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runscript.R
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runscript.R
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source('DEbased_BlendedGmatrix.R')
## excuting example file with
### naive vs AGD infection score > 3
### using the q values from the DE analysis of the Genes
### markers could be 10 kbs away from the Gene
### Blend 90% of the DE Gmatrix with 10% of the Polygenic part
#### add 0.01 to the diagonals of the matrix before inversion
DEGmat_qvalue_tau90_Kkb10 <- DEbased_BlendedG(DEresults='../AGDbolaksgeno/AGD_List.sig.genes.txt',
inpGeno='../AGDbolaksgeno/AGD_GS',
DEsample1='n',DEsample2='i2_3',
kbdist=10000,
wgtcolumn='q_value',
s_tau=0.90,
svalue_diagG=0.01,
outname='G_qvalue_tau90_Kkb10')
hist(diag(DEGmat_qvalue_tau90_Kkb10$G),breaks=25,col = sample(colours(),1),
main='',xlab=' Diagonals of G')
## excuting example file with
### naive vs AGD infection score > 3
### using the log2_FC from the DE analysis of the Genes
### markers could be 25 kbs away from the Gene
### Blend 90% of the DE Gmatrix with 10% of the Polygenic part
#### add 0.01 to the diagonals of the matrix before inversion
DEGmat_log2FC_tau90_Kkb25 <- DEbased_BlendedG(DEresults='../AGDbolaksgeno/AGD_List.sig.genes.txt',
inpGeno='../AGDbolaksgeno/AGD_GS',
DEsample1='n',DEsample2='i2_3',
kbdist=25000,
wgtcolumn='log2_FC',
s_tau=0.90,
svalue_diagG=0.01,
outname='G_log2FC_tau90_Kkb25')
hist(diag(DEGmat_log2FC_tau90_Kkb25$G),breaks=25,col = sample(colours(),1),
main='',xlab=' Diagonals of G')