chi2-methods: The PCP 'chi square' method

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

The PCP 'chi square' method

Description

In the original protein correlation profiling (PCP), Andersen et al. use the peptide normalised profiles along gradient fractions and compared them with the reference profiles (or set of profiles) by computing Chi^2 values, \frac{\sum (x_i - x_p)^2}{x_p}, where x_i is the normalised value of the peptide in fraction i and x_p is the value of the marker (from Wiese et al., 2007). The protein Chi^2 is then computed as the median of the peptide Chi^2 values. Peptides and proteins with similar profiles to the markers will have small Chi^2 values.

The chi2 methods implement this idea and compute such Chi^2 values for sets of proteins.

Methods

signature(x = "matrix", y = "matrix", method = "character", fun = "NULL", na.rm = "logical")

Compute nrow(x) times nrow(y) Chi^2 values, for each x, y feature pair. Method is one of "Andersen2003" or "Wiese2007"; the former (default) computed the Chi^2 as sum(y-x)^2/length(x), while the latter uses sum((y-x)^2/x). na.rm defines if missing values (NA and NaN) should be removed prior to summation. fun defines how to summarise the Chi^2 values; default, NULL, does not combine the Chi^2 values.

signature(x = "matrix", y = "numeric", method = "character", na.rm = "logical")

Computes nrow(x) Chi^2 values, for all the (x_i, y) pairs. See above for the other arguments.

signature(x = "numeric", y = "matrix", method = "character", na.rm = "logical")

Computes nrow(y) Chi^2 values, for all the (x, y_i) pairs. See above for the other arguments.

signature(x = "numeric", y = "numeric", method = "character", na.rm = "logical")

Computes the Chi^2 value for the (x, y) pairs. See above for the other arguments.

Author(s)

Laurent Gatto <[e-mail]>

References

Andersen, J. S., Wilkinson, C. J., Mayor, T., Mortensen, P. et al., Proteomic characterization of the human centrosome by protein correlation profiling. Nature 2003, 426, 570 - 574.

Wiese, S., Gronemeyer, T., Ofman, R., Kunze, M. et al., Proteomics characterization of mouse kidney peroxisomes by tandem mass spectrometry and protein correlation profiling. Mol. Cell. Proteomics 2007, 6, 2045 - 2057.

See Also

empPvalues

Examples

mrk <- rnorm(6)
prot <- matrix(rnorm(60), ncol = 6)
chi2(mrk, prot, method = "Andersen2003")
chi2(mrk, prot, method = "Wiese2007")

pepmark <- matrix(rnorm(18), ncol = 6)
pepprot <- matrix(rnorm(60), ncol = 6)
chi2(pepmark, pepprot)
chi2(pepmark, pepprot, fun = sum)

lgatto/pRoloc documentation built on Oct. 23, 2024, 12:51 a.m.