Paired CBS
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Michelle
Nov 11, 2011, 4:02:01 PM11/11/11
to aroma.affymetrix
Dear aroma community,
We are trying to do paired CBS analysis on some Affy SNP 6.0 data
using the R package "PSCBS", but not sure how to extract the total CN
and BAF values from the raw array files (.CEL) into the needed format
(pasted below). It seems aroma.affymetrix has some relevant functions
but there are few examples to follow. Could someone provide some
sample code on this? Thanks!
chromosome position CT betaT CN
betaN
SNP_A-8575125 1 554484 1.8778996 0.06460427 1.8856720
0.1161352
SNP_A-8575115 1 554636 0.1659543 0.64621653 0.2282602
0.6919347
SNP_A-8575371 1 557616 2.1193934 0.82567971 2.3405546
0.8425635
SNP_A-8709646 1 711153 1.3974123 0.25975011 2.2465997
0.1765471
CT: total tumor copy number
betaT: tumor allele B fractions (BAFs)
CN: total matched normal copy number
betaN: matched normal BAFs
We are trying to do paired CBS analysis on some Affy SNP 6.0 data
using the R package "PSCBS", but not sure how to extract the total CN
and BAF values from the raw array files (.CEL) into the needed format
(pasted below). It seems aroma.affymetrix has some relevant functions
but there are few examples to follow. Could someone provide some
sample code on this? Thanks!
chromosome position CT betaT CN
betaN
SNP_A-8575125 1 554484 1.8778996 0.06460427 1.8856720
0.1161352
SNP_A-8575115 1 554636 0.1659543 0.64621653 0.2282602
0.6919347
SNP_A-8575371 1 557616 2.1193934 0.82567971 2.3405546
0.8425635
SNP_A-8709646 1 711153 1.3974123 0.25975011 2.2465997
0.1765471
CT: total tumor copy number
betaT: tumor allele B fractions (BAFs)
CN: total matched normal copy number
betaN: matched normal BAFs
Henrik Bengtsson
Nov 11, 2011, 7:52:19 PM11/11/11
to aroma-af...@googlegroups.com
Hi Michelle,
I've just added vignette 'Paired parent-specific copy-number
segmentation (Paired PSCBS) (low-level API after AS-CRMAv2)':
http://aroma-project.org/vignettes/PairedPSCBS-lowlevel
See if that helps
/Henrik
> --
> When reporting problems on aroma.affymetrix, make sure 1) to run the latest version of the package, 2) to report the output of sessionInfo() and traceback(), and 3) to post a complete code example.
>
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Michelle
Nov 15, 2011, 1:23:38 PM11/15/11
to aroma-af...@googlegroups.com
Hi Henrik,
I tested the updated code and got the error message "Exception: Could not locate a file for this chip type: Mapping250K_Nsp" -- a similar message showed up at some point previously when I was trying to process SNP 6.0 data, saying "could not locate a file for this chip type: GenomeWideSNP_6". I checked the annotation files and they are good. And I think my directory structure is correct too:
working directory: C:\Data\GSE12702_RAW
annotation: C:\Data\GSE12702_RAW\annotationData\chipTypes\Mapping250K_Nsp
raw data: C:\Data\GSE12702_RAW\rawData\GSE12702\Mapping250K_Nsp
Thanks!
-Michelle
>library("aroma.affymetrix");
>cwd="C:/Data/GSE12702_RAW/"
>setwd(cwd)
>dataSet <- "GSE12702";
>chipType <- "Mapping250K_Nsp";
># Load all samples (in case more samples were downloaded)
>csR <- AffymetrixCelSet$byName(dataSet, chipType=chipType);
># Extract tumor-normal pair of interest
>pair <- c(T="GSM318736", N="GSM318737");
>csR <- extract(csR, indexOf(csR, pair));
>print(csR);
AffymetrixCelSet:
Name: GSE12702
Tags:
Path: rawData/GSE12702/Mapping250K_Nsp
Platform: Affymetrix
Chip type: Mapping250K_Nsp
Number of arrays: 2
Names: GSM318736, GSM318737 [2]
Time period: 2008-04-04 14:07:29 -- 2008-04-04 14:39:34
Total file size: 125.31MB
RAM: 0.00MB
> res <- doASCRMAv2(csR, verbose=verbose);
CRMAv2...
Arguments:
combineAlleles: FALSE
arrays:
chr ""
ram:
Data set
AffymetrixCelSet:
Name: GSE12702
Tags:
Path: rawData/GSE12702/Mapping250K_Nsp
Platform: Affymetrix
Chip type: Mapping250K_Nsp
Number of arrays: 2
Names: GSM318736, GSM318737 [2]
Time period: 2008-04-04 14:07:29 -- 2008-04-04 14:39:34
Total file size: 125.31MB
RAM: 0.00MB
CRMAv2/Allelic crosstalk calibration...
AllelicCrosstalkCalibration:
Data set: GSE12702
Input tags:
User tags: *
Asterisk ('*') tags: ACC,-XY
Output tags: ACC,-XY
Number of files: 2 (125.31MB)
Platform: Affymetrix
Chip type: Mapping250K_Nsp
Algorithm parameters: (rescaleBy: chr "groups", targetAvg: num [1:2] 2200 2200, subsetToAvg: chr "-XY", mergeShifts: logi TRUE, B: int 1, flavor: chr "sfit", algorithmParameters:List of 3, ..$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001, ..$ q: num 2, ..$ Q: num 98)
Output path: probeData/GSE12702,ACC,-XY/Mapping250K_Nsp
Is done: FALSE
RAM: 0.00MB
Calibrating data set for allelic cross talk...
Compressing model parameter to a short format...
Compressing model parameter to a short format...done
Calibrating 2 arrays...
Path: probeData/GSE12702,ACC,-XY/Mapping250K_Nsp
Array #1 ('GSM318736') of 2...
Error in list(`doASCRMAv2(csR, verbose = verbose)` = <environment>, `doASCRMAv2.default(csR, verbose = verbose)` = <environment>, :
[2011-11-15 13:02:41] Exception: Could not locate a file for this chip type: Mapping250K_Nsp
at throw(Exception(...))
at throw.default("Could not locate a file for this chip type: ", paste(c(chipType, tags), collapse = ","))
at throw("Could not locate a file for this chip type: ", paste(c(chipType, tags), collapse = ","))
at method(static, ...)
at AromaCellSequenceFile$byChipType(chipType, nbrOfCells = nbrOfCells)
at getSetsOfProbes.AllelicCrosstalkCalibration(this, verbose = less(verbose, 1))
at getSetsOfProbes(this, verbose = less(verbose, 1))
at process.AllelicCrosstalkCalibration(acc, verbose = verbose)
at process(acc, verbose = verbose)
at doCRMAv2.AffymetrixCelSet(..., combineAlleles = FALSE)
at doCRMAv2(..., combineAlleles = FALSE)
at doASCRMAv2.default(csR, verbose = verbose)
at doASCRMAv2(csR, verbose = verbose)
Array #1 ('GSM318736') of 2...done
Calibrating 2 arrays...done
Calibrating data set for allelic cross talk...done
Henrik Bengtsson
Nov 15, 2011, 1:30:00 PM11/15/11
to aroma-af...@googlegroups.com
Hi,
the Aroma Cell Sequence (ACS) annotation file is missing. You can
download it from here:
http://aroma-project.org/chipTypes/Mapping250K_Nsp-and-Mapping250K_Sty
/Henrik
Michelle
Nov 15, 2011, 4:23:03 PM11/15/11
to aroma-af...@googlegroups.com
Hi Henrik,
I downloaded the acs annotation file and now the code works for this 250K dataset. However, for my SNP 6.0 data, I still get this error message "Exception: Could not locate a file for this chip type: GenomeWideSNP_6"
Under annotation directory I have these files:
GenomeWideSNP_6,Full,monocell.CDF
GenomeWideSNP_6,Full.cdf
GenomeWideSNP_6,HB20080710.acs
GenomeWideSNP_6,Full,na26,HB20080722.ufl
GenomeWideSNP_6,Full,na26,HB20080821.ugp
GenomeWideSNP_6,na31,hg19,HB20110328.ufl
GenomeWideSNP_6,Full,na31,hg19,HB20110328.ugp
GenomeWideSNP_6,Full,na31,hg19,HB20110328.ufl
and the output is
> library("aroma.affymetrix");
>
> cwd="C:/Data/TCGA/_aroma_/"
> setwd(cwd)
>
> dataSet <- "TCGABreast";
> chipType <- "GenomeWideSNP_6";
>
> csR <- AffymetrixCelSet$byName(dataSet, chipType=chipType);
Error in list(`AffymetrixCelSet$byName(dataSet, chipType = chipType)` = <environment>, :
[2011-11-15 16:16:21] Exception: Could not locate a file for this chip type: GenomeWideSNP_6
at throw(Exception(...))
at throw.default("Could not locate a file for this chip type: ", paste(c(chipType, tags), collapse = ","))
at throw("Could not locate a file for this chip type: ", paste(c(chipType, tags), collapse = ","))
at method(static, ...)
at AffymetrixCdfFile$byChipType(chipType)
at method(static, ...)
at AffymetrixCelSet$byName(dataSet, chipType = chipType)
Henrik Bengtsson
Nov 15, 2011, 4:47:08 PM11/15/11
to aroma-af...@googlegroups.com
Hi.
On Tue, Nov 15, 2011 at 1:23 PM, Michelle <suy...@gmail.com> wrote:
> Hi Henrik,
> I downloaded the acs annotation file and now the code works for this 250K
> dataset. However, for my SNP 6.0 data, I still get this error message
> "Exception: Could not locate a file for this chip type: GenomeWideSNP_6"
> Under annotation directory I have these files:
> GenomeWideSNP_6,Full,monocell.CDF
> GenomeWideSNP_6,Full.cdf
> GenomeWideSNP_6,HB20080710.acs
> GenomeWideSNP_6,Full,na26,HB20080722.ufl
> GenomeWideSNP_6,Full,na26,HB20080821.ugp
> GenomeWideSNP_6,na31,hg19,HB20110328.ufl
> GenomeWideSNP_6,Full,na31,hg19,HB20110328.ugp
> GenomeWideSNP_6,Full,na31,hg19,HB20110328.ufl
The GenomeWideSNP_6 (and the GenomeWideSNP_5) chip is special, because
Affymetrix provides two different *annotation* sets (CDFs), one
"default" and one "full".
To use the "Full" annotation, you need the following annotation data files:
GenomeWideSNP_6,HB20080710.acs (same for "default" and "full")
GenomeWideSNP_6,Full.cdf
GenomeWideSNP_6,Full,na31,hg19,HB20110328.ugp
GenomeWideSNP_6,Full,na31,hg19,HB20110328.ufl
and then do:
dataSet <- "TCGABreast";
chipType <- "GenomeWideSNP_6,Full";
csR <- AffymetrixCelSet$byName(dataSet, chipType=chipType);
...
/Henrik
>
> and the output is
>> library("aroma.affymetrix");
>>
>> cwd="C:/Data/TCGA/_aroma_/"
>> setwd(cwd)
>>
>> dataSet <- "TCGABreast";
>> chipType <- "GenomeWideSNP_6";
>>
>> csR <- AffymetrixCelSet$byName(dataSet, chipType=chipType);
> Error in list(`AffymetrixCelSet$byName(dataSet, chipType = chipType)` =
> <environment>, :
>
> [2011-11-15 16:16:21] Exception: Could not locate a file for this chip type:
> GenomeWideSNP_6
> at throw(Exception(...))
> at throw.default("Could not locate a file for this chip type: ",
> paste(c(chipType, tags), collapse = ","))
> at throw("Could not locate a file for this chip type: ", paste(c(chipType,
> tags), collapse = ","))
> at method(static, ...)
> at AffymetrixCdfFile$byChipType(chipType)
> at method(static, ...)
> at AffymetrixCelSet$byName(dataSet, chipType = chipType)
>
Michelle
Nov 18, 2011, 4:16:10 PM11/18/11
to aroma-af...@googlegroups.com
Hi Henrik,
Now I have successfully run PSCBS analysis on some SNP 6.0 arrays using your sample code. However, I noticed that with some paired tumor/normal arrays, the "doASCRMAv2" step produces an error; I have pasted the entire output below, the error occurs in the last 10 lines:
res <- doASCRMAv2(csR, verbose=verbose);
CRMAv2...
Arguments:
combineAlleles: FALSE
arrays:
chr ""
ram:
Data set
AffymetrixCelSet:
Name: TCGABreast
Tags:
Path: rawData/TCGABreast/GenomeWideSNP_6
Platform: Affymetrix
Chip type: GenomeWideSNP_6,Full
Number of arrays: 2
Names: HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430, HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454 [2]
Time period: 2010-08-24 17:35:47 -- 2010-08-24 23:42:47
Total file size: 131.73MB
RAM: 0.00MB
CRMAv2/Allelic crosstalk calibration...
AllelicCrosstalkCalibration:
Data set: TCGABreast
Input tags:
User tags: *
Asterisk ('*') tags: ACC,ra,-XY
Output tags: ACC,ra,-XY
Number of files: 2 (131.73MB)
Platform: Affymetrix
Chip type: GenomeWideSNP_6,Full
Algorithm parameters: (rescaleBy: chr "all", targetAvg: num 2200, subsetToAvg: chr "-XY", mergeShifts: logi TRUE, B: int 1, flavor: chr "sfit", algorithmParameters:List of 3, ..$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001, ..$ q: num 2, ..$ Q: num 98)
Output path: probeData/TCGABreast,ACC,ra,-XY/GenomeWideSNP_6
Is done: FALSE
RAM: 0.00MB
Calibrating data set for allelic cross talk...
Compressing model parameter to a short format...
Compressing model parameter to a short format...done
Calibrating 2 arrays...
Path: probeData/TCGABreast,ACC,ra,-XY/GenomeWideSNP_6
Array #1 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430') of 2...
setsOfProbes:
List of 2
$ snps :List of 7
..$ A/C : int [1:237717, 1:2] 2266716 6113640 3757084 5957782 3713366 938870 3009140 4020622 5198986 2839656 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "C"
..$ A/G : int [1:951450, 1:2] 1274800 3849278 5806862 2029956 2132192 5971262 5219456 1059250 2055252 6206844 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "G"
..$ A/T : int [1:185438, 1:2] 555222 492748 4994022 1329092 6692926 5150182 5756788 5310522 2906000 2928044 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "T"
..$ C/G : int [1:267860, 1:2] 6881634 896238 1628636 4498862 1176896 2319948 4682414 6833666 2348290 1324730 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "C" "G"
..$ C/T : int [1:1052269, 1:2] 5273360 2514104 585930 2795750 2556530 4709726 4231336 1431338 1393532 6819040 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "C" "T"
..$ G/T : int [1:221871, 1:2] 2707540 368952 4666772 817620 2127848 4918332 4459040 1090876 1165094 1420088 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "G" "T"
..$ missing: int [1:20376, 1:2] 4936927 6020943 646043 2191151 5673771 3920197 4958087 5638071 2734479 2024837 ...
$ nonSNPs: int [1:964281] 3466656 3466655 3463975 3463976 3461296 3461295 3458616 3458615 3455936 3455935 ...
- attr(*, "version")= num 4
Reading all probe intensities...
Reading all probe intensities...done
Fitting calibration model...
Allele probe-pair group #1 ('A/C') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 237717
Fitting...done
Allele probe-pair group #1 ('A/C') of 7...done
Allele probe-pair group #2 ('A/G') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 951450
Fitting...done
Allele probe-pair group #2 ('A/G') of 7...done
Allele probe-pair group #3 ('A/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 185438
Fitting...done
Allele probe-pair group #3 ('A/T') of 7...done
Allele probe-pair group #4 ('C/G') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 267860
Fitting...done
Allele probe-pair group #4 ('C/G') of 7...done
Allele probe-pair group #5 ('C/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 1052269
Fitting...done
Allele probe-pair group #5 ('C/T') of 7...done
Allele probe-pair group #6 ('G/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 221871
Fitting...done
Allele probe-pair group #6 ('G/T') of 7...done
Allele probe-pair group #7 ('missing') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 20376
Fitting...done
Allele probe-pair group #7 ('missing') of 7...done
Fitting calibration model...done
Estimated origins:
A/C A/G A/T C/G C/T G/T missing
A 178.843 188.494 185.662 202.701 208.244 201.682 170.253
B 196.242 193.147 181.165 177.946 177.616 180.129 174.375
Weighted average offset: 190.68
Backtransforming (calibrating) data...
Allele basepair #1 ('A/C') of 7...
Allele basepair #1 ('A/C') of 7...done
Allele basepair #2 ('A/G') of 7...
Allele basepair #2 ('A/G') of 7...done
Allele basepair #3 ('A/T') of 7...
Allele basepair #3 ('A/T') of 7...done
Allele basepair #4 ('C/G') of 7...
Allele basepair #4 ('C/G') of 7...done
Allele basepair #5 ('C/T') of 7...
Allele basepair #5 ('C/T') of 7...done
Allele basepair #6 ('G/T') of 7...
Allele basepair #6 ('G/T') of 7...done
Allele basepair #7 ('missing') of 7...
Allele basepair #7 ('missing') of 7...done
Backtransforming (calibrating) data...done
Correcting offset for all non-SNP cells...
Cells:
int [1:964281] 3466656 3466655 3463975 3463976 3461296 3461295 3458616 3458615 3455936 3455935 ...
Offset: 190.676008099814
Correcting offset for all non-SNP cells...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496582 13.3 1733286 46.3 13143139 351.0
Vcells 21545222 164.4 47541391 362.8 191940319 1464.4
List of 3
$ paramsShort:List of 6
..$ rescaleBy : chr "all"
..$ targetAvg : num 2200
..$ mergeShifts : logi TRUE
..$ B : int 1
..$ flavor : chr "sfit"
..$ algorithmParameters:List of 3
.. ..$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. ..$ q : num 2
.. ..$ Q : num 98
$ accFits :List of 8
..$ A/C :List of 7
.. ..$ M : cfit [1:3, 1:2] 179 6381 403 196 241 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.20e+03 -6.88e-15 0.00 3.07e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 179 196
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.0362 0.00715 0.98174
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.00026 -0.03688 -0.00728 1.01887
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 237717 2
..$ A/G :List of 7
.. ..$ M : cfit [1:3, 1:2] 188 7365 249 193 406 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 7.18e+03 2.57e-16 0.00 9.67e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 188 193
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.00849 0.02967 0.83968
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.0003 -0.0101 -0.0353 1.1913
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 951450 2
..$ A/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 186 6303 287 181 376 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.12e+03 -1.69e-14 0.00 2.64e-14 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 186 181
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.0166 0.0319 0.9494
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.0006 -0.0175 -0.0336 1.0539
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 185438 2
..$ C/G :List of 7
.. ..$ M : cfit [1:3, 1:2] 203 6447 232 178 333 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.24e+03 -3.47e-18 0.00 4.88e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 203 178
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.00464 0.02477 1.01831
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.00011 -0.00455 -0.02433 0.98213
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 267860 2
..$ C/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 208 6486 242 178 376 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.28e+03 1.82e-15 0.00 -2.26e-14 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 208 178
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.00543 0.03158 1.15611
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.0001 -0.0047 -0.0273 0.8651
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 1052269 2
..$ G/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 202 6092 411 180 265 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 5.89e+03 -2.15e-14 0.00 -9.69e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 202 180
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.0355 0.0144 1.2002
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.0004 -0.0296 -0.012 0.8335
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 221871 2
..$ missing:List of 7
.. ..$ M : cfit [1:3, 1:2] 170 5361 287 174 221 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. .. used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496228 13.3 1733286 46.3 13143139 351.0
Vcells 21544700 164.4 47541391 362.8 191940319 1464.4
Storing calibrated data...
Creating CEL file for results, if missing...
Creating CEL file for results, if missing...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496206 13.3 1733286 46.3 13143139 351.0
Vcells 14651723 111.8 51403329 392.2 191940319 1464.4
Storing calibrated data...done
Array #1 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430') of 2...done
Array #2 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454') of 2...
setsOfProbes:
List of 2
$ snps :List of 7
..$ A/C : int [1:237717, 1:2] 2266716 6113640 3757084 5957782 3713366 938870 3009140 4020622 5198986 2839656 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "C"
..$ A/G : int [1:951450, 1:2] 1274800 3849278 5806862 2029956 2132192 5971262 5219456 1059250 2055252 6206844 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "G"
..$ A/T : int [1:185438, 1:2] 555222 492748 4994022 1329092 6692926 5150182 5756788 5310522 2906000 2928044 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "A" "T"
..$ C/G : int [1:267860, 1:2] 6881634 896238 1628636 4498862 1176896 2319948 4682414 6833666 2348290 1324730 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "C" "G"
..$ C/T : int [1:1052269, 1:2] 5273360 2514104 585930 2795750 2556530 4709726 4231336 1431338 1393532 6819040 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "C" "T"
..$ G/T : int [1:221871, 1:2] 2707540 368952 4666772 817620 2127848 4918332 4459040 1090876 1165094 1420088 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:2] "G" "T"
..$ missing: int [1:20376, 1:2] 4936927 6020943 646043 2191151 5673771 3920197 4958087 5638071 2734479 2024837 ...
$ nonSNPs: int [1:964281] 3466656 3466655 3463975 3463976 3461296 3461295 3458616 3458615 3455936 3455935 ...
- attr(*, "version")= num 4
Reading all probe intensities...
Reading all probe intensities...done
Fitting calibration model...
Allele probe-pair group #1 ('A/C') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 237717
Fitting...done
Allele probe-pair group #1 ('A/C') of 7...done
Allele probe-pair group #2 ('A/G') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 951450
Fitting...done
Allele probe-pair group #2 ('A/G') of 7...done
Allele probe-pair group #3 ('A/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 185438
Fitting...done
Allele probe-pair group #3 ('A/T') of 7...done
Allele probe-pair group #4 ('C/G') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 267860
Fitting...done
Allele probe-pair group #4 ('C/G') of 7...done
Allele probe-pair group #5 ('C/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 1052269
Fitting...done
Allele probe-pair group #5 ('C/T') of 7...done
Allele probe-pair group #6 ('G/T') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 221871
Fitting...done
Allele probe-pair group #6 ('G/T') of 7...done
Allele probe-pair group #7 ('missing') of 7...
Fitting...
Model/algorithm flavor: sfit
Model parameters:
List of 3
$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
$ q : num 2
$ Q : num 98
Number of data points: 20376
Fitting...done
Allele probe-pair group #7 ('missing') of 7...done
Fitting calibration model...done
Estimated origins:
A/C A/G A/T C/G C/T G/T missing
A 175.037 187.463 184.073 208.378 213.897 227.028 165.556
B 195.759 196.142 175.449 174.859 171.283 177.845 163.816
Weighted average offset: 191.40
Backtransforming (calibrating) data...
Allele basepair #1 ('A/C') of 7...
Allele basepair #1 ('A/C') of 7...done
Allele basepair #2 ('A/G') of 7...
Allele basepair #2 ('A/G') of 7...done
Allele basepair #3 ('A/T') of 7...
Allele basepair #3 ('A/T') of 7...done
Allele basepair #4 ('C/G') of 7...
Allele basepair #4 ('C/G') of 7...done
Allele basepair #5 ('C/T') of 7...
Allele basepair #5 ('C/T') of 7...done
Allele basepair #6 ('G/T') of 7...
Allele basepair #6 ('G/T') of 7...done
Allele basepair #7 ('missing') of 7...
Allele basepair #7 ('missing') of 7...done
Backtransforming (calibrating) data...done
Correcting offset for all non-SNP cells...
Cells:
int [1:964281] 3466656 3466655 3463975 3463976 3461296 3461295 3458616 3458615 3455936 3455935 ...
Offset: 191.404409513204
Correcting offset for all non-SNP cells...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496589 13.3 1733286 46.3 13143139 351.0
Vcells 21545247 164.4 51403329 392.2 191940319 1464.4
List of 3
$ paramsShort:List of 6
..$ rescaleBy : chr "all"
..$ targetAvg : num 2200
..$ mergeShifts : logi TRUE
..$ B : int 1
..$ flavor : chr "sfit"
..$ algorithmParameters:List of 3
.. ..$ alpha: num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. ..$ q : num 2
.. ..$ Q : num 98
$ accFits :List of 8
..$ A/C :List of 7
.. ..$ M : cfit [1:3, 1:2] 175 6674 396 196 202 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.50e+03 1.59e-14 0.00 4.18e-16 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 175 196
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.03399 0.00089 0.99517
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.00003 -0.034154 -0.000895 1.004879
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 237717 2
..$ A/G :List of 7
.. ..$ M : cfit [1:3, 1:2] 187 7551 221 196 360 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 7.36e+03 -2.56e-15 0.00 2.01e-14 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 187 196
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.00453 0.02226 0.84237
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.00012 -0.00537 -0.02643 1.18727
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 951450 2
..$ A/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 184 6723 269 175 377 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.54e+03 -5.50e-15 0.00 3.97e-14 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 184 175
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.013 0.0308 0.9453
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.0004 -0.0138 -0.0326 1.0583
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 185438 2
..$ C/G :List of 7
.. ..$ M : cfit [1:3, 1:2] 208 6728 181 175 308 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.52e+03 7.11e-15 0.00 -6.47e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 208 175
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 -0.00416 0.02035 1.02009
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 0.99992 0.00408 -0.01995 0.98022
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 267860 2
..$ C/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 214 6748 190 171 365 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.53e+03 -9.56e-16 0.00 -1.62e-14 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 214 171
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 -0.00366 0.02957 1.14403
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 0.9999 0.0032 -0.0258 0.874
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 1052269 2
..$ G/T :List of 7
.. ..$ M : cfit [1:3, 1:2] 227 6339 304 178 232 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Minv : cfit [1:3, 1:2] 0.00 6.11e+03 8.11e-15 0.00 5.22e-15 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ origin : Named num [1:2] 227 178
.. .. ..- attr(*, "names")= chr [1:2] "A" "B"
.. ..$ W : num [1:2, 1:2] 1 0.01252 0.00884 1.22327
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. .. .. ..$ : chr [1:2] "A" "B"
.. ..$ Winv : num [1:2, 1:2] 1.00009 -0.01023 -0.00723 0.81756
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:2] "A" "B"
.. .. .. ..$ : chr [1:2] "AA" "BB"
.. ..$ params :List of 4
.. .. ..$ alpha : num [1:8] 0.1 0.075 0.05 0.03 0.01 0.0025 0.001 0.0001
.. .. ..$ q : num 2
.. .. ..$ Q : num 98
.. .. ..$ verbose: logi FALSE
.. ..$ dimData: int [1:2] 221871 2
..$ missing:List of 7
.. ..$ M : cfit [1:3, 1:2] 166 5655 267 164 251 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "origin" "AA" "BB"
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496232 13.3 1733286 46.3 13143139 351.0
Vcells 21544708 164.4 51403329 392.2 191940319 1464.4
Storing calibrated data...
Creating CEL file for results, if missing...
Creating CEL file for results, if missing...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496210 13.3 1733286 46.3 13143139 351.0
Vcells 14651731 111.8 47806381 364.8 191940319 1464.4
Storing calibrated data...done
Array #2 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454') of 2...done
Calibrating 2 arrays...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 496426 13.3 1733286 46.3 13143139 351.0
Vcells 14652182 111.8 47806381 364.8 191940319 1464.4
Calibrating data set for allelic cross talk...done
AffymetrixCelSet:
Name: TCGABreast
Tags: ACC,ra,-XY
Path: probeData/TCGABreast,ACC,ra,-XY/GenomeWideSNP_6
Platform: Affymetrix
Chip type: GenomeWideSNP_6,Full
Number of arrays: 2
Names: HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430, HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454 [2]
Time period: 2011-11-18 15:37:49 -- 2011-11-18 15:40:48
Total file size: 131.47MB
RAM: 0.00MB
CRMAv2/Allelic crosstalk calibration...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505275 13.5 1733286 46.3 13143139 351.0
Vcells 14178678 108.2 38245104 291.8 191940319 1464.4
CRMAv2/Base position normalization...
BasePositionNormalization:
Data set: TCGABreast
Input tags: ACC,ra,-XY
User tags: *
Asterisk ('*') tags: BPN,-XY
Output tags: ACC,ra,-XY,BPN,-XY
Number of files: 2 (131.47MB)
Platform: Affymetrix
Chip type: GenomeWideSNP_6,Full
Algorithm parameters: (unitsToFit: chr "-XY", typesToFit: chr "pm", unitsToUpdate: NULL, typesToUpdate: chr "pm", shift: num 0, target: chr "zero", model: chr "smooth.spline", df: int 5)
Output path: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6
Is done: FALSE
RAM: 0.00MB
Normalization data set for probe-sequence effects...
Normalizing 2 arrays...
Path: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6...
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505079 13.5 1733286 46.3 13143139 351.0
Vcells 29374377 224.2 42329226 323.0 191940319 1464.4
Array #1 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430') of 2...
Compressing model parameter to a short format...
Compressing model parameter to a short format...done
Fitting model for current array...
$params
$params$intercept
[1] 9.430088
$params$A
[1] -0.10328626 -0.05102370 -0.06579606 -0.06949456 0.04795146
$params$C
[1] 0.124844586 0.265978480 0.045258828 0.347072487 -0.007290026
$params$G
[1] 0.15575555 0.30435641 -0.09017155 0.45946950 -0.07280913
$map
<NA> A C G T
00 01 02 03 04
$B
1 2 3 4 5
[1,] 0.000000e+00 0.000000e+00 0.00000000 0.00000000 0.000000000
[2,] 1.507041e-03 0.000000e+00 -0.05205793 0.15617380 -0.104115863
[3,] 1.205633e-02 0.000000e+00 -0.09953061 0.29859182 -0.199061216
[4,] 4.069010e-02 0.000000e+00 -0.13783277 0.41349832 -0.275665548
[5,] 9.645062e-02 0.000000e+00 -0.16237917 0.48713752 -0.324758348
[6,] 1.883319e-01 1.205633e-05 -0.16859984 0.50579951 -0.337199674
[7,] 3.151042e-01 2.604167e-03 -0.15516504 0.46549512 -0.310330078
[8,] 4.527271e-01 1.604697e-02 -0.12797448 0.38392343 -0.255948950
[9,] 5.740741e-01 4.938272e-02 -0.09390603 0.28171808 -0.187812057
[10,] 6.520182e-01 1.116536e-01 -0.05983758 0.17951274 -0.119675163
[11,] 6.600116e-01 2.115162e-01 -0.03245964 0.09766826 -0.065112176
[12,] 5.940032e-01 3.426288e-01 -0.01117830 0.04594086 -0.030627239
[13,] 4.791667e-01 4.791667e-01 0.01406302 0.02031093 -0.013540619
[14,] 3.426288e-01 5.940032e-01 0.05395333 0.01583821 -0.010558809
[15,] 2.115162e-01 6.600116e-01 0.11918162 0.02758246 -0.018388305
[16,] 1.116536e-01 6.520182e-01 0.21909877 0.05071150 -0.033482143
[17,] 4.938272e-02 5.740741e-01 0.34303351 0.08201058 -0.048500882
[18,] 1.604697e-02 4.527271e-01 0.46490145 0.11951007 -0.053185626
[19,] 2.604167e-03 3.151042e-01 0.55822173 0.16127232 -0.037202381
[20,] 1.205633e-05 1.883319e-01 0.59651348 0.20535973 0.009782848
[21,] 0.000000e+00 9.645062e-02 0.55709877 0.25000000 0.096450617
[22,] 0.000000e+00 4.069010e-02 0.44540551 0.29464286 0.219261533
[23,] 0.000000e+00 1.205633e-02 0.27945877 0.33928571 0.369199184
[24,] 0.000000e+00 1.507041e-03 0.07734306 0.38392857 0.537221327
[25,] 0.000000e+00 0.000000e+00 -0.14285714 0.42857143 0.714285714
attr(,"degree")
[1] 3
attr(,"knots")
20% 40% 60% 80%
5.8 10.6 15.4 20.2
attr(,"Boundary.knots")
[1] 1 25
attr(,"intercept")
[1] FALSE
attr(,"class")
[1] "ns" "basis" "matrix"
$algorithm
[1] "solve"
attr(,"class")
[1] "ProbePositionEffects"
Fitting model for current array...done
Saving model fit...
List of 2
$ paramsShort:List of 8
..$ unitsToFit : int [1:1784732] 1 2 3 4 5 6 7 8 9 10 ...
..$ typesToFit : chr "pm"
..$ unitsToUpdate: int [1:1881415] 1 2 3 4 5 6 7 8 9 10 ...
..$ typesToUpdate: chr "pm"
..$ shift : num 0
..$ target : chr "zero"
..$ model : chr "smooth.spline"
..$ df : int 5
$ fit :List of 4
..$ params :List of 4
.. ..$ intercept: num 9.43
.. ..$ A : num [1:5] -0.1033 -0.051 -0.0658 -0.0695 0.048
.. ..$ C : num [1:5] 0.12484 0.26598 0.04526 0.34707 -0.00729
.. ..$ G : num [1:5] 0.1558 0.3044 -0.0902 0.4595 -0.0728
..$ map : Named raw [1:5] 00 01 02 03 ...
.. ..- attr(*, "names")= chr [1:5] NA "A" "C" "G" ...
..$ B : ns [1:25, 1:5] 0 0.00151 0.01206 0.04069 0.09645 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:5] "1" "2" "3" "4" ...
.. ..- attr(*, "degree")= num 3
.. ..- attr(*, "knots")= Named num [1:4] 5.8 10.6 15.4 20.2
.. .. ..- attr(*, "names")= chr [1:4] "20%" "40%" "60%" "80%"
.. ..- attr(*, "Boundary.knots")= int [1:2] 1 25
.. ..- attr(*, "intercept")= logi FALSE
.. ..- attr(*, "class")= chr [1:3] "ns" "basis" "matrix"
..$ algorithm: chr "solve"
..- attr(*, "class")= chr "ProbePositionEffects"
Saving model fit...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505505 13.5 1733286 46.3 13143139 351.0
Vcells 31208078 238.1 92845299 708.4 191940319 1464.4
Reading probe signals...
num [1:6835685] 1250 1348 787 2649 2591 ...
Min. 1st Qu. Median Mean 3rd Qu. Max.
-2663 955 2215 2831 3962 165600
Reading probe signals...done
Predicting mean log2 probe signals...
Reading probe sequences...
Cells:
int [1:6835685] 3466655 3463975 3461295 3458615 3455935 3453255 3450575 3447895 3445215 3442535 ...
Reading probe sequences...done
mu:
num [1:6835685] 0 0 0 0 0 0 0 0 0 0 ...
Min. 1st Qu. Median Mean 3rd Qu. Max.
-2.2020 -0.7528 -0.4313 -0.4258 -0.1021 2.0480
Predicting mean log2 probe signals...done
Discrepancy scale factors towards target...
Target: zero
Min. 1st Qu. Median Mean 3rd Qu. Max.
-2.0480 0.1021 0.4313 0.4258 0.7528 2.2020
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2417 1.0730 1.3480 1.4150 1.6850 4.6000
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505463 13.5 1733286 46.3 13143139 351.0
Vcells 69658627 531.5 133169071 1016.0 191940319 1464.4
Discrepancy scale factors towards target...done
Normalizing probe signals...
num [1:6835685] 1250 1348 787 2649 2591 ...
Min. 1st Qu. Median Mean 3rd Qu. Max.
-4229 1248 2862 3748 5161 288100
Normalizing probe signals...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505470 13.5 1733286 46.3 13143139 351.0
Vcells 62822941 479.4 133169071 1016.0 191940319 1464.4
Storing normalized data...
Adding temporary suffix from file...
Pathname: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6/HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430.CEL
Suffix: .tmp
Rename existing file?: FALSE
Temporary pathname: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6/HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430.CEL.tmp
Adding temporary suffix from file...done
Creating CEL file for results, if missing...
Creating CEL file for results, if missing...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505463 13.5 1733286 46.3 13143139 351.0
Vcells 52569434 401.1 133169071 1016.0 191940319 1464.4
Dropping temporary suffix from file...
Temporary pathname: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6/HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430.CEL.tmp
Suffix: .tmp
Regular expression for suffix: \.tmp$
Pathname: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6/HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430.CEL
Renaming existing file...
Result: TRUE
Renaming existing file...done
Dropping temporary suffix from file...done
Storing normalized data...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505707 13.6 1733286 46.3 13143139 351.0
Vcells 52569931 401.1 133169071 1016.0 191940319 1464.4
Array #1 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C01_628430') of 2...done
Array #2 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454') of 2...
Fitting model for current array...
$params
$params$intercept
[1] 9.483034
$params$A
[1] -0.10636566 -0.05082790 -0.06357315 -0.06277421 0.03406283
$params$C
[1] 0.10643845 0.26189310 0.06693642 0.33682777 0.05742607
$params$G
[1] 0.12831346 0.33500882 -0.09829836 0.38589204 -0.03075466
$map
<NA> A C G T
00 01 02 03 04
$B
1 2 3 4 5
[1,] 0.000000e+00 0.000000e+00 0.00000000 0.00000000 0.000000000
[2,] 1.507041e-03 0.000000e+00 -0.05205793 0.15617380 -0.104115863
[3,] 1.205633e-02 0.000000e+00 -0.09953061 0.29859182 -0.199061216
[4,] 4.069010e-02 0.000000e+00 -0.13783277 0.41349832 -0.275665548
[5,] 9.645062e-02 0.000000e+00 -0.16237917 0.48713752 -0.324758348
[6,] 1.883319e-01 1.205633e-05 -0.16859984 0.50579951 -0.337199674
[7,] 3.151042e-01 2.604167e-03 -0.15516504 0.46549512 -0.310330078
[8,] 4.527271e-01 1.604697e-02 -0.12797448 0.38392343 -0.255948950
[9,] 5.740741e-01 4.938272e-02 -0.09390603 0.28171808 -0.187812057
[10,] 6.520182e-01 1.116536e-01 -0.05983758 0.17951274 -0.119675163
[11,] 6.600116e-01 2.115162e-01 -0.03245964 0.09766826 -0.065112176
[12,] 5.940032e-01 3.426288e-01 -0.01117830 0.04594086 -0.030627239
[13,] 4.791667e-01 4.791667e-01 0.01406302 0.02031093 -0.013540619
[14,] 3.426288e-01 5.940032e-01 0.05395333 0.01583821 -0.010558809
[15,] 2.115162e-01 6.600116e-01 0.11918162 0.02758246 -0.018388305
[16,] 1.116536e-01 6.520182e-01 0.21909877 0.05071150 -0.033482143
[17,] 4.938272e-02 5.740741e-01 0.34303351 0.08201058 -0.048500882
[18,] 1.604697e-02 4.527271e-01 0.46490145 0.11951007 -0.053185626
[19,] 2.604167e-03 3.151042e-01 0.55822173 0.16127232 -0.037202381
[20,] 1.205633e-05 1.883319e-01 0.59651348 0.20535973 0.009782848
[21,] 0.000000e+00 9.645062e-02 0.55709877 0.25000000 0.096450617
[22,] 0.000000e+00 4.069010e-02 0.44540551 0.29464286 0.219261533
[23,] 0.000000e+00 1.205633e-02 0.27945877 0.33928571 0.369199184
[24,] 0.000000e+00 1.507041e-03 0.07734306 0.38392857 0.537221327
[25,] 0.000000e+00 0.000000e+00 -0.14285714 0.42857143 0.714285714
attr(,"degree")
[1] 3
attr(,"knots")
20% 40% 60% 80%
5.8 10.6 15.4 20.2
attr(,"Boundary.knots")
[1] 1 25
attr(,"intercept")
[1] FALSE
attr(,"class")
[1] "ns" "basis" "matrix"
$algorithm
[1] "solve"
attr(,"class")
[1] "ProbePositionEffects"
Fitting model for current array...done
Saving model fit...
List of 2
$ paramsShort:List of 8
..$ unitsToFit : int [1:1784732] 1 2 3 4 5 6 7 8 9 10 ...
..$ typesToFit : chr "pm"
..$ unitsToUpdate: int [1:1881415] 1 2 3 4 5 6 7 8 9 10 ...
..$ typesToUpdate: chr "pm"
..$ shift : num 0
..$ target : chr "zero"
..$ model : chr "smooth.spline"
..$ df : int 5
$ fit :List of 4
..$ params :List of 4
.. ..$ intercept: num 9.48
.. ..$ A : num [1:5] -0.1064 -0.0508 -0.0636 -0.0628 0.0341
.. ..$ C : num [1:5] 0.1064 0.2619 0.0669 0.3368 0.0574
.. ..$ G : num [1:5] 0.1283 0.335 -0.0983 0.3859 -0.0308
..$ map : Named raw [1:5] 00 01 02 03 ...
.. ..- attr(*, "names")= chr [1:5] NA "A" "C" "G" ...
..$ B : ns [1:25, 1:5] 0 0.00151 0.01206 0.04069 0.09645 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:5] "1" "2" "3" "4" ...
.. ..- attr(*, "degree")= num 3
.. ..- attr(*, "knots")= Named num [1:4] 5.8 10.6 15.4 20.2
.. .. ..- attr(*, "names")= chr [1:4] "20%" "40%" "60%" "80%"
.. ..- attr(*, "Boundary.knots")= int [1:2] 1 25
.. ..- attr(*, "intercept")= logi FALSE
.. ..- attr(*, "class")= chr [1:3] "ns" "basis" "matrix"
..$ algorithm: chr "solve"
..- attr(*, "class")= chr "ProbePositionEffects"
Saving model fit...done
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 505518 13.5 1733286 46.3 13143139 351.0
Vcells 52569617 401.1 133169071 1016.0 191940319 1464.4
Reading probe signals...
Unable to read file: probeData/TCGABreast,ACC,ra,-XY/GenomeWideSNP_6/HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454.CEL
Error in attr(cel, "row.names") <- seq_len(length(cel[[1]])) :
attempt to set an attribute on NULL
In addition: Warning messages:
1: In log2(y) : NaNs produced
2: In log2(y) : NaNs produced
Reading probe signals...done
Array #2 ('HAULS_p_TCGAb47_SNP_2N_GenomeWideSNP_6_C10_628454') of 2...done
Path: probeData/TCGABreast,ACC,ra,-XY,BPN,-XY/GenomeWideSNP_6...done
Normalizing 2 arrays...done
Normalization data set for probe-sequence effects...done
Thanks,
Michelle
Michelle
Feb 17, 2012, 4:44:59 PM2/17/12
to aroma-af...@googlegroups.com
Hi Henrik,
I ran the PSCBS package on some Affy SNP 6.0 arrays. In the output I found that several files contain a few lines of negative "tcnMean" values, while the rest are all positive. My question is - the total copy number is supposed to be positive, right? Are these negative values caused by some error in the computation? If so, how to adjust them?
Thanks,
Michelle
Henrik Bengtsson
Feb 17, 2012, 6:04:18 PM2/17/12
to aroma-af...@googlegroups.com
Hi,
yes, *true*/*biological* CNs cannot be negative. However,
*observed*/*estimated* ones may very well end up being *slightly*
negative, due to noise in data. So, it is neither a bug nor something
wrong with PSCBS.
There is nothing in Paired PSCBS that enforces the CNs to be strictly
non-negative (>= 0), neither does it restrict the CN estimates to be
integers (0, 1, 2, 3, ...), because there may be normal cell
contamination or tumor clonality. However, you can imagine that there
are downstream methods that takes the PSCBS estimates and make such
decisions, but that is not part of PSCBS itself.
You can always manual adjust negative estimates to be zero, if you
really need them not to be negative, but I still haven't had to do it.
/Henrik
>
> Thanks,
> Michelle
Michelle
May 7, 2012, 5:48:31 PM5/7/12
to aroma-af...@googlegroups.com
Hi Henrik,
On this page http://aroma-project.org/vignettes/PairedPSCBS-lowlevel you showed how to make whole genome plots of TCN, the decrease-of-heterozygosity (DH), and the minor-major CN estimates. Is there a way to make such plots for individual chromosomes?
Thanks,
Michelle
Pierre Neuvial
May 8, 2012, 3:32:19 AM5/8/12
to aroma-af...@googlegroups.com
Hi Michelle,
Yes: just replace
plotTracks(fit);
by
plotTracks(fit, chromosome=2);
Best,
Pierre
Yes: just replace
plotTracks(fit);
by
plotTracks(fit, chromosome=2);
Best,
Pierre
Michelle
May 8, 2012, 6:02:24 PM5/8/12
to aroma-af...@googlegroups.com
Hi Pierre,
Instead, if I plot on the screen and save the image manually, the saved plot is much clearer.
Thanks!
I find that if I use R commands to save the plot, such as this,
"PairedPSCBS"), width=840, aspectRatio=0.6, { |
plotTracks(fit); |
}); |
The saved plot has poor quality, with the text all blurry.
Instead, if I plot on the screen and save the image manually, the saved plot is much clearer.
I wonder if this is a known problem and if there is any solution to it?
Thanks,
Michelle
Message has been deleted
Michelle
Jun 19, 2012, 5:29:09 PM6/19/12
to aroma-af...@googlegroups.com
Hi Henrik,
I noticed that the tcnMeans from PSCBS are centered around 2, while the rawCNs extracted using the CBS model are centered around 1.
What is the difference between tcnMean and rawCN, and, is there a column in PSCBS output that is equivalent to rawCN?
Thanks,
Michelle
On Fri, Feb 17, 2012 at 6:04 PM, Henrik Bengtsson <h...@biostat.ucsf.edu> wrote:
Henrik Bengtsson
Jun 19, 2012, 5:50:30 PM6/19/12
to aroma-af...@googlegroups.com
> I noticed that the tcnMeans from PSCBS are centered around 2, while the
> rawCNs extracted using the CBS model are centered around 1.
> What is the difference between tcnMean and rawCN, and, is there a column in
> PSCBS output that is equivalent to rawCN?
The difference is just a scale factor of exactly 2 (two), which
> rawCNs extracted using the CBS model are centered around 1.
> What is the difference between tcnMean and rawCN, and, is there a column in
> PSCBS output that is equivalent to rawCN?
corresponds to the assumption that the genome is (mainly) diploid.
It's just a matter of (default) conventions. PSCBS calculates total
CNs as C=2*T/N. The CbsModel (assuming you refer to that) is working
with M = log2(T/N), which on the non-log scale is 2^M = T/N.
/H
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