• Spectrolyzer P for MALDI
• A software package for MALDI-based label-free quantitative proteomics
• Statistical analysis
• Pipeline for MALDI data preprocessing
• Examples
• Videos
• Download Trial
• I'm Interested

• Importing many different MS data formats is possible
• Spectrolyzer is a complete MS pre-processing tool and does not require any other software like Matlab or R to help out with standard data processing
• Partially preprocessed MS spectra can be easily exported and further analyzed using e.g. external software

For more details see: Spectrolyzer for MALDI product brochure

Spectrolyzer can perform all stages of processing required for analyzing large sets of MALDI data. The software contains the following preprocessing methods:

• normalization,
• baseline subtraction,
• Savitzky-Golay filtering,
• peak detection,
• peak merging,
• binning.

Thereafter, Spectrolyzer is able to perform different statistical analyses, e.g. build diagnostic models and detect features which best discriminate between two different groups of samples (control samples and disease samples).

In collaboration with renowned research groups, e.g. Professor Kaj Blennow (from Sahlgrenska University Hospital), we have developed a comprehensive set of tools for preprocessing MALDI data. These tools are available in the software Spectrolyzer together with other tools for multivariate statistics.

Using Spectrolyzer, raw MALDI data can be prepared for comprehensive statistical analyses. This includes various one- and multi-dimensional data exploration tools as well as advanced multivariate statistical methods. Special emphasis is given to methods supporting biomarker discovery.

1. Noise Filtering (Smoothing)
   1. Noise Filtering (Smoothing) — Savitzky-Golay filter can be used to reduce noise in MS measurements. The underlying idea of smoothing is to average neighboring points (measuring nearly the same underlying value), in order to reduce the level of noise without much biasing the value obtained.
2. Baseline Subtraction
   1. Baseline Subtraction — Baseline subtraction is performed to remove the baseline signal from each spectrum.
3. Normalization
   1. TIC Normalization / Peak Normalization – Two normalization methods are available, one (Peak Normalization) for peaks and one (Total Ion Current (TIC) Normalization) for raw data.
4. Peak Extraction by Clustering
   1. Peak Detection — Peptide signals usually appear as local maxima (i.e. peaks) in MS spectra, hence an efficient approach to extracting significant peaks is needed to get meaningful and interpretable results. Peak Extraction by Clustering detects peaks automatically, taking into account both a noise level and a presence of the baseline trend in MS proteomic data.
5. Peak Detection by Custom Peak Integration
   1. Peak Detection by Custom Peak Integration — a peak detection method that requires that peaks are manually prepared as a TXT file and applied onto the analyzed spectra.
6. Merging Replicated Samples
   1. Merging Replicated Samples — A method for merging different samples together, e.g. if the dataset contains two or more replicated spectra for the same experiment or patient.
7. Peak Alignment by Binning 
   1. Peak Alignment by Binning — Algorithm for grouping peaks into bins (features). Binning algorithm is used to prepare MS data for multivariate statistical analyses, i.e. data are transformed into expression profile matrix. It is a mandatory step before any statistical analysis can be performed.

Don't just take our word for it, download Spectrolyzer today and see for yourself. Your data holds much more than what your current software is showing you!

Do you need more information? Want to ask about a specific feature or work on a project with us? Send us your contact information and we'll get in touch immediately.