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authorPeter Carbonetto2017-05-09 12:48:27 -0500
committerPeter Carbonetto2017-05-09 12:48:27 -0500
commitf808355cd7abbadd2fbcc5ff471ba89a12205159 (patch)
treeb5b9bef67de6b04d70a4c8b67b2e464b9aee343f /example
parent0f6431e9191acfafc33152a610b87d3d0524c1fe (diff)
downloadpangemma-f808355cd7abbadd2fbcc5ff471ba89a12205159.tar.gz
Putatively fixed -Wc++11-narrowing error in io.cpp (see Issue #24).
Diffstat (limited to 'example')
-rw-r--r--example/demo.txt17
1 files changed, 8 insertions, 9 deletions
diff --git a/example/demo.txt b/example/demo.txt
index 766d949..3231ddc 100644
--- a/example/demo.txt
+++ b/example/demo.txt
@@ -1,13 +1,10 @@
-## Detailed description of the data set is available in the online GEMMA user manual
-## Each of the following steps may take over one minute to run
-
-
-
-
-
+## Detailed description of the data set is available in the online
+## GEMMA user manual. Each of the following steps may take over one
+## minute to run.
## To calculate a centered relatedness matrix:
-../bin/gemma -g mouse_hs1940.geno.txt.gz -p mouse_hs1940.pheno.txt -a mouse_hs1940.anno.txt -gk -o mouse_hs1940
+../bin/gemma -g mouse_hs1940.geno.txt.gz -p mouse_hs1940.pheno.txt \
+ -a mouse_hs1940.anno.txt -gk -o mouse_hs1940
# The estimated relatedness matrix should look like this:
0.3350590 -0.0227226 0.0103535 ...
@@ -55,7 +52,9 @@ se(pve) in the null model = 0.032774
## To perform association tests with a multivariate linear mixed model, for two phenotypes CD8 (column 1) and MCH (column 6):
## Notice that the number of individuals in this analysis is different from that above, so the allele frequencies are different between the two analyses
-../bin/gemma -g mouse_hs1940.geno.txt.gz -p mouse_hs1940.pheno.txt -n 1 6 -a mouse_hs1940.anno.txt -k ./output/mouse_hs1940.cXX.txt -lmm -o mouse_hs1940_CD8MCH_lmm
+../bin/gemma -g mouse_hs1940.geno.txt.gz -p mouse_hs1940.pheno.txt \
+ -n 1 6 -a mouse_hs1940.anno.txt -k ./output/mouse_hs1940.cXX.txt \
+ -lmm -o mouse_hs1940_CD8MCH_lmm
# The result for top 5 SNPs should look like this:
chr rs ps n_miss allele1 allele0 af beta_1 beta_2 Vbeta_1_1 Vbeta_1_2 Vbeta_2_2 p_wald