Statistical Consulting Services

Our Services

Models

Quantitative models for hypothesis testing

Data analysis

Data visualization and statistical inference.

Software

Graphical interfaces for data visualization and analysis.

Education

Training on statistical methods with R and Python.

Our work

Neuroscience

  • Image processing, registration, segmentation
  • Spike inference from fluorescence time series
  • Custom software for behavioral and imaging data
  • Analysis of behavioral movies
  • Decoupling of evoked and spontaneous activity using generalized linear models

Biostatistics

  • Principal component analysis
  • Clustering methods
  • Hierarchical models
  • Gene regulatory networks
  • Factor analysis for latent variable analysis
  • Patient cohort stratification
  • Design of clinical trials
  • Generalized linear models

Bioinformatics

  • RNA sequencing
  • Principal component analysis
  • Clustering methods
  • Gene regulatory networks

Mechanistic models

  • Markov model simulations
  • Computational fluid dynamics
  • Energy-based models
  • Recurrent networks
  • Dynamical systems
  • Functional mock-up interfaces (FMU)

Personalized training

  • Statistical inference
  • Data visualization
  • Graphical iser interfaces
  • Probabilistic programming

Our Approach

Discuss

Understanding your questions and hypotheses is our priority. For the success of a multi-disciplinary project is crucial to establish a dialog between different expertise areas. Our experience across scientific disciplines is key to deliver dedicated products tailored for the specific needs.

Formalize

We use quantitative models to formalize your knowledge about the data. This enables us to use rigorous statistical methods to infer interpretable features.

Solution

We combine our computational modeling expertise with software development to provide reliable utilities for data analysis and visualization

Follow-up

We provide on-demand support for maintaining our products in order to insure their stability over changes that might occur in your pipelines or software updates.

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