Hello all, I'm trying to use the composite method on limma_1.7.2 but I get an error when I normalizeWithinArrays: ---------------------------------------------------------------------- Error in predLoess(object$y, object$x, newx, object$s, object$weights, : NA/NaN/Inf in foreign function call (arg 3) ---------------------------------------------------------------------- I have NA values in my data. The "loess" function can handle NA's as it is done in normalizeWithinArrays: fit <- loess(y ~ x, weights = w, span = span, subset = controlspots, na.action = na.exclude, degree = 0, surface = "direct", family = "symmetric", trace.hat = "approximate", iterations = iterations) but I can't see the same to the "predict" function. I overrided that modifing normalizeWithinArrays as: global <- predict(fit, newdata = x) ---------------------------------------------- global <- predict(fit, newdata = x[!is.na(x)]) ?Is this a correct way to do that or did I miss something? Thanks in advance, D.

> Hello all, > > I'm trying to use the composite method on limma_1.7.2 but I get an error > when I normalizeWithinArrays: You're right. This is a bug in limma that "composite" normalization doesn't tolerate missing values (amongst the the control spots). I'll fix it for limma 1.7.4 (not yet available). > ---------------------------------------------------------------------- > Error in predLoess(object$y, object$x, newx, object$s, object$weights, : > NA/NaN/Inf in foreign function call (arg 3) > ---------------------------------------------------------------------- > > > I have NA values in my data. The "loess" function can handle NA's as it > is done in normalizeWithinArrays: > > fit <- loess(y ~ x, weights = w, span = span, subset = controlspots, > na.action = na.exclude, degree = 0, surface = "direct", > family = "symmetric", trace.hat = "approximate", > iterations = iterations) > > but I can't see the same to the "predict" function. I overrided that > modifing normalizeWithinArrays as: > > global <- predict(fit, newdata = x) > ---------------------------------------------- > global <- predict(fit, newdata = x[!is.na(x)]) > > > ?Is this a correct way to do that or did I miss something? This is correct but you need a bit more as well. By the way, rather than using "composite" normalization I think it is usually more flexible and effective to simply up-weight the control spots in the ordinary normalization process. See modifyWeights(). Gordon > Thanks in advance, > > D.