4.1 Introduction to FlowJO V 10.6 Flow Cytometry Analysis Software

Developed in the 1990s by experts such as Mario Roederer from Stanford University. From the initial V1 version to the current V10 version, it has gone through a development history as long as that of Windows.

The usage rate in top immunology journals is 97%.  1.FlowJO has comprehensive and professional analysis functionsLeft Side:
A Long Developing History (Long research and development history) Multiple Analysis Tools (Rich specialized functions)
Many Plugins (Rich functions with multiple plugins)
Right Side:Any FCS File from Any Cytometer (Full compatibility with FCS files from any cytometer)
Publication Quality Graphics (High-quality graphical results suitable for publications)
Quickly Update (Rapid optimization and updates) 2
FlowJO has rich specialized analysis functions

◆ Compensation adjustment matrix If the data of the fluorescence compensation single-stain control is good, the grouping of positive cell populations and negative cell populations will be obvious. Just import the data of the single-stain tube into the compensation editor of FlowJo, and FlowJO can automatically set gates, define negative and positive cell populations, and calculate the fluorescence compensation matrix.

 ◆ Bioanalysis platform
For tracking cell proliferation, tracers such as CFSE are generally used to label cells. As the number of cell division generations increases, the dye is gradually diluted, resulting in a distribution where the fluorescence intensity decreases with each generation. This makes it easy to analyze the number and proportion of cell proliferation generations. In the past, Modfit was the first choice for cell proliferation analysis, but now the latest version of FlowJO has added this analysis function.
3 FlowJO is fully compatible with all FCS format files FlowJO high-quality results (output in different forms)

◆4  T-SNEdimensionality reduction analysis
T-distributed Stochastic Neighbor Embedding(T-Distribution Stochastic Neighbour Embedding) is a machine learning method for dimensionality reduction that helps us identify associated patterns.

The main advantage of t-SNE is its ability to preserve local structures. This means that points that are close to each other in the high-dimensional data space remain close when projected into the low-dimensional space. t- SNE can also generate beautiful visualizations, known as "brain maps" Streaming data can be subjected to multiple rounds of cluster analysis on samples using the

FlowSOM algorithm, and then the proportions of each subpopulation contained in different groups of samples can be.