (f2 <- drcfit(s2))
+   plot(f2, items = "NAP_24")
+   
+   f1$fitres[1:3, ]
+   f2$fitres[1:3, ]
+ 
+   (f2.AIC <- drcfit(s2, information.criterion = "AIC"))
+   (f2.BIC <- drcfit(s2, information.criterion = "BIC"))
+   
+   (r1 <- bmdcalc(f1))
+   r1$res[1:2, ]
+   (r2 <- bmdcalc(f2))
+   r2$res[1:2, ]
+   
+   (b1 <- bmdboot(r1, niter = 100))
+   b1$res[1:2, ]
+   (b2 <- bmdboot(r2, niter = 100))
+   b2$res[1:2, ]
+   
+   
+   # Trial with NA values for each replicate of a dose
+   Scenedesmus_metab3 <- Scenedesmus_metab1 
+   Scenedesmus_metab3[c(1,23), ]
+   # Put NA values for each replicate for dose 1.79 (line 23 item "NAP_24")
+   Scenedesmus_metab3[23, 11] <- NA
+   Scenedesmus_metab3[23, 20] <- NA
+   Scenedesmus_metab3[23, 21] <- NA
+   (o3 <- continuousomicdata(Scenedesmus_metab3))
+   plot(o3)
+ 
+   (s3 <- itemselect(o3, select.method = "quadratic"))
+ 
+   (f3 <- drcfit(s3))
+   plot(f3, items = "NAP_24")
+   plot(f1, items = "NAP_24")
+   
+   f3$fitres[1:5, ]
+   f1$fitres[1:5, ]
+ 
+   (r3 <- bmdcalc(f3))
+ 
+   (b3 <- bmdboot(r3, niter = 100))
+   b3$res[1:5, ]
+   b1$res[1:5, ]
+   
+   ################## example with microarray or RNAseq data #############
+   ## It should stop
+   data(Zhou_kidney_pce)
+   Zhou <- Zhou_kidney_pce[1:1000, ]
+   Zhou[10,10] <- NA
+   try(RNAseqdata(Zhou))
+   
+   try(microarraydata(Zhou))
+  }
> 
> proc.time()
   user  system elapsed 
   9.34    0.68   10.03