Hello, I have several questions about data normalization of a large matrix of intensity data (21269,72) (non-microarray data). summary(MYdata) #### example of data NOTE many NAs ######## ID a b c Min. : 1 Min. : 2003 Min. : 2008 Min. : 2001 1st Qu.: 5318 1st Qu.: 4027 1st Qu.: 4155 1st Qu.: 4331 Median :10635 Median : 7635 Median : 7570 Median : 8006 Mean :10635 Mean : 57586 Mean : 73246 Mean : 101309 3rd Qu.:15952 3rd Qu.: 17191 3rd Qu.: 18076 3rd Qu.: 18843 Max. :21269 Max. :20335320 Max. :30073282 Max. :27649912 NA's : 18323 NA's : 18467 NA's : 18471 ########################################################## What would be the best way to normalize or preprocess this type of data (80%+ missing)? A log2 transformation creates nice "similar" shaped distributions with different medians. Currently I do this to normalize divide column data by (column median/min column meidan) divide row data by (row median/min row meidan) Repeat 1 more time *the method I am using turns the distribution into a spike shape.* Is the above method acceptable for for future statistical applications? Are there better normalization methods I can use? NA question I would like to remove all of the rows with less than 30% missing values before continuing with normalization, but I cannot figure out how. Is there a way to remove all rows that have say more than 30% missing data? If i could just count the number of NAs in a row and divide it by the number of columns i would be in good shape, but I cant figure out how to do this. NA.OMIT is too harsh and remove most of the rows. Thanks much, Alan