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add susieR in suggestion and make changes in vignettes #81

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Merged
xueweic merged 3 commits into from
Apr 22, 2025
Merged

add susieR in suggestion and make changes in vignettes #81

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5f1abdf
adding susieR
xueweic Apr 22, 2025
9e0cb6e
minor fix
xueweic Apr 22, 2025
28b768b
Merge branch 'StatFunGen:main' into main
xueweic Apr 22, 2025
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,8 @@ Suggests:
knitr,
rmarkdown,
ashr,
MASS
MASS,
susieR
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)
Config/testthat/edition: 3
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7 changes: 7 additions & 0 deletions vignettes/Ambiguous_Colocalization.Rmd
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Expand Up @@ -105,6 +105,13 @@ susie_GWAS$sets$cs$L1
intersect(susie_eQTL$sets$cs$L1, susie_GWAS$sets$cs$L1)
```

To visulize the fine-mapping results,

```{r plot-susie}
susieR::susie_plot(susie_eQTL, y = "PIP")
susieR::susie_plot(susie_GWAS, y = "PIP")
```


# 3. Get the ambiguous colocalization results and summary

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14 changes: 3 additions & 11 deletions vignettes/LD_Free_Colocalization.Rmd
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Expand Up @@ -131,17 +131,9 @@ for (i in 1:length(X)){
x <- X[[i]]
y <- Y[[i]]
effect_n[i] <- length(y)
# simply use `rr <- susieR::univariate_regression(X = x, y = y)`
output = matrix(0,ncol(x),2)
for (i in 1:ncol(x)) {
fit = summary(lm(y ~ x[,i]))$coef
if (nrow(fit) == 2)
output[i,] = as.vector(fit[2,1:2])
else
output[i,] = c(0,0)
}
effect_est <- cbind(effect_est, output[,1])
effect_se <- cbind(effect_se, output[,2])
output <- susieR::univariate_regression(X = x, y = y)
effect_est <- cbind(effect_est, output$beta)
effect_se <- cbind(effect_se, output$sebeta)
}
colnames(effect_est) <- colnames(effect_se) <- c("Y1", "Y2", "Y3", "Y4", "Y5")
rownames(effect_est) <- rownames(effect_se) <- colnames(X[[1]])
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14 changes: 3 additions & 11 deletions vignettes/Summary_Statistics_Colocalization.Rmd
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Expand Up @@ -190,17 +190,9 @@ for (i in 1:length(X)){
x <- X[[i]]
y <- Y[[i]]
effect_n[i] <- length(y)
# simply use `rr <- susieR::univariate_regression(X = x, y = y)`
output = matrix(0,ncol(x),2)
for (i in 1:ncol(x)) {
fit = summary(lm(y ~ x[,i]))$coef
if (nrow(fit) == 2)
output[i,] = as.vector(fit[2,1:2])
else
output[i,] = c(0,0)
}
effect_est <- cbind(effect_est, output[,1])
effect_se <- cbind(effect_se, output[,2])
output <- susieR::univariate_regression(X = x, y = y)
effect_est <- cbind(effect_est, output$beta)
effect_se <- cbind(effect_se, output$sebeta)
}
colnames(effect_est) <- colnames(effect_se) <- c("Y1", "Y2", "Y3", "Y4", "Y5")
rownames(effect_est) <- rownames(effect_se) <- colnames(X[[1]])
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