I note that "current implementations of R use 32-bit integers for integer
vectors," but I am working with large arrays that contain integers from 0
to 3, so they could be stored as unsigned 8-bit integers. Can R do this?
(FYI -- This is for storing minor-allele counts for genetic studies.
There are 0, 1 or 2 minor alleles and 3 would represent missing.)
It is theoretically possible to store such data with four integers per
byte. This is what PLINK (GPL license) does in its binary (.bed) pedigree
format:
http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#ped
That might be too much to hope for. ;-)
I think that the R system uses double-precision floating point numbers by
default. When I impute minor-allele counts, I get posterior expected
values ranging from 0 to 2 (called dosages). The imputation isn't very
precise, so it would be fine to store such data using one or two bytes.
(The values are used as regressors and small changes would have minimal
impact on results.) I could use unsigned 8-bit integers (0 to 255),
probably using only 0 to 254 so that 1 and 2 could be represented with
perfect precision as 127/127 and 254/127 (but I would do regression on the
integer values). Or I could use 16 bits, doubling memory load and
improving precision. It would be convenient if R could work with
half-precision floating-point numbers (binary16):
http://en.wikipedia.org/wiki/Half_precision_floating-point_format
Can R do that?
If not, is anyone interested in working on developing some of these
features in R? We have GPL code from PLINK and Octave that might help a
lot.
http://www.gnu.org/software/octave/doc/interpreter/Integer-Data-Types.html
Best,
Mike
--
Michael B. Miller, Ph.D.
Bioinformatics Specialist
Minnesota Center for Twin and Family Research
Department of Psychology
University of Minnesota
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