# identify significant SNPs coef <- coef(fit) nz_idx <- which(coef != 0) nz_snps <- nz_idx - 1 df <- data.frame(lambda = rep(lam, length(coef)), coef = coef, SNP = row.names(coef)) # Create bar plot ggplot(df, aes(x = SNP, y = coef, fill = abs(coef) > 0.1)) + geom_col() + scale_fill_manual(values = c("lightgray", "red")) + geom_hline(yintercept = 0, linetype = "dashed", color = "black") + geom_vline(xintercept = which(df$lambda == lam), linetype = "dashed", color = "black") + coord_flip() + labs(x = "", y = "Coefficient", title = "Marginal Model") + theme_bw() + theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), legend.position = "none")
时间: 2024-03-28 14:40:55 浏览: 14
这段代码是用来绘制SNP回归系数的条形图。首先,用`coef()`函数获取回归系数,用`which()`函数获取系数不为0的SNP的索引,再减去1得到SNP的编号。然后,创建一个数据框`df`,其中包括lambda、coef和SNP三列,分别表示岭回归的惩罚参数、回归系数和SNP编号。接下来,使用`ggplot()`函数创建条形图,其中`x`表示SNP,`y`表示回归系数,`fill`表示系数的绝对值是否大于0.1。使用`scale_fill_manual()`函数设置填充颜色,使用`geom_hline()`函数添加水平虚线表示回归系数为0的位置,使用`geom_vline()`函数添加垂直虚线表示当前的惩罚参数lambda。最后,使用`coord_flip()`函数将x轴和y轴交换,使用`labs()`函数添加标题和标签,使用`theme_bw()`函数设置白色背景,使用`theme()`函数修改图形元素的样式。
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# identify significant SNPs coef <- coef(fit) nz_idx <- which(coef != 0) nz_snps <- nz_idx - 1 # visualize results par(mar = c(5, 4, 4, 8) + 0.1) plot(coef, xvar = "lambda", label = TRUE, main = "Marginal Model") abline(v = lam, lty = 2) significant_snps <- which(abs(coef) > 0.1) points(z, col = "red", pch = 19, cex = 1.2)
这段代码的作用是在 R 语言中进行 Lasso 回归,然后可视化结果。具体来说,它完成以下几个任务:
1. 使用 `coef()` 函数获取 Lasso 回归的系数向量 `coef`。
2. 使用 `which()` 函数找到非零系数对应的 SNP 索引,存储在向量 `nz_idx` 中。
3. 由于 SNP 索引是从 0 开始的,需要将 `nz_idx` 中的值减去 1,得到真正的 SNP 索引,存储在向量 `nz_snps` 中。
4. 使用 `par()` 函数设置绘图区域的边距。
5. 使用 `plot()` 函数绘制 Lasso 回归系数随惩罚力度参数 $\lambda$ 的变化情况,其中 `xvar = "lambda"` 表示将 $\lambda$ 作为 x 轴变量,`label = TRUE` 表示在图中标记非零系数对应的 SNP。
6. 使用 `abline()` 函数在图中绘制一条竖直于 x 轴的虚线,表示选择的最优惩罚力度参数 $\lambda$。
7. 使用 `which()` 函数找到绝对值大于 0.1 的系数对应的 SNP,存储在向量 `significant_snps` 中。
8. 使用 `points()` 函数在图中标记出 `significant_snps` 中的 SNP,颜色为红色,符号为实心圆点,大小为 1.2。
需要注意的是,上述代码的可视化结果使用基础绘图函数 `plot()` 和 `points()`,而不是 `ggplot2`。如果你想使用 `ggplot2` 画出类似的图形,可以参考前面的示例代码。
1 Simulate a dataset containing continuous traits with given ℎ 2 . Split it into training and testing datasets with proportion 4 ∶ 1. set.seed(20132014) n <- 5000 p <- 1000 h <- c(0.2, 0.8)[1] # simulate genotype (not exactly) x_r <- matrix(rnorm(n * p), ncol = p) xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow = TRUE) xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow = TRUE) x <- (x_r - xmean)/xsd # ... 2 Please perform the marginal model. ## [1] 526 776 50 557 801 345 298 559 178 214 1 2 −0.025 0.000 0.025 0.050 0 250 500 750 1000 snp u • Show the locations of significant SNPs. • Replicate above figure, and highlight the significant coefficents in red
For the first question, I understand that you want me to simulate a dataset with continuous traits and split it into training and testing datasets with a given heritability (h2) and some specific code. Here is the modified code to simulate the dataset and split it into training and testing datasets:
```R
set.seed(20132014)
n <- 5000
p <- 1000
h2 <- 0.2
# simulate genotype (not exactly)
x_r <- matrix(rnorm(n * p), ncol = p)
xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow = TRUE)
xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow = TRUE)
x <- (x_r - xmean)/xsd
# simulate phenotype
g <- colSums(x) * sqrt(2 * h2 / p) # genetic effect
e <- rnorm(n) * sqrt(1 - h2) # environmental effect
y <- g + e # phenotype
# split into training and testing datasets
train_idx <- sample(1:n, size = n * 0.8, replace = FALSE)
x_train <- x[train_idx, ]
y_train <- y[train_idx]
x_test <- x[-train_idx, ]
y_test <- y[-train_idx]
```
This code simulates a dataset with n = 5000 individuals and p = 1000 SNPs. The heritability is set to 0.2. The code first simulates the genotype matrix x, and then uses it to simulate the phenotype y. The genetic effect is proportional to the sum of each SNP's effect, and the environmental effect is simulated from a normal distribution. Finally, the code splits the dataset into training (80%) and testing (20%) datasets.
For the second question, I understand that you want me to perform a marginal model and then show the locations of significant SNPs and replicate a figure highlighting significant coefficients in red. Here is the code to perform the marginal model and visualize the results:
```R
library(glmnet)
set.seed(1234)
# fit marginal model
cvfit <- cv.glmnet(x_train, y_train, alpha = 0)
lam <- cvfit$lambda.min
fit <- glmnet(x_train, y_train, alpha = 0, lambda = lam)
# identify significant SNPs
coef <- coef(fit)
nz_idx <- which(coef != 0)
nz_snps <- nz_idx - 1
# visualize results
par(mar = c(5, 4, 4, 8) + 0.1)
plot(coef, xvar = "lambda", label = TRUE, main = "Marginal Model")
abline(v = lam, lty = 2)
significant_snps <- which(abs(coef) > 0.1)
points(coef[significant_snps], col = "red", pch = 19, cex = 1.2)
```
This code fits a marginal model using the glmnet package and identifies significant SNPs based on a threshold of 0.1. The code then visualizes the results using the plot function and highlights the significant SNPs in red. The abline function adds a vertical line at the optimal lambda value selected by cross-validation.