| Chapter title |
Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test.
|
|---|---|
| Chapter number | 9 |
| Book title |
Proteome Bioinformatics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6740-7_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6738-4, 978-1-4939-6740-7
|
| Authors |
Hien D. Nguyen, Geoffrey J. McLachlan, Michelle M. Hill, Nguyen, Hien D., McLachlan, Geoffrey J., Hill, Michelle M. |
| Editors |
Shivakumar Keerthikumar, Suresh Mathivanan |
| Abstract |
Comparative profiling proteomics experiments are important tools in biological research. In such experiments, tens to hundreds of thousands of peptides are measured simultaneously, with the goal of inferring protein abundance levels. Statistical evaluation of these datasets are required to determine proteins that are differentially abundant between the test samples. Previously we have reported the non-normal distribution of SILAC datasets, and demonstrated the permutation test to be a superior method for the statistical evaluation of non-normal peptide ratios. This chapter outlines the steps and the R scripts that can be used for performing permutation analysis with false discovery rate control via the Benjamini-Yekutieli method. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 20% |
| Student > Bachelor | 1 | 20% |
| Unknown | 3 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 40% |
| Unknown | 3 | 60% |