Dataset Information

Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.

ABSTRACT: Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from non-changing genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.

SUBMITTER: Norris AW

PROVIDER: S-EPMC1334678 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature

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Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.

Norris Andrew W AW Kahn C Ronald CR

Proceedings of the National Academy of Sciences of the United States of America 20060109 3

Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly ...[more]

PMID: 16407153

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Project description:All large-scale LC-MS/MS post-translational methylation site discovery experiments require methylpeptide spectrum matches (methyl-PSMs) to be identified at acceptably low false discovery rates (FDRs). To meet estimated methyl-PSM FDRs, methyl-PSM filtering criteria are often determined using the target-decoy approach. The efficacy of this methyl-PSM filtering approach has, however, yet to be thoroughly evaluated. Here we conduct a systematic analysis of methyl-PSM FDRs across a range of sample preparation workflows (each differing in their exposure to the alcohols methanol and isopropanol) and mass spectrometric instrument platforms (each employing a different mode of MS/MS dissociation). Through 13CD3-methionine labeling (heavy-methyl SILAC) of S. cerevisiae cells and in-depth manual data inspection, accurate lists of true positive methyl-PSMs were determined, allowing methyl-PSM FDRs to be compared to target-decoy approach-derived methyl-PSM FDR estimates. These results show that global FDR estimates produce extremely unreliable methyl-PSM filtering criteria; we demonstrate that this is an unavoidable consequence of the high number of amino acid combinations capable of producing peptide sequences that are isobaric to methylated peptides of a different sequence. Separate methyl-PSM FDR estimates were also found to be unreliable due to prevalent sources of false positive methyl-PSMs that produce high peptide identity score distributions. Incorrect methylation site localizations, peptides containing cysteinyl-S-β-propionamide, and methylated glutamic or aspartic acid residues can partially, but not wholly, account for these false positive methyl-PSMs. Together these results indicate that the target-decoy approach is an unreliable means of estimating methyl-PSM FDRs and methyl-PSM filtering criteria. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large-scale LC-MS/MS methylation site discovery experiments, and make recommendations on how to reduce methyl-PSM FDRs in samples not amenable to heavy isotope labeling.

Dataset Information

Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.

Publications

Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.

Similar Datasets

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