Subset of data which is producing the same NaN error:
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
1 10901 1147 964 84 116 91 35 1234 7831 61 440 10
2 492 6062 342 9 1886 48 3822 396 1039 30 1 173
3 289 136 14 23 3833 50 2758 3559 227 3967 187 190
4 981 4 2 18 19 45 74 3754 548 407 2869 44
5 -1 773 67 48 272 1573 53 30 316 209 30 332
6 54 154 8920 78 89 422 4719 8 1082 779 683 1736
7 34 2753 91 15575 468 3856 3 10056 72 133 325 272
8 60 8 120 4589 45280 253 14 6 6 569 2324 16915
9 287 8 5 2441 14 4542 1 239 952 1074 121 37
10 12 1 1463 61 43 420 834 11 2057 12 95 -2
I have a data matrix and I want to normalize the arrays within, I have used this code:
library (affy)
loess.matrix<-normalize.loess(data.matrix,subset=1:nrow(data.matrix))
and I got this error:
Warning message: In normalize.loess(sample, subset = 1:nrow(sample)) : NaNs produced
I also got the following error with a larger dataset, but I think the root of the problem is the production of NaN values:
Error in simpleLoess(y, x, w, span, degree, parametric, drop.square, normalize, : NA/NaN/Inf in foreign function call (arg 1) In addition: Warning message: In normalize.loess(data.matrix, subset = 1:nrow(data.matrix)) : NaNs produced
Has anyone encountered this before?
The problem was that my data contained negative values and when performing lowess normalisation NaN values were being produced.
I handled the negative values by applying the logic below, this allowed me to successfully normalise my dataset.
data.matrix[data.matrix <= 0]=1e-15