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Metabolomic and apical data sets for the effect of triclosan in the chlorophyte Scenedesmus vacuolatus.

Usage

data(Scenedesmus_metab)
data(Scenedesmus_apical)

Format

Scenedesmus_metab contains one row per metabolite, with the first column corresponding to the identifier of each metabolite, and the other columns giving the log10 tranformed area under the curve for each replicate at each concentration. In the first line, after the name for the identifier column, we have the tested concentrations for each corresponding replicate.

Scenedesmus_apical contains one row per apical endpoint, with the first column corresponding to the identifier of each endpoint, and the other columns giving the measured value of this each endpoint for each replicate at each concentration. In the first line, after the name for the identifier column, we have the tested concentrations for each corresponding replicate.

Source

Larras, F., Billoir, E., Scholz, S., Tarkka, M., Wubet, T., Delignette-Muller, M. L., & Schmitt-Jansen, M. (2020). A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus. Journal of Hazardous Materials, 122727.

Examples

# (1.1) load of metabolomics data
#
data(Scenedesmus_metab)
head(Scenedesmus_metab)
#>           V1       V2       V3       V4       V5       V6       V7       V8
#> 1 metab.code 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7.760000
#> 2      NAP_1 4.338845 4.727077 4.664407 4.741994 4.338845 4.667462 4.338845
#> 3      NAP_2 5.923194 5.997305 5.897229 6.092802 5.966068 5.733371 5.548711
#> 4      NAP_3 4.780252 4.890248 5.103817 5.060089 5.037458 4.829921 4.948354
#> 5      NAP_4 4.027370 4.457973 4.027370 4.027370 4.350887 4.027370 4.027370
#> 6      NAP_5 5.269317 4.660272 5.407287 5.282763 4.660272 4.660272 5.306268
#>         V9      V10      V11      V12      V13      V14      V15      V16
#> 1 4.780000 2.920000 1.790000 1.100000 0.690000 7.760000 7.760000 4.780000
#> 2 4.639875 4.684765 4.338845 4.338845 4.855040 4.338845 4.927042 4.338845
#> 3 5.478389 5.708228 5.585534 5.832640 5.853180 5.425401 5.590360 5.478412
#> 4 4.863668 4.923078 4.922019 4.870656 5.071359 4.869461 5.115907 5.135603
#> 5 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370 4.027370
#> 6 4.660272 5.342616 5.295892 4.660272 5.319847 5.104808 4.660272 5.219089
#>        V17      V18      V19      V20      V21      V22      V23      V24
#> 1 4.780000 2.920000 2.920000 1.790000 1.790000 1.100000 1.100000 0.690000
#> 2 4.338845 4.338845 4.733983 4.338845 4.338845 4.338845 5.078072 4.338845
#> 3 5.460895 5.485156 5.448148 5.582259 5.700495 5.976869 5.435696 5.875684
#> 4 5.002352 5.325395 4.479222 4.979134 5.164020 5.067967 5.279681 5.166167
#> 5 4.027370 4.027370 4.027370 4.521230 4.328400 4.422097 4.689859 4.492537
#> 6 4.660272 4.660272 4.961302 4.660272 4.660272 4.660272 5.455795 5.462184
#>        V25
#> 1 0.690000
#> 2 4.703429
#> 3 5.656397
#> 4 5.018734
#> 5 4.027370
#> 6 4.660272
str(Scenedesmus_metab)
#> 'data.frame':	225 obs. of  25 variables:
#>  $ V1 : chr  "metab.code" "NAP_1" "NAP_2" "NAP_3" ...
#>  $ V2 : num  0 4.34 5.92 4.78 4.03 ...
#>  $ V3 : num  0 4.73 6 4.89 4.46 ...
#>  $ V4 : num  0 4.66 5.9 5.1 4.03 ...
#>  $ V5 : num  0 4.74 6.09 5.06 4.03 ...
#>  $ V6 : num  0 4.34 5.97 5.04 4.35 ...
#>  $ V7 : num  0 4.67 5.73 4.83 4.03 ...
#>  $ V8 : num  7.76 4.34 5.55 4.95 4.03 ...
#>  $ V9 : num  4.78 4.64 5.48 4.86 4.03 ...
#>  $ V10: num  2.92 4.68 5.71 4.92 4.03 ...
#>  $ V11: num  1.79 4.34 5.59 4.92 4.03 ...
#>  $ V12: num  1.1 4.34 5.83 4.87 4.03 ...
#>  $ V13: num  0.69 4.86 5.85 5.07 4.03 ...
#>  $ V14: num  7.76 4.34 5.43 4.87 4.03 ...
#>  $ V15: num  7.76 4.93 5.59 5.12 4.03 ...
#>  $ V16: num  4.78 4.34 5.48 5.14 4.03 ...
#>  $ V17: num  4.78 4.34 5.46 5 4.03 ...
#>  $ V18: num  2.92 4.34 5.49 5.33 4.03 ...
#>  $ V19: num  2.92 4.73 5.45 4.48 4.03 ...
#>  $ V20: num  1.79 4.34 5.58 4.98 4.52 ...
#>  $ V21: num  1.79 4.34 5.7 5.16 4.33 ...
#>  $ V22: num  1.1 4.34 5.98 5.07 4.42 ...
#>  $ V23: num  1.1 5.08 5.44 5.28 4.69 ...
#>  $ V24: num  0.69 4.34 5.88 5.17 4.49 ...
#>  $ V25: num  0.69 4.7 5.66 5.02 4.03 ...

# \donttest{

# (1.2) import and check of metabolomics data
#
(o_metab <- continuousomicdata(Scenedesmus_metab))
#> Warning: 
#> We recommend you to check that your omic data were correctly pretreated
#> before importation. In particular data (e.g. metabolomic signal) should
#> have been log-transformed, without replacing 0 values by NA values
#> (consider using the half minimum method instead for example).
#> Elements of the experimental design in order to check the coding of the data:
#> Tested doses and number of replicates for each dose:
#> 
#>    0 0.69  1.1 1.79 2.92 4.78 7.76 
#>    6    3    3    3    3    3    3 
#> Number of items: 224 
#> Identifiers of the first 20 items:
#> 
#>  [1] "NAP_1"  "NAP_2"  "NAP_3"  "NAP_4"  "NAP_5"  "NAP_6"  "NAP_7"  "NAP_8" 
#>  [9] "NAP_9"  "NAP_11" "NAP_13" "NAP_14" "NAP_15" "NAP_16" "NAP_17" "NAP_18"
#> [17] "NAP_19" "NAP_20" "NAP_21" "NAP_22"
plot(o_metab)


# (2.1) load of apical data
#
data(Scenedesmus_apical)
head(Scenedesmus_apical)
#>               V1      V2       V3       V4      V5      V6       V7      V8
#> 1       endpoint 0.10000  0.10000  0.10000 0.10000 0.10000  0.10000  0.1000
#> 2         growth 4.05405 -3.86402 -0.40118 0.21115 4.78474 -0.28645 -1.9627
#> 3 photosynthesis 0.00000  0.00000  0.00000 0.00000 0.00000  0.00000  0.0000
#>         V9     V10      V11      V12      V13      V14      V15      V16
#> 1  0.10000 0.10000  0.10000  0.10000  0.10000  2.40000  2.40000  2.40000
#> 2 -2.53559 8.21172 -4.63598 -1.64234 -1.93339 -7.98142  7.33857  3.34705
#> 3  0.00000 0.00000  0.00000  0.00000  0.00000 -7.18853 -4.50187 -8.42766
#>        V17       V18       V19      V20       V21      V22       V23      V24
#> 1  2.40000   2.40000   2.40000  3.80000   3.80000  6.20000   6.20000 10.10000
#> 2 -2.55707  -8.68987 -11.48974  1.41102 -10.93750 -4.08453 -12.98564  2.45072
#> 3 -5.63618 -11.47670  -6.96251 -7.53671  -8.01109 -6.37088  -8.79645 -7.67328
#>        V25      V26       V27      V28      V29       V30       V31      V32
#> 1 10.10000 16.50000  16.50000 16.50000 16.50000  16.50000  16.50000 26.80000
#> 2 -3.71622 27.30152  29.05854 17.60842 14.03268  18.95971  16.65211 64.81147
#> 3 -7.28024 -6.78936 -12.60403 -5.77177 -5.86055 -18.67683 -14.58052 -5.76963
#>       V33      V34      V35      V36      V37
#> 1 26.8000 43.50000 43.50000 70.70000 70.70000
#> 2 58.3826 77.06083 72.71959 72.94341 81.89225
#> 3  5.8997 22.08560 19.35689 21.28191 35.79860
str(Scenedesmus_apical)
#> 'data.frame':	3 obs. of  37 variables:
#>  $ V1 : chr  "endpoint" "growth" "photosynthesis"
#>  $ V2 : num  0.1 4.05 0
#>  $ V3 : num  0.1 -3.86 0
#>  $ V4 : num  0.1 -0.401 0
#>  $ V5 : num  0.1 0.211 0
#>  $ V6 : num  0.1 4.78 0
#>  $ V7 : num  0.1 -0.286 0
#>  $ V8 : num  0.1 -1.96 0
#>  $ V9 : num  0.1 -2.54 0
#>  $ V10: num  0.1 8.21 0
#>  $ V11: num  0.1 -4.64 0
#>  $ V12: num  0.1 -1.64 0
#>  $ V13: num  0.1 -1.93 0
#>  $ V14: num  2.4 -7.98 -7.19
#>  $ V15: num  2.4 7.34 -4.5
#>  $ V16: num  2.4 3.35 -8.43
#>  $ V17: num  2.4 -2.56 -5.64
#>  $ V18: num  2.4 -8.69 -11.48
#>  $ V19: num  2.4 -11.49 -6.96
#>  $ V20: num  3.8 1.41 -7.54
#>  $ V21: num  3.8 -10.94 -8.01
#>  $ V22: num  6.2 -4.08 -6.37
#>  $ V23: num  6.2 -13 -8.8
#>  $ V24: num  10.1 2.45 -7.67
#>  $ V25: num  10.1 -3.72 -7.28
#>  $ V26: num  16.5 27.3 -6.79
#>  $ V27: num  16.5 29.1 -12.6
#>  $ V28: num  16.5 17.61 -5.77
#>  $ V29: num  16.5 14.03 -5.86
#>  $ V30: num  16.5 19 -18.7
#>  $ V31: num  16.5 16.7 -14.6
#>  $ V32: num  26.8 64.81 -5.77
#>  $ V33: num  26.8 58.4 5.9
#>  $ V34: num  43.5 77.1 22.1
#>  $ V35: num  43.5 72.7 19.4
#>  $ V36: num  70.7 72.9 21.3
#>  $ V37: num  70.7 81.9 35.8

# (2.2) import and check of apical data
#
(o_apical <- continuousanchoringdata(Scenedesmus_apical, backgrounddose = 0.1))
#> Warning: 
#> We recommend you to check that your anchoring data are continuous and
#> defined in a scale that enable the use of a normal error model (needed
#> at each step of the workflow including the selection step).
#> Elements of the experimental design in order to check the coding of the data:
#> Tested doses and number of replicates for each dose:
#> 
#>    0  2.4  3.8  6.2 10.1 16.5 26.8 43.5 70.7 
#>   12    6    2    2    2    6    2    2    2 
#> Number of endpoints: 2 
#> Names of the endpoints:
#> [1] "growth"         "photosynthesis"
# It is here necessary to define the background dose as there is no dose at 0 in the data
# The BMD cannot be computed without defining the background level
plot(o_apical)
#> Warning: log-10 transformation introduced infinite values.


# (2.3) selection of responsive endpoints on apical data
#
(s_apical <- itemselect(o_apical, select.method = "quadratic", FDR = 0.05))
#> Number of selected items using a quadratic trend test with an FDR of 0.05: 2
#> Identifiers of the responsive items:
#> [1] "growth"         "photosynthesis"

# (2.4) fit of dose-response models on apical data
#
(f_apical <- drcfit(s_apical, progressbar = TRUE))
#> The fitting may be long if the number of selected items is high.
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
#> Results of the fitting using the AICc to select the best fit model
#> Distribution of the chosen models among the 2 fitted dose-response curves:
#> 
#>             Hill           linear      exponential     Gauss-probit 
#>                0                0                0                2 
#> log-Gauss-probit 
#>                0 
#> Distribution of the trends (curve shapes) among the 2 fitted dose-response curves:
#> 
#> U 
#> 2 
f_apical$fitres
#>               id irow    adjpvalue        model nbpar        b        c
#> 1         growth    1 4.120696e-17 Gauss-probit     4 12.20929 77.03040
#> 2 photosynthesis    2 2.669437e-13 Gauss-probit     4 16.87413 28.65168
#>          d        e         f    SDres typology trend        y0 yatdosemax
#> 1 77.03040  5.38810 -84.19789 5.236305     GP.U     U  0.644969   77.03035
#> 2 28.65168 12.88431 -40.04502 3.788006     GP.U     U -1.267411   28.53860
#>     yrange maxychange  xextrem    yextrem
#> 1 84.19784   76.38538  5.38810  -7.167487
#> 2 39.93195   29.80601 12.88431 -11.393343
plot(f_apical) 
#> Warning: log-10 transformation introduced infinite values.
#> Warning: log-10 transformation introduced infinite values.
#> Warning: log-10 transformation introduced infinite values.

plot(f_apical, dose_log_trans = TRUE)
#> Warning: log-10 transformation introduced infinite values.
#> Warning: log-10 transformation introduced infinite values.
#> Warning: log-10 transformation introduced infinite values.

plot(f_apical, plot.type = "dose_residuals")
#> Warning: log-10 transformation introduced infinite values.


# (2.5) Benchmark dose calculation on apical data
#
r_apical <- bmdcalc(f_apical, z = 1)
r_apical$res
#>               id irow    adjpvalue        model nbpar        b        c
#> 1         growth    1 4.120696e-17 Gauss-probit     4 12.20929 77.03040
#> 2 photosynthesis    2 2.669437e-13 Gauss-probit     4 16.87413 28.65168
#>          d        e         f    SDres typology trend        y0 yatdosemax
#> 1 77.03040  5.38810 -84.19789 5.236305     GP.U     U  0.644969   77.03035
#> 2 28.65168 12.88431 -40.04502 3.788006     GP.U     U -1.267411   28.53860
#>     yrange maxychange  xextrem    yextrem  BMD.zSD   BMR.zSD  BMD.xfold
#> 1 84.19784   76.38538  5.38810  -7.167487 2.344346 -4.591336 0.02400000
#> 2 39.93195   29.80601 12.88431 -11.393343 2.978888 -5.055417 0.09375678
#>    BMR.xfold
#> 1  0.5804721
#> 2 -1.3941523


# }