MAP Version 1
MAP is an useful tool for model-based analysis of proteomic data to detect proteins with significant abundance changes between two samples.
Input Parameters
How to cite
Mushan Li, Jian Liu, Qian Wang, Yuannyu Zhang, Jian Xu, Yijing Zhang, Feng Zhou and Zhen Shao (2018) MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes. (under review)
Help information

Version 1
Usage: -text_data -strategy -window_size -step_size -fraction_of_proteins
-text_data              This tab-delimited text file is expected to be a table with 3 columns: (1) Gene symbols, (2) MS                                         intensities of sample 1 and (3) MS intensities of sample 2.
-strategy               Trimmed_total_count/Total_count/Pass
-window_size        Size of the sliding window to scan the ratio-intensity plot (similar to the M-A plot widely used for                                     microarray data analysis). By default: 400 proteins.
-step_size              Step size of the sliding window. By default: 100 proteins.
-fraction_of_proteins   The fraction of proteins with the weakest intensity changes of each window used to model                                             technical variations. They are assumed to be predominantly comprised of non-differentially                                             expressed proteins. By default: 50 (values higher than 60 or lower than 30 is not recommended).

Parameters Description
-IN: Input data
this tab-delimited text file is expected to be a table with 3 columns: (1) Gene symbols, (2) MS intensities of sample 1 and (3) MS intensities of sample 2.
-S: Strategy
-W: Strategy
size of the sliding window to scan the ratio-intensity plot (similar to the M-A plot widely used for microarray data analysis). By default: 400 proteins.
-Step: Step size
step size of the sliding window. By default: 100 proteins.
-F: Fraction_of_proteins
the fraction of proteins with the weakest intensity changes of each window used to model technical variations. They are assumed to be predominantly comprised of non-differentially expressed proteins. By default: 50 (values higher than 60 or lower than 30 is not recommended).
  • Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No. XDB13000000
    Maintained by BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences.