tv.qiagenbioinformatics.com

Presented by Lynne Mullen, Ph.D.

Learn how to format your own data and how to upload it into IPA. IPA can upload your experimental data to enable you to perform pathways visualization, literature searches on the molecules in the dataset, and enable you to perform the many different type of analyses offered in IPA. In this webinar we will use metastatic melanoma microarray data as an example dataset.

Learn how to:

Sections:

Minutes                  Description

0:00-2:08                 Introduction to the webinar

2:09-3:06                 Description of the dataset used for the demonstration

3:07-7:17                 Rules for formatting the dataset

7:18-9:44                 Data measurements values that can be used

9:45-11:24               Additional formatting rules and potential issues

11:25-12:47             How to import Cuffdiff files

12:48-14:48             Where you can upload data in IPA

14:49-15:12             Navigating to the dataset

15:13-17:40             Dataset upload options

17:41-22:45             Mapping columns in the Raw Data view

22:46-24:03             Adding metadata

24:04-24:35             Saving the dataset

24:36-29:57             Creating Core Analysis settings

29:58-31:14             Previewing the dataset

31:15-35:10             Setting cutoffs

35:11-38:41             Starting the analysis and Summary

38:42-39:41             Upcoming webinars

39:42 to

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0:00-2:09             Introduction to the webinar

2:10-3:44             Example dataset used for the live demonstration

3:45-7:58             Dataset formatting rules

7:59-8:57             Multiple Observation dataset example

8:58-11:42          Types of data measurement values you can use

11:43-13:03        Additional formatting rules

13:04-14:32        Uploading files from Cufflinks software

14:33-16:22        Live demo: where you can start the upload process

16:23-26:06        Uploading data with Core Analysis options

26:07-27:07        Setting up the type of Core Analysis to be used

27:08-29:21        Create Core Analysis page: General Settings

29:22-30:38        Create Core Analysis page: Network options

30:39-31:26        Create Core Analysis page: Other filtering options

31:27-32:57        Create Core Analysis page: Preview dataset options

32:58-34:31        Create Core Analysis page: Applying cutoffs

34:32-35:36        How to start the analysis

35:37-38:07        Summary of the webinar

38:08-end            Customer Support and additional resources information

Learn how to format your own data and how to upload it into IPA. IPA can upload your experimental data to enable you to perform pathways visualization, literature searches on the molecules in the dataset, and to perform the many different types of analyses offered in IPA. In this webinar we will use phosphoproteomics data as an example dataset.

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Learn how to view and interpret your comparison analyses results in IPA and the multiple ways of relating the molecules in your dataset to the body of information in the Ingenuity Knowledge Base. IPA allows you to compare analysis results from multiple experimental groups to identify similarities, differences, and trends.  Heatmaps for Canonical Pathways, Upstream Regulators, and Diseases & Functions allow you to easily and quickly view information from your analyses side-by-side. You can also visualize Gene Heatmaps to gain instant insight into specifically how genes affect your results.

Learn how to view and interpret your Phosphoproteomics Analysis results in IPA. Find out how to understand your Phosphoproteomics Analysis and about the multiple ways of relating the molecules in your dataset to the body of information in the Ingenuity Knowledge Base.

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New user training: Large dataset analysis and knowledge base queries using QIAGEN Ingenuity Pathway Analysis (IPA)

Users will learn how to:

Learn how to interpret your p-value of overlaps results in IPA. You can gain a deeper understanding of the right-tailed Fisher’s Exact Test, why it’s used in IPA, how the results are calculated, and caveats to keep in mind when interpreting your results.
For more information visit our website

Sections:

Understanding the tumor microenvironment (TME) presents a major challenge for biopharma researchers and translational scientists. The complexity of cellular interactions, variability across patient samples, and overwhelming volume of public omics data can make it difficult to pinpoint key molecular drivers and predict functional consequences. Building a clear, actionable view of the TME often feels like assembling a puzzle without the picture on the box.

In this webinar, discover how QIAGEN Ingenuity Pathway Analysis (IPA) can help overcome these challenges by combining expertly curated biological knowledge with powerful predictive modeling tools. Learn how to easily construct molecular networks, interpret gene expression data from resources like TCGA and GEO, and model upstream regulators to predict immune and tumor behavior. Using examples such as IL1B’s role in non-small cell lung cancer (NSCLC), we’ll show you how IPA can accelerate your discoveries and guide therapeutic insights.

What you’ll learn:

• How to identify disease-associated molecules and visualize their biological interactions

• How to mine and interpret public gene expression data for deeper insights into cancer biology

• How to model upstream regulators and predict functional outcomes within the TME

This 90-minute training session is about how QIAGEN Ingenuity Pathway Analysis (IPA) allows visualization of molecular intricacy and variations at multiple levels such as transcriptome, proteome, and metabolome. Through a series of brief technical vignettes, it is demonstrated how to:

· Generate associations among molecular signatures obtained via integrating multi-omics data

· Extract mechanisms from multi-omics data for precision medicine

· Disease stratification based on multi-omics profiles

· Map disease networks among targets and indications