Body: <p>I was wondering if there is something I am doing wrong when trying to
  calculate True positive rate, TPR with your function
  this is what I am doing:</p>
  
  <pre><code>&gt; head(qq)
      chr   pos    gene_id       pval_nominal pval_ret  META
  1: chr1 54490 ENSG00000227232     0.608495 0.783778 0.7733204
  2: chr1 58814 ENSG00000227232     0.295211 0.897582 0.6970567
  3: chr1 60351 ENSG00000227232     0.439788 0.867959 0.7525581
  4: chr1 61920 ENSG00000227232     0.319528 0.601809 0.4407018
  5: chr1 63671 ENSG00000227232     0.237739 0.988039 0.8626555
  6: chr1 64931 ENSG00000227232     0.276679 0.907037 0.6971364
  
  library(qvalue)
  pvals=qq<span class="math-container">$META
  qval_obj=qvalue(pvals) #is false discovery rate
  pi1=1-qval_obj$</span>pi0 #TPR
  &gt; pi1
  [1] 0.08827036
  </code></pre>
  
  <p>It is very unlikely that TPR would be this small, can you please advise?</p>
  
  <p>I do have 59981 META p values in this data frame.</p>
  
  <p>IS here pi1 representing TPR value?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-29T20:03:32.860Z
Comments.Text: TPR = TP / (TP + FN), where TP = true positive, FN = false negative. You don't know whether a gene is a true positive or false negative. Do not confuse the terms. pi0 is the probability that the result comes from a null hypothesis. For differential gene expression, In simple terms, it means the proportion of genes that are not differentially expressed.
Comments.UserDisplayName: StupidWolf
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-29T20:06:40.687Z
Comments.Text: You can briefly check out pi0 here, where the qvalue package is based on (http://genomics.princeton.edu/storeylab/papers/directfdr.pdf). When doing FDR, if you use Benjamini-Hochberg, pi0 is assumed to be 1. The "qvalue" method estimates pi0 from the data and uses it estimate FDR.
Comments.UserDisplayName: StupidWolf
Tags: <r>
Title: Calculating True Positive Rate with qvalue function
ThreadText: # Calculating True Positive Rate with qvalue function
  
  (Asked by: username_0 on 2019-10-29)
  
  I was wondering if there is something I am doing wrong when trying to
  calculate True positive rate, TPR with your function
  this is what I am doing:
  
  ```
  > head(qq)
      chr   pos    gene_id       pval_nominal pval_ret  META
  1: chr1 54490 ENSG00000227232     0.608495 0.783778 0.7733204
  2: chr1 58814 ENSG00000227232     0.295211 0.897582 0.6970567
  3: chr1 60351 ENSG00000227232     0.439788 0.867959 0.7525581
  4: chr1 61920 ENSG00000227232     0.319528 0.601809 0.4407018
  5: chr1 63671 ENSG00000227232     0.237739 0.988039 0.8626555
  6: chr1 64931 ENSG00000227232     0.276679 0.907037 0.6971364
  
  library(qvalue)
  pvals=qq$META
  qval_obj=qvalue(pvals) #is false discovery rate
  pi1=1-qval_obj$pi0 #TPR
  > pi1
  [1] 0.08827036
  
  ```
  
  It is very unlikely that TPR would be this small, can you please advise?
  
  I do have 59981 META p values in this data frame.
  
  IS here pi1 representing TPR value?
  
  **Tags:** r
  
  > **Comments:**
  > **username_1**: TPR = TP / (TP + FN), where TP = true positive, FN = false negative. You don't know whether a gene is a true positive or false negative. Do not confuse the terms. pi0 is the probability that the result comes from a null hypothesis. For differential gene expression, In simple terms, it means the proportion of genes that are not differentially expressed.  
  > **username_1**: You can briefly check out pi0 here, where the qvalue package is based on (http://genomics.princeton.edu/storeylab/papers/directfdr.pdf). When doing FDR, if you use Benjamini-Hochberg, pi0 is assumed to be 1. The "qvalue" method estimates pi0 from the data and uses it estimate FDR.  
  
  ---
  
  ## 0 answers
  
  