High-Dimensional Data Analysis

Pipelines for HD Data Analysis

In This Video

A brief introduction to pipelines in FCS Express will be given. Pipelines are a set of data processing steps that stand alone or are connected in series. The output of a step can be applied to a data file or utilized as the input of the next step, or series of steps, that may be applied to your data.

Learn How To

Add a Pipeline to a FCS Express Layout
Define the Main Pipeline Body
Select and Add Additional Pipeline Steps

HD Data Analysis Part 1 Pipelines

In This Video

Learn about the importance of Pre-processing data. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time.

Learn How To

Identify Data Pre-processing Steps
Evaluate the Pre-processing Steps Available
Recognize Differences Between Pre-processing Steps

HD Data Analysis Part 2 Scaling

In This Video

Learn about scaling data in preparation for advanced analysis of your data set. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time.

Learn How To

Choose an Appropriate Scaling for Data
Differentiate between Hybrid Scales
Stabilize Variance between High and Low Values

HD Data Analysis Part 3 Normalization

In This Video

Understand the importance of normalization when pre-processing data for advanced analysis. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time.

Learn How To

Bring Parameter Distributions into Alignment
Compare Scaled, Scaled and Normalized, and Scaled and 0 to 1 Scaled Data
Evaluate how Normalization Impacts Data

HD Data Analysis Part 4 Downsampling

In This Video

Evaluate how to apply downsampling to your data sets. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time.

Learn How To

Define Downsampling Methods
Use Downsampling to Select for Rare Populations
Compare and Evaluate Downsampling Methods

HD Data Analysis Part 5 Cleaning

In This Video

Explore cleaning algorithms to remove outliers from data to enhance downstream advanced analysis. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time. In this webinars, we will take advantage of the easy-to-use Pipeline tools provided in FCS Express 7 to help researchers better understand how to create HD Data Analysis workflows.

Learn How To

Recognize the Importance of Data Cleaning
Differentiate between FlowAI and FlowCut Algorithms
Integrate Cleaning Algorithms in a Pipeline

HD Data Analysis Part 6 Merging and Conclusion

In This Video

Recognize how merging data files can be beneficial to downstream advanced analysis processing. High-Dimensional (HD) Data Analysis is usually associated with algorithms such as UMAP, FlowSOM, tSNE, and the latest iterations of these tools. However, these tools are often a small part of the overall analysis. Pre-processing of data is crucial yet is often underestimated, misunderstood, or poorly addressed by scientists using HD Data Analysis tools for the first time. In this webinars, we will take advantage of the easy-to-use Pipeline tools provided in FCS Express 7 to help researchers better understand how to create HD Data Analysis workflows.

Learn How To

Recognize the Importance of Merging Data Files
Evaluate Advantages and Disadvantages of Available Merging Methods
Utilize Different Methods to Merge Data Files

File Merging and Concatenation (Short video)

In This Video

Learn how merging multiple files and samples can be quickly and easily performed in FCS Express using Batch Export tools. A merged file is useful when comparing multiple data sets and tSNE/SPADE transformations across a range of samples. Real time gating and statistics can be used with a merged file just like any other data file in FCS Express.

Learn How To

Perform a Virtual File Merge
Define Batch Export Options
Export a Merged Data File

File Merging and Concatenation (Full video)

In This Video

Watch this video to learn about the file merging and concatenation tools in FCS Express 7. With virtual file merging, we allow users to more easily combine data files, use plate heat maps to work with multiple samples even if those data were not acquired on a plate, and improve analyses such as tSNE and SPADE by combining data from multiple groups or experiments.

Learn How To

Evaluate the File Merging Tools Available
Separate Data Files Within a Merged Data File
Utilized Merged Data for Downstream Analysis

UMAP

In This Video

Explore the use of Uniform Manifold Approximation and Projection, or UMAP for advanced analysis. UMAP is a dimensionality reduction technique that allows users to create new UMAP X and UMAP Y parameters from a high-dimensional dataset. Please watch this brief video to learn more about how to use UMAP in FCS Express.

Learn How To

Build a Pipeline for UMAP
Adjust UMAP Variables
Apply the UMAP Transformation to Data

Phenograph

In This Video

Learn how to easily incorporate Phenograph into your high-dimensional data analysis. Phenograph is a clustering tool that facilitates the analysis of high-dimensional data. Phenograph is integrated as an algorithm into FCS Express’s comprehensive Transformations library.

Learn How To

Merge Data Files
Build a Pipeline for Phenograph
Apply Phenograph Pipeline to Data

tSNE (Short video)

In This Video

Explore how to apply and manipulate tSNE directly in FCS Express. High-dimensional single-cell technologies, such as multi-color flow cytometry, mass cytometry, and image cytometry, can measure dozens of parameters at the single-cell level. FCS Express integrates t-Distributed Stochastic Neighbor Embedding, otherwise known as t-SNE, to allow you to map high-dimensional cytometry data onto a two dimension plot while conserving the original high-dimensional structure to help you visualize and analyze high-dimensional data.

Learn How To

Add a tSNE Transformation
Define Options for tSNE Transformation
Apply a tSNE Transformation to Data

tSNE (Full video)

In This Video

Learn how to apply and manipulate tSNE directly in FCS Express. High-dimensional single-cell technologies, such as multicolor flow cytometry, mass cytometry, and image cytometry, can measure dozens of parameters at the single-cell level. FCS Express integrates t-Distributed Stochastic Neighbor Embedding, otherwise known as t-SNE, to allow you to map high-dimensional cytometry data onto a two dimension plot while conserving the original high-dimensional structure to help you visualize and analyze high-dimensional data.

Learn How To

Create a tSNE Transformation
Set Options to Optimize tSNE
Apply tSNE to Data and View Across Multiple Parameters

FlowSOM (Short video)

In This Video

Investigate how to FlowSOM in FCS Express. FlowSOM is a clustering and visualization tools that facilitate the analysis of high-dimensional data. FlowSOM clusters the input dataset using a Self-Organizing Map allowing users to cluster large multi-dimensional data sets in a short time. The resulting clusters are then presented to the user as a Minimum Spanning Tree in which each clusters are connected to the closest cluster.

Learn How To

Create a Pipeline for FlowSOM
Define Options in Substeps of the FlowSOM Algorithm
Display FlowSOM results on Plate Heat Map

FlowSOM (Full video)

In This Video

Learn how to incorporate FlowSOM into your advanced data analysis. FlowSOM is a clustering and visualization tools that clusters data using a Self-Organizing Map allowing users to cluster large multi-dimensional data sets in a short time. Watch this webinar to learn how FlowSOM works and how to run it in FCS Express 7.

Learn How To

Add the FlowSOM Pre-Defined Algorithm to a Pipeline
Set up FlowSOM to Optimize Results
Display and Interpret FlowSOM Results

SPADE (Short video)

In This Video

The short overview video covers the basics of working with SPADE in FCS Express via an easy to use interface for Flow and Image cytometry derived data sets. SPADE is visualization and clustering tool that helps reduce the dimensionality of clusters and data for further data exploration.

Learn How To

Add a SPADE Transformation
Define SPADE Options
Display and Format SPADE results

SPADE (Full video)

In This Video

This webinar covers the basics of working with SPADE in FCS Express, in an easy to use interface for Flow and Image cytometry derived data sets. SPADE is a visualization and clustering tool that reduces the dimensionality of clusters and data for further data exploration.

Learn How To

Reduce Dimensionality Using SPADE
Adjust Options to Optimize SPADE Results
Visualize SPADE Transformation to Plate Heat Map

FlowAI

In This Video

Explore how FlowAI can be beneficial for data and enhance downstream advanced analysis. FlowAI allows the user to perform quality control on flow cytometry data in order to improve both manual and automated downstream analysis. The algorithm removes events with anomalous values by taking into account three aspects of a flow cytometry data file: 1. Flow rate 2. Signal acquisition 3. Dynamic range Please watch this quick video to learn more about this feature.

Learn How To

Introduce FlowAI in a Pipeline
Set Substep Options to Optimize FlowAI
Apply FlowAI to Data

High Dimensional Analysis and Visualization of Flow Cytometry Data

In This Video

In this webinar, we cover the basics of High Dimensional Data Analysis in Flow Cytometry – with a focus on understanding the background concepts needed when using these tools, what each of the common methods actually do, and why they are necessary.

Learn How To

Approach Dimensionality Reduction for Data Analysis
Differentiate Dimensionality Reduction Methods
Evaluate When to Use a Given Method

Python Integration

In This Video

Learn about Python integration with FCS Express and how it allows users to run Python scripts as part of FCS Express pipelines providing unprecedented power and flexibility for high-dimensional data analysis. Python integration via FCS Express provides access to algorithms such as PHATE, PARC, TriMAP, Fit-SNE, and many others. Join this webinar to learn more on the Python integration and to start analyzing your high-dimensional data with this brand-new toolbox.

Learn How To

Use Python Integration
Build a Pipeline for Python
Apply the Python Pipeline to a Plot

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