pRoloc (version 1.12.3)

chi2-methods: 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.

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)