Enrichment or depletion of a GO category within a class of genes: which test?

MOTIVATION: A number of available program packages determine the significant enrichments and/or depletions of GO categories among a class of genes of interest. Whereas a correct formulation of the problem leads to a single exact null distribution, these GO tools use a large variety of statistical tests whose denominations often do not clarify the underlying P-value computations. SUMMARY: We review the different formulations of the problem and the tests they lead to: the binomial, chi2, equality of two probabilities, Fisher's exact and hypergeometric tests. We clarify the relationships existing between these tests, in particular the equivalence between the hypergeometric test and Fisher's exact test. We recall that the other tests are valid only for large samples, the test of equality of two probabilities and the chi2-test being equivalent. We discuss the appropriateness of one- and two-sided P-values, as well as some discreteness and conservatism issues. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting fluorescent protein expression and co-localisation on single secretory vesicles with linear spectral unmixing.

Many questions in cell biology and biophysics involve the quantitation of co-localisation and the interaction of proteins tagged with different fluorophores. However, the incomplete separation of the different colour channels due to the presence of autofluorescence, along with cross-excitation and emission "bleed-through" of one colour channel into the other, all combine to render the interpretation of multi-band images ambiguous. Here we introduce a new live-cell epifluorescence spectral imaging and linear unmixing technique for classifying resolution-limited point objects containing multiple fluorophores. We demonstrate the performance of our technique by detecting, at the single-vesicle level, the co-expression of the vesicle-associated membrane protein, VAMP-2 (also called synaptobrevin-2), linked to either enhanced green fluorescent protein (EGFP) or citrine [a less pH-sensitive variant of enhanced yellow fluorescent protein (EYFP)], in mouse cortical astrocytes. In contrast, the co-expression of VAMP-2-citrine and the lysosomal transporter sialine fused to EGFP resulted in little overlap. Spectral imaging and linear unmixing permit us to fingerprint the expression of spectrally overlapping fluorescent proteins on single secretory organelles in the presence of a spectrally broad autofluorescence. Our technique provides a robust alternative to error-prone dual- or triple colour co-localisation studies.

Jacobian conditioning analysis for model validation.

Our aim is to stress the importance of Jacobian matrix conditioning for model validation. We also comment on Monari and Dreyfus (2002), where, following Rivals and Personnaz (2000), it is proposed to discard neural candidates that are likely to overfit and/or for which quantities of interest such as confidence intervals cannot be computed accurately. In Rivals and Personnaz (2000), we argued that such models are to be discarded on the basis of the condition number of their Jacobian matrix. But Monari and Dreyfus (2002) suggest making the decision on the basis of the computed values of the leverages, the diagonal elements of the projection matrix on the range of the Jacobian, or "hat" matrix: they propose to discard a model if computed leverages are outside some theoretical bounds, pretending that it is the symptom of the Jacobian rank deficiency. We question this proposition because, theoretically, the hat matrix is defined whatever the rank of the Jacobian and because, in practice, the computed leverages of very ill-conditioned networks may respect their theoretical bounds while confidence intervals cannot be estimated accurately enough, two facts that have escaped Monari and Dreyfus's attention. Wealso recall the most accurate way to estimate the leverages and the properties of these estimations. Finally, we make an additional comment concerning the performance estimation in Monari and Dreyfus (2002).