Aggreagate quantitative features

These functions take a matrix of quantitative features x and aggregate the features (rows) according to either a vector (or factor) INDEX or an adjacency matrix MAT. The aggregation method is defined by function FUN.

Adjacency matrices are an elegant way to explicitly encode for shared peptides (see example below) during aggregation.

Usage

colMeansMat(x, MAT, na.rm = FALSE)

colSumsMat(x, MAT, na.rm = FALSE)

aggregate_by_matrix(x, MAT, FUN, ...)

aggregate_by_vector(x, INDEX, FUN, ...)

Arguments

x: A matrix of mode numeric or an HDF5Matrix object of type numeric.
MAT: An adjacency matrix that defines peptide-protein relations with nrow(MAT) == nrow(x): a non-missing/non-null value at position (i,j) indicates that peptide i belong to protein j. This matrix is tyically binary but can also contain weighted relations.
na.rm: A logical(1) indicating whether the missing values (including NaN) should be omitted from the calculations or not. Defaults to FALSE.
FUN: A function to be applied to the subsets of x.
...: Additional arguments passed to FUN.
INDEX: A vector or factor of length nrow(x).

Value

aggregate_by_matrix() returns a matrix (or Matrix) of dimensions ncol(MAT) and ncol(x), with dimnamesequal tocolnames(x)andrownames(MAT)`.

aggregate_by_vector() returns a new matrix (if x is a matrix) or HDF5Matrix (if x is an HDF5Matrix) of dimensions length(INDEX) and ncol(x), with dimnames equal tocolnames(x)andINDEX`.

Vector-based aggregation functions

When aggregating with a vector/factor, user-defined functions must return a vector of length equal to ncol(x) for each level in INDEX. Examples thereof are:

medianPolish() to fits an additive model (two way decomposition) using Tukey's median polish procedure using stats::medpolish();
robustSummary() to calculate a robust aggregation using MASS::rlm();
base::colMeans() to use the mean of each column;
base::colSums() to use the sum of each column;
matrixStats::colMedians() to use the median of each column.

Matrix-based aggregation functions

When aggregating with an adjacency matrix, user-defined functions must return a new matrix. Examples thereof are:

colSumsMat(x, MAT) aggregates by the summing the peptide intensities for each protein. Shared peptides are re-used multiple times.
colMeansMat(x, MAT) aggregation by the calculating the mean of peptide intensities. Shared peptides are re-used multiple times.

Handling missing values

By default, missing values in the quantitative data will propagate to the aggregated data. You can provide na.rm = TRUE to most functions listed above to ignore missing values, except for robustSummary() where you should supply na.action = na.omit (see ?MASS::rlm).

Author

Laurent Gatto and Samuel Wieczorek (aggregation from an adjacency matrix).

Examples


x <- matrix(c(10.39, 17.16, 14.10, 12.85, 10.63, 7.52, 3.91,
              11.13, 16.53, 14.17, 11.94, 11.51, 7.69, 3.97,
              11.93, 15.37, 14.24, 11.21, 12.29, 9.00, 3.83,
              12.90, 14.37, 14.16, 10.12, 13.33, 9.75, 3.81),
            nrow = 7,
            dimnames = list(paste0("Pep", 1:7), paste0("Sample", 1:4)))
x
#>      Sample1 Sample2 Sample3 Sample4
#> Pep1   10.39   11.13   11.93   12.90
#> Pep2   17.16   16.53   15.37   14.37
#> Pep3   14.10   14.17   14.24   14.16
#> Pep4   12.85   11.94   11.21   10.12
#> Pep5   10.63   11.51   12.29   13.33
#> Pep6    7.52    7.69    9.00    9.75
#> Pep7    3.91    3.97    3.83    3.81

## -------------------------
## Aggregation by vector
## -------------------------

(k <- paste0("Prot", c("B", "E", "X", "E", "B", "B", "E")))
#> [1] "ProtB" "ProtE" "ProtX" "ProtE" "ProtB" "ProtB" "ProtE"

aggregate_by_vector(x, k, colMeans)
#>         Sample1  Sample2  Sample3   Sample4
#> ProtB  9.513333 10.11000 11.07333 11.993333
#> ProtE 11.306667 10.81333 10.13667  9.433333
#> ProtX 14.100000 14.17000 14.24000 14.160000
aggregate_by_vector(x, k, robustSummary)
#>         Sample1  Sample2  Sample3  Sample4
#> ProtB  9.513333 10.15800 11.07333 11.99333
#> ProtE 11.451493 10.81333 10.13667  9.22360
#> ProtX 14.100000 14.17000 14.24000 14.16000
aggregate_by_vector(x, k, medianPolish)
#>       Sample1 Sample2 Sample3 Sample4
#> ProtB   10.39   11.13   11.93   12.90
#> ProtE   12.85   11.94   11.21   10.12
#> ProtX   14.10   14.17   14.24   14.16

## -------------------------
## Aggregation by matrix
## -------------------------

adj <- matrix(c(1, 0, 0, 1, 1, 1, 0, 0,
                1, 0, 1, 0, 0, 1, 0, 0,
                1, 0, 0, 0, 1),
              nrow = 7,
              dimnames = list(paste0("Pep", 1:7),
                              paste0("Prot", c("B", "E", "X"))))
adj
#>      ProtB ProtE ProtX
#> Pep1     1     0     0
#> Pep2     0     1     0
#> Pep3     0     0     1
#> Pep4     1     1     0
#> Pep5     1     0     0
#> Pep6     1     0     0
#> Pep7     0     1     1

## Peptide 4 is shared by 2 proteins (has a rowSums of 2),
## namely proteins B and E
rowSums(adj)
#> Pep1 Pep2 Pep3 Pep4 Pep5 Pep6 Pep7 
#>    1    1    1    2    1    1    2 

aggregate_by_matrix(x, adj, colSumsMat)
#>       Sample1 Sample2 Sample3 Sample4
#> ProtB   41.39   42.27   44.43   46.10
#> ProtE   33.92   32.44   30.41   28.30
#> ProtX   18.01   18.14   18.07   17.97
aggregate_by_matrix(x, adj, colMeansMat)
#>        Sample1  Sample2  Sample3   Sample4
#> ProtB 10.34750 10.56750 11.10750 11.525000
#> ProtE 11.30667 10.81333 10.13667  9.433333
#> ProtX  9.00500  9.07000  9.03500  8.985000

## ---------------
## Missing values
## ---------------

x <- matrix(c(NA, 2:6), ncol = 2,
            dimnames = list(paste0("Pep", 1:3),
                            c("S1", "S2")))
x
#>      S1 S2
#> Pep1 NA  4
#> Pep2  2  5
#> Pep3  3  6

## simply use na.rm = TRUE to ignore missing values
## during the aggregation

(k <- LETTERS[c(1, 1, 2)])
#> [1] "A" "A" "B"
aggregate_by_vector(x, k, colSums)
#>   S1 S2
#> A NA  9
#> B  3  6
aggregate_by_vector(x, k, colSums, na.rm = TRUE)
#>   S1 S2
#> A  2  9
#> B  3  6

(adj <- matrix(c(1, 1, 0, 0, 0, 1), ncol = 2,
               dimnames = list(paste0("Pep", 1:3),
                           c("A", "B"))))
#>      A B
#> Pep1 1 0
#> Pep2 1 0
#> Pep3 0 1
aggregate_by_matrix(x, adj, colSumsMat, na.rm = FALSE)
#>   S1 S2
#> A NA  9
#> B  3  6
aggregate_by_matrix(x, adj, colSumsMat, na.rm = TRUE)
#>   S1 S2
#> A  2  9
#> B  3  6