Apparatus Platform

Premier toolkit for exploring post-translational biology

The Apparatus is an adaptive system that places your data into exactly the right set of algorithms to optimize performance for your particular experimental configuration. Each analysis comes with different needs, strengths and weaknesses. Our system offers advanced inferential tools for proteomics that cannot be found anywhere else. Below you will find a basic outline of the primary toolkit. Please check back frequently, as we pride ourselves on updating and improving our system as better strategies emerge.

Power

The Apparatus was built on cutting-edge advances in computational proteomics, and we continually update the system with the latest developments in computational biology. This ensures that you will have access to the most powerful techniques available to eliminate false discoveries and maximize the probability that you will find the signals needed to advance your research.

Intuition

The interactive data analysis platform makes it easy to develop an intuitive understanding of your data. Clicking through layers of evidence and links to databases of pre-existing biological knowledge, allows researchers to comprehend their experimental results at a level that cannot be achieved just by simply looking at a p-value.

Efficiency

With a few hours of processing time, The Apparatus can automate data science tasks that typically require weeks to months of high-paid labor. Our software will make your team more efficient by eliminating time spent on data manipulation, method development, quality control, and report generation.

Platform Features

Cloud-Based Computational Infrastructure

From data management to automated analysis and interactive exploratory tools, we handle your entire data processing pipeline. Our efficient operational model allows us to maintain our system at a significantly lower cost than what you would incur managing it internally.

Code-Free Linear Models

Automate everything. By simply uploading parameter files describing your experimental design, we use advanced conditional logic to automatically answer every question that might be explored in your data.

Interactive Visualizations

Enhance your experimental insights with our dynamic visualization tools. Our system empowers you to tackle complex, high-level questions and gain an intuitive understanding of data through quick visualization of sample profiles and underlying layers of evidence. Seamless integration with UniProt allows users to effortlessly enrich each discovery with vital biological context.

Post-Translational Modifications

By adjusting a single parameter, users can shift the analysis focus from proteins to peptide-level inferences. Our system automatically generates and analyzes equivalence classes of peptides, grouping together overlapping peptides with identical modifications, repeat scans, and charge state variants.

Heatmap, Clustering and Gene Set Analysis

Our interactive heatmaps enhance quality control by visualizing entire datasets or significant subsets, with annotations from the experimental design and column normalization factors. Additionally, our system utilizes specially designed algorithms for gene set analysis that leverage the properties of proteomics datasets.

Heteroskedasticity

The relationship between the number of ions collected and measurement precision is of paramount importance to the analysis of mass spectrometry data. We have incorporated information about data quality throughout our system allowing us to use more data (avoiding arbitrary signal-to-noise ratio) and to improve almost every aspect of data analysis.

Interbatch Analyses

Common workflows often overlook the number and quality of measurements across batches, leading to decreased sensitivity and increased false positives. Our algorithms are designed to account fully for technical variations between batches, maintaining data integrity and accuracy.

Eliminating interference

Isotopic impurities and TMT interference can cause both false positives and false negatives. Our advanced algorithms detect these issues ensuring robust and reliable results.

Column Normalizations

Our system automatically implements adjustable column normalizations to correct for systematic errors like pipetting inaccuracies across samples. These corrections are thoroughly documented and visualized to ensure data consistency.

Outlier Detection

Our system automatically identifies and removes outliers caused by misidentifications and interfering compounds at the scan level, enhancing the reliability of our data analysis.

Variance Moderation

Small sample sizes can lead to inaccurate variance estimates and skewed p-values. We integrate a prior distribution of technical variance to moderate these estimates, ensuring more stable and realistic statistical outcomes.

Multiple Imputations

Instead of discarding incomplete observations or relying on speculative imputations, our approach involves creating a plausible distribution for each missing value. We perform analyses multiple times with these imputed values to account for imputation errors, effectively reducing the risk of false positives commonly associated with missing data.

Longitudinal Models

Our models capitalize on the inherent correlations in repeated measurements from the same subjects, adopting proven strategies tailored for mass spectrometry proteomics. This approach maximizes the analytical value of longitudinal data.

The Apparatus