Dear Aroma.affymetrix teem,

I followed the vignette "Allele-specific copy numbers using non-negative 
matrix factorization". I want to extract LRR and BAF to feed into another 
software "ASCAT <http://heim.ifi.uio.no/bioinf/Projects/ASCAT/>" to correct 
aneuploidy and normal contamination. 

>From the vignette, I can use extractTotalAndFreqB to extract total CN and 
BAF. My question is how can I get LRR from Total CN. I went through this 
thread  "creat binary data files containing BAF data 
<https://groups.google.com/forum/#!searchin/aroma-affymetrix/kai/aroma-affymetrix/1Qcj_nxiQvc/ViKo4wnWcvwJ>",
 
but get confused.

quote from "creat binary data files containing BAF data 
<https://groups.google.com/forum/#%21searchin/aroma-affymetrix/kai/aroma-affymetrix/1Qcj_nxiQvc/ViKo4wnWcvwJ>
"
"The take-home message is that
the definition of "log2 ratios" has become de facto standard in our
field, where as "CN" is a bit ambiguous, and can mean "CN ratio"
(C=theta/thetaR, or C=2*theta/thetaR) as well as "CN intensity"
(theta).  By adding tags we try to make this less ambiguous."

How total CN was defined in aroma.affymetrix?


Best regards,
Hongen








my code 

############
library("aroma.affymetrix")
library("ACNE")


log <- verbose <- Arguments$getVerbose(-8, timestamp=TRUE)
# Don't display too many decimals.
options(digits=4)


#Verifying annotation data files
cdf <- AffymetrixCdfFile$byChipType("CytoScanHD_Array");
print(cdf)
gi <- getGenomeInformation(cdf)
print(gi)
si <- getSnpInformation(cdf)
print(si)
acs <- AromaCellSequenceFile$byChipType(getChipType(cdf, fullname=FALSE))
print(acs)

##raw data sets
csR<- AffymetrixCelSet$byName("smida", cdf=cdf);

###step 1 Calibration for crosstalk between allele probe pairs
acc <- AllelicCrosstalkCalibration(csR, model="CRMAv2")

print(acc)

csC <- process(acc, verbose=verbose)
print(csC)


##Step 2 - Normalization for nucleotide-position probe sequence effects
bpn <- BasePositionNormalization(csC, target="zero")
csN <- process(bpn, verbose=verbose)
cs <- csN



#step 3
#Probe summarization using non-negative-matrix factorization (NMF)

plm <- NmfSnpPlm(csN, mergeStrands=TRUE)
print(plm)

if (length(findUnitsTodo(plm)) > 0) {
   # Fit CN probes quickly (~5-10s/array + some overhead)
  units <- fitCnProbes(plm, verbose=verbose)
  str(units)
  # int [1:945826] 935590 935591 935592 935593 935594 935595 ...

  # Fit remaining units, i.e. SNPs (~5-10min/array)
  units <- fit(plm, verbose=verbose)
  str(units)
}

ces <- getChipEffectSet(plm)
print(ces)



my session information:
######################
> sessionInfo()
R version 3.1.3 (2015-03-09)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Fedora 20 (Heisenbug)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ACNE_0.8.0              aroma.light_2.2.1       aroma.affymetrix_2.13.2
 [4] aroma.core_2.13.1       R.devices_2.13.0        R.filesets_2.7.2       
 [7] R.utils_2.0.2           R.oo_1.19.0             affxparser_1.38.0      
[10] R.methodsS3_1.7.0      

loaded via a namespace (and not attached):
 [1] aroma.apd_0.6.0    base64enc_0.1-2    digest_0.6.8       
DNAcopy_1.40.0    
 [5] MASS_7.3-40        matrixStats_0.14.0 PSCBS_0.44.0       
R.cache_0.11.0    
 [9] R.huge_0.9.0       R.rsp_0.20.0       tools_3.1.3       



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