MAD, acronym of Mosaic Alteration Detector, is an R package, depending from the package R-GADA, that allows to detect mosaic events using SNP data. The best feature of this package is that it allows to run the process over a big set of samples.
To run the analysis each sample must have a file, containing the following information:
- Name (of the SNP)
- Chromosome (where the SNP is placed)
- Position (of the SNP in the chromosome)
- The value for the log2 ratio (of the SNP)
- The value of the B allele frequency (of the SNP)
- And the given genotype (of the SNP)
All this information needs to be stored in a tab-splited file:
[carleshf@coruscan analysisstarwarsrawData]$ head padme Name Chr Position Log.R.Ratio GType B.Allele.Freq S-4DTYN 3 75333236 0.320067 AB 0.503067484662577 S-4DTYJ 2 137804803 -0.372684 BB 0.93558282208589 S-4DTYD 1 79235573 -0.224208 AA 0.00920245398773001 ... ... ... ... ... ...
Moreover, the R package
MAD requires to place all the samples into a folder called
rawData, let’s see:
analysis +-- starwats +-- rawData +-- padme +-- anakin +-- qui_gon +-- palpatine +-- watto +-- startrek +-- rawData +-- james_kirk +-- spock +-- uhura +-- scott
Having the correct structure, all the samples into the folder
rawData, we place the R session into the upper folder, in the case
R> path <- getwd() R> path  "/home/carleshf/.../analysis/starwars"
Being there we can start the mosaicism analysis, that is performed in three steps:
- The set-up step.
- The segmentation procedure step.
- The backward elimination step.
library( mad ) object <- setupParGADA.B.deviation( folder = path, NumCols = 6, log2ratio = 4, BAFcol = 6, GenoCol = 5 )
parSBL( object, estim.sigma2 = TRUE, aAlpha = 0.8 )
parBE.B.deviation( object, T = 8, MinSegLen = 100 )
At this point the analysis is finished. All the possible events have been detected ans stored into the variable called
MAD allows us to export all this information as a table:
exportSegments2File( object, file = "mosaic_events.txt" )
The content of this file follows:
[carleshf@coruscan analysisstarwars]$ head mosaic_events.txt IniProbe EndProbe LenProbe qqBdev chr LRR (s.e.) Bdev %HetWT %Hom State sample 66690197 71078462 183 0.95 3 -0.64 0.21 0.188 0 8.2 2 palpatine 17309881 21421319 127 0.38 22 -0.24 0.34 0.032 4.7 51.2 2 watto 143559 15049329 495 0.89 18 -0.64 0.24 0.214 0 10.3 2 anakin
We can see that
MAD gives us a lot of information. May be, the most import could be the region where the mosaic event was detected (from
EndProbe), the chromosome containing the event (
chr), the classification of the event (
State) and the sample that suffers the event (
The number that codifies the
State of the detected abnormalities corresponds:
For more information I refer you to the following:
- The web page of the package – link
- The vignette of the package – link
- The paper where the method (package) was used – link