Quantitative data normalisation — normalizeMethods • MsCoreUtils

Function to normalise a matrix of quantitative omics data. The nature of the normalisation is controlled by the method argument, described below.

Usage

normalizeMethods()

normalize_matrix(x, method, ...)

Arguments

x: A matrix or an HDF5Matrix object to be normalised.
method: character(1) defining the normalisation method. See normalizeMethods() for available ones.
...: Additional parameters passed to the inner normalisation function.

Value

A matrix of same class as x with dimensions dim(x).

Details

The method parameter can be one of "sum", "max", "center.mean", "center.median", "div.mean", "div.median", "diff.meda", "quantiles", "quantiles.robust" or "vsn". The normalizeMethods() function returns a vector of available normalisation methods.

For "sum" and "max", each feature's intensity is divided by the maximum or the sum of the feature respectively. These two methods are applied along the features (rows).
"center.mean" and "center.median" center the respective sample (column) intensities by subtracting the respective column means or medians. "div.mean" and "div.median" divide by the column means or medians.
"diff.median" centers all samples (columns) so that they all match the grand median by subtracting the respective columns medians differences to the grand median.
Using "quantiles" or "quantiles.robust" applies (robust) quantile normalisation, as implemented in preprocessCore::normalize.quantiles() and preprocessCore::normalize.quantiles.robust(). "vsn" uses the vsn::vsn2() function. Note that the latter also glog-transforms the intensities. See respective manuals for more details and function arguments.

See also

The scale() function that centers (like center.mean above) and scales.

Author

Laurent Gatto

Examples

normalizeMethods()
#>  [1] "sum"              "max"              "center.mean"      "center.median"   
#>  [5] "div.mean"         "div.median"       "diff.median"      "quantiles"       
#>  [9] "quantiles.robust" "vsn"             

## test data
set.seed(42)
m <- matrix(rlnorm(60), 10)

normalize_matrix(m, method = "sum")
#>             [,1]        [,2]       [,3]        [,4]       [,5]        [,6]
#>  [1,] 0.31393285 0.293856133 0.05864968 0.125671426 0.09792670 0.109963217
#>  [2,] 0.04132996 0.715463967 0.01224328 0.147101553 0.05066466 0.033196581
#>  [3,] 0.11677032 0.020251317 0.06838566 0.228648076 0.17333805 0.392606576
#>  [4,] 0.21066540 0.084658010 0.37693894 0.060854278 0.05409294 0.212790434
#>  [5,] 0.12451231 0.072734432 0.55298434 0.137701921 0.02115455 0.090912443
#>  [6,] 0.13882341 0.291569272 0.10036947 0.027724557 0.23797015 0.203543139
#>  [7,] 0.50754608 0.084252023 0.08655639 0.051090905 0.04973318 0.220821420
#>  [8,] 0.13163892 0.010158049 0.02481774 0.061794488 0.61328088 0.158309924
#>  [9,] 0.75362945 0.008723409 0.15863000 0.008955514 0.06504185 0.005019784
#> [10,] 0.09883183 0.393959126 0.05548646 0.109099076 0.20271108 0.139912436

normalize_matrix(m, method = "max")
#>             [,1]       [,2]       [,3]       [,4]       [,5]        [,6]
#>  [1,] 1.00000000 0.93604775 0.18682236 0.40031309 0.31193519 0.350276239
#>  [2,] 0.05776666 1.00000000 0.01711237 0.20560302 0.07081371 0.046398677
#>  [3,] 0.29742324 0.05158171 0.17418369 0.58238473 0.44150573 1.000000000
#>  [4,] 0.55888470 0.22459344 1.00000000 0.16144333 0.14350584 0.564522299
#>  [5,] 0.22516425 0.13153073 1.00000000 0.24901595 0.03825525 0.164403285
#>  [6,] 0.47612497 1.00000000 0.34423885 0.09508737 0.81617020 0.698095301
#>  [7,] 1.00000000 0.16599877 0.17053898 0.10066259 0.09798752 0.435076596
#>  [8,] 0.21464703 0.01656345 0.04046717 0.10076050 1.00000000 0.258136084
#>  [9,] 1.00000000 0.01157520 0.21048806 0.01188318 0.08630481 0.006660813
#> [10,] 0.25086823 1.00000000 0.14084318 0.27692994 0.51454850 0.355144549

normalize_matrix(m, method = "quantiles")
#> Loading required namespace: preprocessCore
#>            [,1]      [,2]      [,3]      [,4]      [,5]      [,6]
#>  [1,] 2.4493083 2.4493083 0.9149807 1.5186864 1.1236151 1.5186864
#>  [2,] 0.2002101 5.9849751 0.2002101 2.9628356 0.9149807 0.3730709
#>  [3,] 0.9149807 0.6151294 1.5186864 5.9849751 2.9628356 5.9849751
#>  [4,] 1.5186864 0.9149807 2.9628356 0.9149807 0.6151294 2.4493083
#>  [5,] 1.1236151 1.1236151 5.9849751 2.4493083 0.2002101 0.6151294
#>  [6,] 0.3730709 1.5186864 0.7569865 0.3730709 1.5186864 0.9149807
#>  [7,] 2.9628356 0.7569865 1.1236151 0.7569865 0.3730709 2.9628356
#>  [8,] 0.6151294 0.2002101 0.3730709 0.6151294 5.9849751 0.7569865
#>  [9,] 5.9849751 0.3730709 2.4493083 0.2002101 0.7569865 0.2002101
#> [10,] 0.7569865 2.9628356 0.6151294 1.1236151 2.4493083 1.1236151

normalize_matrix(m, method = "center.mean")
#>             [,1]       [,2]       [,3]        [,4]       [,5]       [,6]
#>  [1,]  1.5256283  1.4919211 -0.8116640  0.49629688 -0.1310407 -0.1633550
#>  [2,] -1.8449643  7.6465793 -1.3791629  0.94293137 -0.6628531 -1.0864871
#>  [3,] -0.9756756 -1.9459281 -0.7055318  1.73481154  0.7745601  3.2911207
#>  [4,] -0.5305029 -1.4385876  1.8216174 -0.53665160 -0.8762926  0.3588507
#>  [5,] -0.9152902 -1.3201035  5.1062550  0.57632503 -1.1052480 -0.4492243
#>  [6,] -1.5141834 -0.3064709 -0.8973765 -0.90098354  0.1818035 -0.2245628
#>  [7,]  2.1201296 -1.4427110 -0.7744206 -0.62421961 -0.9155534  0.4293378
#>  [8,] -1.5038130 -2.1250907 -1.3760790 -0.65355895  2.8782494 -0.4491452
#>  [9,]  5.1129557 -2.1081671  0.0366477 -0.99114796 -0.7102232 -1.4930044
#> [10,] -1.4742841  1.5485584 -1.0202854 -0.04380318  0.5665980 -0.2135303