Machine Learning Assisted Population Identification
The Cytobank platform is a cloud-based platform that accelerates research productivity by enabling you to analyze and visualize multiple single-cell data sets simultaneously. This video covers the experiment manager, experiment summary, working illustration, exporting statistics, and sharing and managing your experiments within Cytobank.
The Cytobank platform utilizes the experiment manager to organize data, analysis, and results. The experiment manager facilitates collaboration across labs, institutions, and the scientific community. This video covers the features of the experiment manager in detail.
CITRUS (cluster identification, characterization, and regression) is an algorithm designed for the fully automated discovery of statistically significant stratifying biological signatures. Input data includes single cell datasets containing numerous samples across multiple known endpoints (e.g., responders versus non-responders). This video covers the basics of CITRUS within Cytobank, advanced applications of CITRUS, and a brief Q&A session.
SPADE stands for "Spanning-tree Progression Analysis of Density-normalized Events." SPADE clusters phenotypically similar cells into a hierarchy that allows high-throughput, multidimensional analysis of heterogeneous samples. This tutorial shows how to run a SPADE analysis on the Cytobank platform.
FlowSOM is a clustering algorithm that speeds time to analysis and quality of clustering with Self-Organizing Maps (SOMs) that can reveal how all markers are behaving on all cells and can detect subsets that might otherwise be missed. It clusters cells (or other observations) based on chosen clustering channels (or markers/features), generates a SOM of clusters, produces a Minimum Spanning Tree (MST) of the clusters, and assigns each cluster to a metacluster, effectively grouping them into a population. The FlowSOM algorithm outputs SOMs and MSTs showing population abundances and marker expression in various formats including pie charts, star plots, and channel-colored plots. This video covers the background of FlowSOM, how to configure a FlowSOM run within Cytobank, and how to interpret the results.
viSNE is a dimensionality reduction algorithm that reduces high-parameter data down to two dimensions for rapid exploratory data analysis of any data type. The output is a set of two new parameters, viSNE 1 and viSNE 2. These parameters can be used to create a viSNE map that can be color-coded by channel to allow for easy visualization of highly complex data. This video covers the background of viSNE, how to configure a viSNE run within Cytobank, and how to interpret the results.