At SNAP Biolabs, we provide customized flow cytometry solutions and are looking forward to working with you to achieve your goals.

Typically described using frequencies and fluorescence intensity in one- or two-dimensional gating steps.

SNAP Biolabs offers the following basic statistical analysis for flow cytometry:

Frequency analysis (evaluate changes in « % positive » events in the target population)
MFI analysis (assess levels of changes in the target protein expression)

Proliferation analysis (the best statistical method to compare growth curves for treated and untreated cells, e.g. multiple comparisons test, post-hoc tests, planned comparisons test, orthogonal comparisons)
Cell Cycle analysis (cell DNA content data normalization and gating, estimation of the proportion of cell populations in different phases of the cell cycle)

Typically used for high-dimensional data visualization and comparison of the phenotypes of cell clusters.

SNAP Biolabs offers the following advanced statistical analysis for flow cytometry:

t-SNE analysis (unsupervised, non-linear dimensionality reduction technique suitable for visualizing high dimensional datasets in two-dimensional space)

CytoCompare analysis (provides a multivariate distance measure as well as a p-value asserting the statistical significance of the phenotypical difference)

Typically used for high-dimensional data to differentiate hierarchical cell clusters associated with a specific phenotype.

SPADE analysis (unsupervised, non-linear dimensionality reduction technique will cluster cells in many groups and visualize the results in a minimal spanning tree)

FlowSOM analysis (similar to SPADE technique in which a discretized representation of the input space is trained and offers additional plotting and meta-clustering calculation)

Citrus analysis (supervised populations finding whose abundance differs between healthy and diseased patients, treated and untreated cells)

Typically used for high-dimensional data to uncover multidimensional relationships.

Principal Component Analysis (PCA) unsupervised, non-linear dimensionality reduction technique to study the most prominent variation in (co)-expression of all markers across all cells exhibited by specific phenotype.

Machine Learning Algorithms:

Develop predictive models that can accurately predict the outcome of experiments or treatments.
Classify cells and better understand the composition of a sample (e.g. to identify rare cell types or to identify cells that are associated with a particular disease or condition).
Evaluate correlations between flow cytometry data and other data sources (e.g. gene expression data) to understand biology of a system and how it is affected by different treatments or interventions.