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High-Dimensional Data Analysis

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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

4 mins

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

30 mins

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

32 mins

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

26 mins

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

33 mins

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

28 mins

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

21 mins

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

4 mins

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

38 mins

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

4 mins

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

8 mins

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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

4 mins

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

1 hour 4 mins

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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

4 mins

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

1 hour 4 mins

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

4 mins

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

1 hour 7 mins

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

6 mins

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

54 mins

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

55 mins

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