Scales the intensities of all features using
$$\widetilde{x}_{ij}=\frac{x_{ij}-\overline{x}_{i}}{s_i}\cdot \frac{\overline{x}_{i}}{s_i}$$
where \(\widetilde{x}_{ij}\) is the intensity of sample \(j\), feature \(i\) after scaling,
\(x_{ij}\) is the intensity of sample \(j\), feature \(i\) before scaling, \(\overline{x}_{i}\) is the mean of intensities of feature \(i\) across all samples
and \({s_i}\) is the standard deviation of intensities of feature \(i\) across all samples. Note that \(\frac{\overline{x}_{i}}{s_i} = \frac{{1}}{CV}\) where CV is the coefficient of variation across all samples.
scale_vast_grouped is a variation of this function that uses a group-specific coefficient of variation.
In other words, it performs autoscaling (scale_auto) and divides by the coefficient of variation, thereby reducing the importance of features with a poor reproducibility.
Arguments
- data
A tidy tibble created by
read_featuretable.
References
R. A. Van Den Berg, H. C. Hoefsloot, J. A. Westerhuis, A. K. Smilde, M. J. Van Der Werf, BMC Genomics 2006, 7, 142, DOI 10.1186/1471-2164-7-142.
J. Sun, Y. Xia, Genes & Diseases 2024, 11, 100979, DOI 10.1016/j.gendis.2023.04.018.
Examples
toy_metaboscape %>%
scale_vast()
#> # A tibble: 110 × 8
#> UID Feature Sample Intensity RT `m/z` Name Formula
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 1 161.10519 Da 26.98 s Sample1 -1.52 0.45 162. NA C7H15N…
#> 2 2 276.13647 Da 27.28 s Sample1 -1.42 0.45 277. Octyl hyd… C16H22…
#> 3 3 304.24023 Da 32.86 s Sample1 NA 0.55 305. Arachidon… C20H32…
#> 4 4 417.23236 Da 60.08 s Sample1 -0.811 1 418. NA NA
#> 5 5 104.10753 Da 170.31 s Sample1 -0.566 2.84 105. NA C5H14NO
#> 6 6 105.04259 Da 199.80 s Sample1 0.259 3.33 106. NA C3H8NO3
#> 7 7 237.09204 Da 313.24 s Sample1 NA 5.22 238. Ketamine C13H16…
#> 8 8 745.09111 Da 382.23 s Sample1 -0.951 6.37 746. NADPH C21H30…
#> 9 9 427.02942 Da 424.84 s Sample1 -0.942 7.08 428. ADP C10H15…
#> 10 10 1284.34904 Da 498.94 s Sample1 NA 8.32 1285. NA NA
#> # ℹ 100 more rows