Delivering scientific precision through a variety of high-dimensional analytic techniques.
Our team of scientists and dedicated analysts collaborate to provide sound scientific evaluation of your flow cytometry and single-cell proteomic data sets, ensuring all data files and statistically relevant reportables are available to our sponsors.
We specialize in turning around high dimensional 18 parameter flow cytometry and 40 parameter single-cell proteomic analysis with reproducible precision and performance.
Our efficient data collection, collation and interpretation processes aim to
Improve your critical decision making
Provide reliable analytics through your discovery
Accelerate your translational and clinical trial programs
Scientifically driven: Our immunology and flow cytometry teams work collaboratively to design panels, outline gating strategies and identify statistically significant reportables to address your program needs. Together they provide a customized data analysis program to deliver comprehensive reportable-summaries to actively address FDA and compliance needs.
Quality Focused: All of our data is 100% QC’d by our highly qualified senior flow cytometrists to ensure we delivery with speed, precision and uncompromised quality.
Committed to YOUR timelines: We collaborate fully with our sponsors to address all budget and timeline constraints. Our project management teams routinely provide regular updates and on-demand analytics, to ensure optimal study reporting.
Providing profound insights into the cellular responses
There are two main way of gating cell populations: Heirarchical and Boolean gating.
Heirarchical gating uses a series of gates, establishing parent gates and sequentially drilling down to define child gates; the criteria of the parent gate is passed on to a child gate.
Boolean or logic gating enables gating to be performed based on ‘AND’, ‘OR’ and ‘NOT logic, for example, it can be useful for finding cell population that express biomarkers a and b, but not c. Boolean and hierarchical gates can be combined when looking for rare cell populations, and markedly improve the efficiency of flow cytometry analysis.
Analysis of Clinical Trial Data High dimensional cytometric analysis can be extremely powerful in the assessment of drug safety and efficacy in clinical trials. Non-intuitive analysis of cohorts can benefit from an analytical approach referred to as tSNE (t-Distributed Stochastic Neighbor Embedding).
This analytical technique uses a dimensionality reduction algorithm to reduce data from N-dimensional distinction into two dimensions while maintaining the structure of the data.
This process can help identify unique phenotypic populations within the data set that might otherwise be missed through conventional analytics.
To learn more about the potential of tSNE analysis of flow cytometry data, refer to these publications below and contact one of our representatives.
Our broad portfolio of data analytic tools along with the expertise of our scientists allows you to get the most information from your precious samples and endpoints.
Van der Maaten (2014) Accelerating tSNE using Tree-Based Algorithms. Journal of Machine Learning Research 15: 3221-324
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