> This is correct. Limma will do "loess" normalization for you but not print-tip-loess on such data. I remedied this by "padding out" the Raw file with NA's which were systematically lacking spots in the last column of each block, this allowed me to do PT-loess in Limma which does not show the same strange behaviour as in marray when weights are used. Another issue has occured though which I have seen on several datasets now related to using zero weights. Distributionally I get a whole lot more outliers leading to M values ranging between e.g. -200, 200 an effect I cant see when using weights of say, 0.1 instead (for all negatively GenePix flagged genes). Is this to be expected or am I doing something wrong? > As far as I know, maNormMain() only handles spot weights on a > single-array basis. I assume you are aware of that already. Yes, I was aware of that since you have pointed that out on this list previously :) Thanks for the reply! /Henning Gordon Smyth wrote: > >> Date: Mon, 18 Apr 2005 12:13:03 +0200 >> From: Henning Redestig <redestig@mpimp-golm.mpg.de> >> Subject: [BioC] marray, weights and normalizations.. >> To: bioconductor@stat.math.ethz.ch >> >> Hi, >> >> I am trying to use the Lapointe et al, PNAS 2004 data set from SMD >> consisting of 112 arrays. These are not as I understand it LIMMA >> compliant since the spots in the raw files are not directly in the >> spotting order (some spots have been left out) >> and therefore I decided >> to use the marray package which seem to be capable of handling even this >> kind of formatting. >> Using read.SMD() to import the data seems to work and image() can plot >> the spots in spatial order indicating that the spotting order >> information has been kept. >> >> Problem arise when I try to normalize the data using maNormMain() as I >> wish to weight the spots based on their flags. > > > As far as I know, maNormMain() only handles spot weights on a > single-array basis. I assume you are aware of that already. > > Gordon > >> Setting w to the weights >> vector or NULL I get MA-plots as provided indicating a strong dependence >> between A and M in the lower intensity range when weights are used >> (lines are lowess fitted lines per print tip). Could anyone enlighten me >> as to why this is the case? Isnt the whole point of the normalization to >> remove any dependence between A and M? > > >> The weights vector was set to 1 for flag=0, 0.1 for flag<=-50 and 0.01 >> for flag<=-75 (GenePix flagging conventions, and weights chosen >> arbitrarily) >> >> Very thankful for help > > >