[BioC] Single channel (numerical intensities) data import into BioConductor

[Kort, Eric]

2008/5/8 Dozmorov, Mikhail G. (HSC) <Mikhail-Dozmorov at ouhsc.edu>:

> We receive microarray data from processing facility in a simple format o
> background subtracted intensities, here's an example, tab delimited:
>
> Gene ID C1860gu C1775gu C1777gu
> AA278251        100.825641      82.30144928     222.4144928
> AA401404        383.6702703     374.1666667     342.3405797
> AA454191        100.7403727     175.2884199     135.4404762
> AA460836        343.9164875     366.684058      351.8411658
> AA723761        445.059587      451.999359      355.5185897
> AA902654        400.1301282     431.4055556     367.1327381
> AA905415        200.9855072     208.0207576     218.5183983
>
> Thus, for each array (C2860gu,...) we have corresponding gene expression
> data. How to import this data into ESET object for further handling?

?ExpressionSet is helpful.  It will lead you in the following direction (try to ensure your file--here named data.txt--is free of extraneous white space since this may cause read.delim to interpret numerical data as character):

library(Biobase)
exp <- new("ExpressionSet", exprs=as.matrix(read.delim("c:/data.txt", row.names=1)))

# that's all that is required to accomplish what you wanted, but here I will create
# some fictitious and random pheno data to illustrate adding pheno data:
pheno <- matrix(runif(12, 1, 10), nrow=3)

# note that nrow(pheno) == ncol(exprs(eset)), as it must.
rownames(pheno) <- colnames(exprs(exp));  colnames(pheno) <- c("WBC", "HgB", "AFP", "Age")
pheno <- new("AnnotatedDataFrame", data=as.data.frame(pheno))
exp <- new("ExpressionSet", exprs=as.matrix(read.delim("c:/data.txt", row.names=1)), phenoData=pheno)

# let's check our work
pData(exp)
             WBC      HgB      AFP      Age
C1860gu 6.934886 7.284751 2.676714 8.815719
C1775gu 8.744655 7.067929 4.415327 7.904734
C1777gu 2.271367 4.686928 3.211371 6.035077

exprs(exp)
          C1860gu   C1775gu  C1777gu
AA278251 100.8256  82.30145 222.4145
AA401404 383.6703 374.16667 342.3406
AA454191 100.7404 175.28842 135.4405
AA460836 343.9165 366.68406 351.8412
AA723761 445.0596 451.99936 355.5186
AA902654 400.1301 431.40556 367.1327
AA905415 200.9855 208.02076 218.5184

Cheers,
Eric

> Thank you!
>
>
>






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