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The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj <= 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.

The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj < 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.

The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj < 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.

The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj <= 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.

The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj <= 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.

The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:

1. Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
2. Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj < 0.05])

Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.