Output from rgEigPCA.py run at 19/05/2010 15:14:48
newfilepath=/opt/galaxy/test-data/rgtestouts/rgEigPCA, rexe=R(click on the image below to see a much higher quality PDF version)
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All Files:
- Rplots.pdf (813 B)
- rgEigPCAtest1.R (1.6 KB)
- rgEigPCAtest1.html
- rgEigPCAtest1.txt (3.3 KB)
- rgEigPCAtest1_PCAPlot.pdf (7.9 KB)
- rgEigPCAtest1_PCAPlot.pdf.png (27.1 KB)
- rgEigPCAtest1_eigensoftplot.pdf.pdf (2.1 KB)
- rgEigPCAtest1_eigensoftplot.pdf.ps (13.6 KB)
- rgEigPCAtest1_eigensoftplot.pdf.xtxt (257 B)
- rgEigPCAtest1_eval.xls (507 B)
- rgEigPCAtest1_log.txt (6.0 KB)
- rgEigPCAtest1_pca.xls (1.3 KB)
- rgEigPCAtest1_pca.xls.evec (3.3 KB)
- rgEigPCAtest1_pca.xls.par (311 B)
Log rgEigPCAtest1_log.txt contents follow below
If you need to rerun this analysis, the command line used was
smartpca.perl -i /opt/galaxy/test-data/tinywga.bed -a /opt/galaxy/test-data/tinywga.bim -b /opt/galaxy/test-data/tinywga.fam -o rgEigPCAtest1_pca.xls -p rgEigPCAtest1_eigensoftplot.pdf -e rgEigPCAtest1_eval.xls -l rgEigPCAtest1_log.txt -k 4 -m 2 -t 2 -s 2
parameter file: rgEigPCAtest1_pca.xls.par
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: /opt/galaxy/test-data/tinywga.bed
snpname: /opt/galaxy/test-data/tinywga.bim
indivname: /opt/galaxy/test-data/tinywga.fam
evecoutname: rgEigPCAtest1_pca.xls.evec
evaloutname: rgEigPCAtest1_eval.xls
altnormstyle: NO
numoutevec: 4
numoutlieriter: 2
numoutlierevec: 2
outliersigmathresh: 2
qtmode: 0
## smartpca version: 8000
norm used
genetic distance set from physical distance
genotype file processed
number of samples used: 40 number of snps used: 25
REMOVED outlier 1334:2 iter 1 evec 1 sigmage -2.329
number of samples after outlier removal: 39
## Tracy-Widom statistics: rows: 39 cols: 24
#N eigenvalue difference twstat p-value effect. n
1 11.481974 NA -0.712 0.332198 8.854
2 7.739771 -3.742203 -1.302 0.510496 8.388
3 7.254586 -0.485185 -0.171 0.201072 7.095
4 4.270950 -2.983636 -0.191 0.205302 8.005
5 2.538320 -1.732630 -0.341 0.238117 9.118
6 1.667964 -0.870356 -0.231 0.21376 9.910
7 1.043403 -0.624560 -0.269 0.222042 11.590
8 0.653401 -0.390003 NA NA NA
9 0.391044 -0.262357 NA NA NA
10 0.338164 -0.052880 NA NA NA
11 0.263384 -0.074780 NA NA NA
12 0.150750 -0.112634 NA NA NA
13 0.085580 -0.065170 NA NA NA
14 0.065301 -0.020279 NA NA NA
15 0.048797 -0.016504 NA NA NA
16 0.006611 -0.042186 NA NA NA
kurtosis snps indivs
eigenvector 1 1.926 2.122
eigenvector 2 2.599 2.148
eigenvector 3 2.593 2.873
eigenvector 4 3.165 2.831
population: 0 Case 9
population: 1 Control 30
## Average divergence between populations:
Case Control popsize
Case 1.028 0.954 9
Control 0.954 0.972 30
number of blocks for moving block jackknife: 1
fst *1000:
C C
C 0 0
C 0 0
s.dev * 1000000:
C C
C 0 0
C 0 0
## Anova statistics for population differences along each eigenvector:
p-value
eigenvector_1_Case_Control_ 0.841067
eigenvector_2_Case_Control_ 0.758376
eigenvector_3_Case_Control_ 0.238793
eigenvector_4_Case_Control_ 0.678458
## Statistical significance of differences beween populations:
pop1 pop2 chisq p-value |pop1| |pop2|
popdifference: Case Control 1.810 0.77061 9 30
eigbestsnp 1 rs762601 22 21898858 1.674
eigbestsnp 1 rs2156921 22 21899063 1.674
eigbestsnp 1 rs4822375 22 21905642 1.674
eigbestsnp 1 rs5751611 22 21896019 1.635
eigbestsnp 1 rs4820537 22 21794810 1.542
eigbestsnp 1 rs3788347 22 21797804 1.413
eigbestsnp 1 rs2267000 22 21785366 1.151
eigbestsnp 1 rs4820539 22 21807970 1.141
eigbestsnp 1 rs5751592 22 21827674 0.890
eigbestsnp 1 rs2283804 22 21820335 0.815
eigbestsnp 1 rs2267006 22 21820990 0.815
eigbestsnp 2 rs5759608 22 21832708 2.197
eigbestsnp 2 rs5759612 22 21833170 2.197
eigbestsnp 2 rs2283804 22 21820335 1.643
eigbestsnp 2 rs2267006 22 21820990 1.643
eigbestsnp 2 rs2283802 22 21784722 1.283
eigbestsnp 2 rs2267009 22 21860168 1.283
eigbestsnp 2 rs2071436 22 21871488 1.283
eigbestsnp 2 rs756632 22 21799918 0.942
eigbestsnp 2 rs16997606 22 21794754 0.715
eigbestsnp 2 rs6003566 22 21889806 0.645
eigbestsnp 2 rs762601 22 21898858 0.545
eigbestsnp 3 rs16997606 22 21794754 1.770
eigbestsnp 3 rs6003566 22 21889806 1.725
eigbestsnp 3 rs3788347 22 21797804 1.452
eigbestsnp 3 rs2267000 22 21785366 1.386
eigbestsnp 3 rs2283802 22 21784722 1.247
eigbestsnp 3 rs2267009 22 21860168 1.247
eigbestsnp 3 rs2071436 22 21871488 1.247
eigbestsnp 3 rs4820537 22 21794810 1.168
eigbestsnp 3 rs762601 22 21898858 1.153
eigbestsnp 3 rs2156921 22 21899063 1.153
eigbestsnp 3 rs4822375 22 21905642 1.153
eigbestsnp 4 rs756632 22 21799918 2.146
eigbestsnp 4 rs4820539 22 21807970 1.938
eigbestsnp 4 rs2267013 22 21875879 1.917
eigbestsnp 4 rs2256725 22 21892891 1.917
eigbestsnp 4 rs2283804 22 21820335 1.336
eigbestsnp 4 rs2267006 22 21820990 1.336
eigbestsnp 4 rs6003566 22 21889806 1.000
eigbestsnp 4 rs16997606 22 21794754 0.875
eigbestsnp 4 rs5751611 22 21896019 0.823
eigbestsnp 4 rs5759636 22 21868698 0.799
eigbestsnp 4 rs2267000 22 21785366 0.584
packedancestrymap output
##end of smartpca run
Correlation between eigenvector 1 (of 4) and Case/Control status is 0.033
Correlation between eigenvector 2 (of 4) and Case/Control status is 0.051
Correlation between eigenvector 3 (of 4) and Case/Control status is 0.193
Correlation between eigenvector 4 (of 4) and Case/Control status is -0.069