Dear Ana
As others have commented this is getting a bit off-topic but here are
some hints.
It is helpful to distinguish two sorts of plot: archival plots and
impact plots. If you want to have an impact plot which gives you a
picture but possibly at the cost of completeness and accuracy then why not:
1 - plot a sample of your 5 million drawn at random
2 - bin the data and plot median p-value against median expected
3 - deal with overlap by choosing a graphical device which supports
transparency and plot points in very light grey so the overlap is more
visible.
Michael
On 12/11/2019 22:04, Ana Marija wrote:
why I selected only those with P<0.003 to put on QQ plot is because
the original data set contains 5556249 points and when I extract only
P<0.001 I am getting 3713 points. Is there is a way to plot the whole
data set, or choose only the representative points?
On Tue, Nov 12, 2019 at 3:42 PM Ana Marija <sokovic.anamar...@gmail.com> wrote:
the smallest p value in my dataset goes to 9.89e-08. How do I make
that known on the new QQ plot with multiplied with 1000 values
On Tue, Nov 12, 2019 at 3:37 PM Ana Marija <sokovic.anamar...@gmail.com> wrote:
Just do I need to change the axis when I multiply with 1000 and what
should I put on my axis?
On Tue, Nov 12, 2019 at 3:07 PM Ana Marija <sokovic.anamar...@gmail.com> wrote:
Hi Duncan,
yes I choose for QQ plot only P<1e-3 and multiplying everything with
1000 works great!
This should not in my understanding influence the interpretation of
the plot, it is only changing the scale of axis.
Thank you so much,
Ana
On Tue, Nov 12, 2019 at 2:51 PM Duncan Murdoch <murdoch.dun...@gmail.com> wrote:
On 12/11/2019 2:56 p.m., Jim Lemon wrote:
I thought about this and did a little study of GWAS and the use of
p-values to assess significant associations. As Ana's plot begins at
values of about 0.001, this seems to imply that almost everything in
the genome is associated to some degree. One expects that most SNPs
will not be associated with a particular condition (p~1), so perhaps
something is going wrong in the calculations that produce the
p-values.
I may be misunderstanding your last sentence, but if there is no
association, the p-value would usually have a uniform distribution from
0 to 1, it wouldn't be near 1.
I'd guess we're not seeing the p values from every test, only those that
are less than 0.001. If that's true, and there are no effects, it makes
sense to multiply all of them by 1000 to get U(0,1) values. On the
plot, that would correspond to subtracting 3 from -log10(p), or adding 3
to the reference line, as Ana requested.
Or just multiply them by 1000 and pass them to qq():
qq(dd$P*1000, main = "Q-Q plot of small GWAS p-values")
As far as I can see, there's no way to tell qqman::qq to move the
reference line.
Duncan Murdoch
Jim
On Wed, Nov 13, 2019 at 12:28 AM Patrick (Malone Quantitative)
<mal...@malonequantitative.com> wrote:
I agree with Abby. That would defeat the purpose of a QQ plot.
On Mon, Nov 11, 2019, 9:54 PM Abby Spurdle <spurdl...@gmail.com> wrote:
Hi
I'm not familiar with the qqman package, or GWAS studies.
However, my guess would be that you're *not* supposed to change the
position of the line.
On Tue, Nov 12, 2019 at 11:48 AM Ana Marija <sokovic.anamar...@gmail.com>
wrote:
Hi,
I was using this library, qqman
https://cran.r-project.org/web/packages/qqman/vignettes/qqman.html
to create QQ plot, attached. How would I change this default abline to
start from the beginning of my QQ line?
This is my code:
qq(dd$P, main = "Q-Q plot of GWAS p-values")
Thanks
Ana
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--
Michael
http://www.dewey.myzen.co.uk/home.html
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