Dataset Information

ProteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

ABSTRACT: The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed "proteiNorm". The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

SUBMITTER: Graw S

PROVIDER: S-EPMC7557219 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

Graw Stefan S Tang Jillian J Zafar Maroof K MK Byrd Alicia K AK Bolden Chris C Peterson Eric C EC Byrum Stephanie D SD

ACS omega 20200930 40

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a ...[more]

PMID: 33073088

Similar Datasets

Project description:Introduction: In several fields, the process of fusing multiple two-dimensional (2D) closed lines is an important step. For instance, this is fundamental in histology and oncology in general. The treatment of a tumor consists of numerous steps and activities. Among them, segmenting the cancer area, that is, the correct identification of its spatial location by the segmentation technique, is one of the most important and at the same time complex and delicate steps. The difficulty in deriving reliable segmentations stems from the lack of a standard for identifying the edges and surrounding tissues of the tumor area. For this reason, the entire process is affected by considerable subjectivity. Given a tumor image, different practitioners can associate different segmentations with it, and the diagnoses produced may differ. Moreover, experimental data show that the analysis of the same area by the same physician at two separate timepoints may result in different lines being produced. Accordingly, it is challenging to establish which contour line is the ground truth. Methods: Starting from multiple segmentations related to the same tumor, statistical metrics and computational procedures could be exploited to combine them for determining the most reliable contour line. In particular, numerous algorithms have been developed over time for this procedure, but none of them is validated yet. Accordingly, in this field, there is no ground truth, and research is still active. Results: In this work, we developed the Two-Dimensional Segmentation Fusion Tool (TDSFT), a user-friendly tool distributed as a free-to-use standalone application for MAC, Linux, and Windows, which offers a simple and extensible interface where numerous algorithms are proposed to "compute the mean" (i.e., the process to fuse, combine, and "average") multiple 2D lines. Conclusions: The TDSFT can support medical specialists, but it can also be used in other fields where it is required to combine 2D close lines. In addition, the TDSFT is designed to be easily extended with new algorithms thanks to a dedicated graphical interface for configuring new parameters. The TDSFT can be downloaded from the following link: https://sourceforge.net/p/tdsft.

Dataset Information

ProteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

Publications

proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets