another trial to lighten examples and tests for passing CRAN check

man/RNAseqdata.Rd

@@ -126,8 +126,8 @@ Marie-Laure Delignette-Muller
 # (see ? Zhou for details)
 #
 datafilename <- system.file("extdata", "RNAseq_sample.txt", package="DRomics")
-(o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "rlog"))
-plot(o)
+(o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "vst"))
+plot(o, range = 1e6)
 
 # If you want to use your own data set just replace datafilename, 
 # the first argument of RNAseqdata(), 
@@ -143,8 +143,8 @@ plot(o)
 # Zhou_kidney_pce (see ?Zhou for details)
 data(Zhou_kidney_pce)
 subsample <- Zhou_kidney_pce[1:1000, ]
-(o <- RNAseqdata(subsample, check = TRUE, transfo.method = "rlog"))
-plot(o)
+(o <- RNAseqdata(subsample, check = TRUE, transfo.method = "vst"))
+plot(o, range = 1e6)
 
 
 # (2) transformation with two methods on the whole data set

man/Scenedesmus.Rd

@@ -42,6 +42,8 @@ data(Scenedesmus_metab)
 head(Scenedesmus_metab)
 str(Scenedesmus_metab)
 
+\donttest{
+
 # (1.2) import and check of metabolomics data
 #
 (o_metab <- metabolomicdata(Scenedesmus_metab))
@@ -76,7 +78,7 @@ r_apical <- bmdcalc(f_apical, z = 1)
 r_apical$res
 
 
-
+}
 }
 
 \keyword{ datasets }% at least one, from doc/KEYWORDS

man/Zhou.Rd

@@ -48,6 +48,8 @@ head(Zhou_liver_pce)
 data(Zhou_liver_tce)
 head(Zhou_liver_tce)
 
+\donttest{
+
 # (2) import, check, normalization and transformation of a sample
 # of one of those datasets
 #
@@ -59,7 +61,6 @@ plot(o)
 # (3) analysis of the whole dataset (for kidney and PCE)
 # (may be long to run)
 
-\donttest{
 d <- Zhou_kidney_pce
 (o <- RNAseqdata(d))
 plot(o)

man/bmdcalc.Rd

@@ -121,8 +121,10 @@ datafilename <- system.file("extdata", "transcripto_very_small_sample.txt", pack
 plot(r) 
 
 # changing the values of z and x for BMD calculation
+\donttest{
 (rb <- bmdcalc(f, z = 2, x = 50))
-plot(rb) 
+plot(rb)
+}
 
 # (2) an example on a microarray data set (a subsample of a greater data set) 
 #

man/bmdplotwithgradient.Rd

@@ -152,7 +152,6 @@ bmdplotwithgradient(r$res, BMDtype = "zSD",
 #
 bmdplotwithgradient(r$res, BMDtype = "zSD",
                       facetby = "trend", shapeby = "model") 
-}                      
 
 # (2)
 # Plot of BMD values with color dose-response gradient
@@ -187,7 +186,6 @@ bmdplotwithgradient(extendedres, BMDtype = "zSD",
                      facetby = "path_class", 
                        shapeby = "trend") 
 
-\donttest{
 # (2.b) The same example forcing the limits of the colour gradient at other 
 # values than observed minimal and maximal values of the signal
 bmdplotwithgradient(extendedres, BMDtype = "zSD",

man/continuousanchoringdata.Rd

@@ -98,8 +98,9 @@ datafilename <- system.file("extdata", "apical_anchoring.txt", package = "DRomic
 
 o <- continuousanchoringdata(datafilename, check = TRUE)
 print(o)
+\donttest{
 plot(o)
-
+}
 # If you want to use your own data set just replace datafilename, 
 # the first argument of continuousanchoringdata(), 
 # by the name of your data file (e.g. "mydata.txt")
@@ -112,8 +113,11 @@ plot(o)
 # Use of an R object of class data.frame
 # on the same example (see ?Scenedesmus for details)
 data(Scenedesmus_apical)
-(o <- continuousanchoringdata(Scenedesmus_apical))
+o <- continuousanchoringdata(Scenedesmus_apical)
+print(o)
+\donttest{
 plot(o)
+}
 
 
 }

man/curvesplot.Rd

@@ -89,6 +89,8 @@ f <- drcfit(s_quad, progressbar = TRUE)
 #
 curvesplot(f$fitres, xmax = max(f$omicdata$dose))
 
+\donttest{
+
 # the same plot with dose in log scale (need x != 0 in input)
 curvesplot(f$fitres, xmin = 0.1, xmax = max(f$omicdata$dose),
   dose_log_transfo = TRUE)
@@ -107,9 +109,6 @@ curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend")
 curvesplot(r$res, xmax = max(f$omicdata$dose), facetby = "trend",
   free.y.scales =  TRUE)
 
-
-\donttest{
-
 # (2) 
 # Plot of all the curves without shifting y0 values to 0
 #
@@ -136,7 +135,6 @@ datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomic
 (r <- bmdcalc(f))
 
 curvesplot(f$fitres, xmax = max(f$omicdata$dose), facetby = "typology")
-}
 
 
 # (5) An example from data published by Larras et al. 2020
@@ -169,3 +167,4 @@ curvesplot(LMres, facetby = "id", npoints = 100, line.size = 1,
 
 
 }
+}

man/drcfit.Rd

@@ -153,9 +153,9 @@ datafilename <- system.file("extdata", "transcripto_very_small_sample.txt", pack
 # use the following commented line
 # datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
 
-(o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))
-(s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.05))
-(f <- drcfit(s_quad, progressbar = TRUE))
+o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess")
+s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.05)
+f <- drcfit(s_quad, progressbar = TRUE)
 
 # Default plot
 plot(f)
@@ -177,11 +177,11 @@ plot(f, plot.type = "dose_residuals", dose_log_transfo = TRUE)
 
 # plot of residuals as function of the fitted value
 plot(f, plot.type = "fitted_residuals")
-}
+
 
 # (2) an example on a microarray data set (a subsample of a greater data set) 
 #
-\donttest{
+
 datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
 
 (o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))
@@ -202,15 +202,15 @@ head(f$fitres)
 
 # Look at the table of results for unsuccessful fits
 head(f$unfitres)
-}
 
 
 # (3) Comparison of parallel and non paralell implementations on a 
 #     larger selection of items
 #
-  \donttest{
+
    s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.05)
     system.time(f1 <- drcfit(s_quad, progressbar = TRUE))
    system.time(f2 <- drcfit(s_quad, progressbar = FALSE, parallel = "snow", ncpus = 2))
+
    }
 }

man/ecdfplotwithCI.Rd

@@ -65,11 +65,11 @@ s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001)
 f <- drcfit(s_quad, progressbar = TRUE)
 r <- bmdcalc(f)
 set.seed(1) # to get reproducible results with a so small number of iterations
-(b <- bmdboot(r, niter = 10)) # with a non reasonable value for niter 
+b <- bmdboot(r, niter = 5) # with a non reasonable value for niter 
 # !!!! TO GET CORRECT RESULTS
 # !!!! niter SHOULD BE FIXED FAR LARGER , e.g. to 1000 
 # !!!! but the run will be longer 
-b$res
+
 # manual ecdf plot of the bootstrap results as an ecdf distribution 
 # on BMD, plot that could also be obtained with plot(b) 
 # in this simple case
@@ -78,6 +78,7 @@ a <- b$res[is.finite(b$res$BMD.zSD.upper), ]
 ecdfplotwithCI(variable = a$BMD.zSD, CI.lower = a$BMD.zSD.lower, 
               CI.upper = a$BMD.zSD.upper, CI.col = "red")
 
+\donttest{
 
 # (2) An example from data published by Larras et al. 2020
 # in Journal of Hazardous Materials
@@ -120,7 +121,6 @@ ecdfplotwithCI(variable = annotres$BMD.zSD,
 
 # (3) an example on a microarray data set (a subsample of a greater data set) 
 #
-\donttest{
 datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
 
 (o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))

man/itemselect.Rd

@@ -129,6 +129,8 @@ datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomic
 #
 (s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.05))
 
+\donttest{
+
 # 1.b using the linear trend test
 #
 (s_lin <- itemselect(o, select.method = "linear", FDR = 0.05))
@@ -142,3 +144,4 @@ datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomic
 (s_quad.2 <- itemselect(o, select.method = "quadratic", FDR = 0.001))
 
 }
+}

man/microarraydata.Rd

@@ -112,7 +112,8 @@ Marie-Laure Delignette-Muller
 # (an example on a subsample of a greater data set published in Larras et al. 2018
 # Transcriptomic effect of triclosan in the chlorophyte Scenedesmus vacuolatus)
 #
-datafilename <- system.file("extdata", "transcripto_sample.txt", package="DRomics")
+datafilename <- system.file("extdata", "transcripto_very_small_sample.txt", 
+  package="DRomics")
 o <- microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess")
 print(o)
 plot(o)
@@ -126,6 +127,7 @@ plot(o)
 # when it is used with its default field separator (sep argument)
 # Tabs are recommended.
 
+\donttest{
 
 # (2) normalization with other methods
 (o.2 <- microarraydata(datafilename, check = TRUE, norm.method = "quantile"))
@@ -134,3 +136,4 @@ plot(o.2)
 plot(o.3)
 
 }
+}

man/targetplot.Rd

@@ -53,6 +53,8 @@ f <- drcfit(s_quad, progressbar = TRUE)
 targetitems <- c("88.1", "1", "3", "15")
 targetplot(targetitems, f = f)
 
+\donttest{
+
 # The same plot in x log scale
 #
 targetplot(targetitems, f = f, dose_log_transfo = TRUE)
@@ -67,3 +69,4 @@ targetplot(targetitems, f = f, dose_log_transfo = TRUE) +
 targetplot(targetitems, f = f, add.fit = FALSE)
 
 }
+}

tests/examplewithRNAseq.R

@@ -1,12 +1,13 @@
 library(DRomics)
 visualize <- FALSE # put to TRUE for a manual check of plots
+doboot <- FALSE
 
 # importation and check of RNAseq data and normalization
 # with respect to library size and transformation 
 # options to put in shiny : transfo.method (2 methods, rlog or vst)
 datafilename <- system.file("extdata", "RNAseq_sample.txt", package="DRomics")
 (o <- RNAseqdata(datafilename, check = TRUE, transfo.method = "vst"))
-plot(o)
+plot(o, range = 1e6)
 
 if (visualize) # too long computation !
 {
@@ -48,27 +49,18 @@ if(visualize) # too long computation !
 }
 
 
-# item selection using the quadratic method
-# options to put in shiny : select.method (3 methods), FDR (numerical positive value < 1)
-(s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001))
 if (visualize)
 {
+  # item selection using the quadratic method
+# options to put in shiny : select.method (3 methods), FDR (numerical positive value < 1)
+  (s_quad <- itemselect(o, select.method = "quadratic", FDR = 0.001))
   (s_lin <- itemselect(o, select.method = "linear", FDR = 0.001))
   (s_ANOVA <- itemselect(o, select.method = "ANOVA", FDR = 0.001))
-}
 
-# no options in shiny
-(f <- drcfit(s_quad, progressbar = TRUE))
-if (visualize)
-{
+  (f <- drcfit(s_quad, progressbar = TRUE))
   f$fitres
   plot(f)
 
-}
-
-
-if (visualize) 
-{
   # various plot of fitted curves (without data)
   curvesplot(f$fitres, xmax = max(f$omicdata$dose), 
              facetby = "model", colorby = "model")
@@ -79,26 +71,21 @@ if (visualize)
   curvesplot(f$fitres[f$fitres$trend == "bell", ], xmax = max(f$omicdata$dose), 
              facetby = "id")
 
-}
-
-if (visualize) 
-{
   curvesplot(f$fitres[f$fitres$trend == "U", ], xmax = max(f$omicdata$dose), 
              facetby = "id")
 }
 
 
 # calculation of benchmark doses
 # options in shiny : z (numerical positive value), x (numerical positive value : percentage)
-(r <- bmdcalc(f, z = 1, x = 10))
-if (visualize)
-(r.2 <- bmdcalc(f, z = 2, x = 50))
+if (visualize) 
+{
+  (r <- bmdcalc(f, z = 1, x = 10))
+  (r.2 <- bmdcalc(f, z = 2, x = 50))
 
 # plot of BMD
 # options in shiny : BMDtype (2 possibilities), plottype (3 possibilities), by (3 possibilities)
 # hist.bins (integer for hist only)
-if (visualize) 
-{
   plot(r, BMDtype = "zSD", plottype = "ecdf", by = "none") 
 
   plot(r, BMDtype = "xfold", plottype = "ecdf", by = "none") 
@@ -109,11 +96,15 @@ if (visualize)
   plot(r, plottype = "hist", by = "trend", hist.bins = 10) 
 }
 
-# Calculation of confidence intervals on BMDs by Bootstrap
-niter <- 1000
-niter <- 10
-b <- bmdboot(r, niter = niter) # niter should be fixed at least at 1000 to get a reasonable precision
-if (visualize) plot(b)
+if (doboot)
+{
+  # Calculation of confidence intervals on BMDs by Bootstrap
+  niter <- 1000
+  niter <- 10
+  b <- bmdboot(r, niter = niter) # niter should be fixed at least at 1000 to get a reasonable precision
+  if (visualize) plot(b)
+
+}
 
 if(visualize) # too long computation !
 {

tests/examplewithanchoringdata.R

@@ -1,5 +1,6 @@
 library(DRomics)
 visualize <- FALSE # put to TRUE for a manual check of plots
+doboot <- FALSE
 
 # importation and check of apical anchoring data
 # datafilename <- system.file("extdata", "apical_anchoring.txt", package="DRomics")
@@ -59,8 +60,12 @@ if (visualize)
 }
 
 # Calculation of confidence intervals on BMDs by Bootstrap
-niter <- 1000
-niter <- 10
-(b <- bmdboot(r, niter = niter)) # niter should be fixed at least at 1000 to get a reasonable precision
-if (visualize) 
-  plot(b)
+if (doboot)
+{
+  niter <- 1000
+  niter <- 10
+  (b <- bmdboot(r, niter = niter)) # niter should be fixed at least at 1000 to get a reasonable precision
+  if (visualize) 
+    plot(b)
+
+}