\documentclass[addpoints,11pt]{exam} \usepackage{url} \usepackage{color} \usepackage{hyperref} \pagestyle{headandfoot} \runningheadrule \firstpageheadrule \firstpageheader{Scientific Computing}{Project Assignment}{11/05/2014 -- 11/06/2014} %\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014} \firstpagefooter{}{}{{\bf Supervisor:} Fabian Sinz} \runningfooter{}{}{} \pointsinmargin \bracketedpoints %\printanswers %\shadedsolutions \begin{document} %%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%% \sffamily % \begin{flushright} % \gradetable[h][questions] % \end{flushright} \begin{center} \input{../disclaimer.tex} \end{center} %%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%% \begin{questions} \question The p-value corresponds to the probability $$P(\mbox{result seems significant}| H_0 \mbox{is true}).$$ This means that if your significance threshold is $\alpha=0.05$ and you accept all test with $p \le \alpha$ as significant, then $5\%$ of all cases in which $H_0$ was true (there was no effect) your test will appear significant (false positive). The problem with that is that you do not know for how many of the tests $H_0$ is actually true. What you really would like to know is: From all those tests that came out significant ($p\le\alpha$) how many of them are false positives? This probability corresponds to $$P(H_0 \mbox{is true}|\mbox{result seems significant})$$ and is called {\em false discovery rate}. In general you cannot compute it. However, if you have many p-values, then you can actually estimate it. The corresponding ``p-value'' for the false discovery rate is called ``q-value''. In the paper {\em Storey, J. D., \& Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences of the United States of America, 100, 9440–9445. doi:10.1073/pnas.1530509100} you can find an algorithm how to compute q-values from p-values. The attached data file {\tt p\_values.dat} contains p-values from test of several neurons whether they respond to a certain stimulus condition or not. \begin{parts} \part Plot a histogram of the p-values. \part Read and understand the paper by Storey and Tibshirani. Visualize their method at your histogram. \part Implement their method and convert each p-value to a q-value. \part From running the script, estimate the proportion of neurons that show a true effect (i.e. $P(H_A)$). \end{parts} \end{questions} \end{document}