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Maximizing the Biological Interpretation of Gene, Transcript & Protein Expression Data with IPA(Ingenuity Pathway Analysis)

Qian Dong, Ph.D.

Field Application Scientist

[email protected]

mailto:[email protected]

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To install IPA client

1. Go to our website: http://www.ingenuity.com/products/ipa

2. Click on ‘LOGIN’

3. Click on ‘NEW: INSTALL IPA CLIENT’ under Ingenuity Pathway Analysis

http://www.ingenuity.com/products/ipa

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Sample Prep

AssayData

Statistics on raw data

Variants/Genes/Protein

s of Interest

Biological interpretation

Hypothesis generation

Samp

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Insigh

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Pathway Analysis

Upstream Analysis ‘Primary’ ‘Secondary’ ‘Tertiary’

What is Ingenuity Pathway Analysis [IPA]?

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QIAGEN Sample to Insight

Sam

ple

Ins

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Upstream Analysis ‘Primary’ ‘Secondary’ ‘Tertiary’

Sample

Prep

Assay

Data

Sequence-

Level

Statistics

Biology of

Interest

(Genes,

Variants, etc.)

Annotation &

Comparative

(Statistical)

Analysis

Annotation &

Biological

Interpretation

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Sample to Insight: Secondary, Tertiary analysis

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Data Analysis

Interpretation

RNA-seq

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IPA gives insight to data

IPA analysis

‘Omics data Pathways

Functions

Regulators

Mechanisms

• Given a large set of gene/proteins that are activated/inhibited/affected:

• Which physiological processes are being affected?

• What specific pathways are likely being perturbed?

• What upstream regulators are involved?

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IPA gives insight to data

• Given a gene/protein/compound

• What other molecules does it interact with?

• What side effects/processes is it associated with?

• What compounds affect its activity?

• Given a disease/process of interest:

• Which genes/proteins/metabolites are good biomarker candidates?

• Which are promising treatment targets?

IPA analysis

Pathways

Functions

Regulators

Mechanisms

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The Ingenuity Knowledge Base

The Ingenuity Ontology

Ingenuity FindingsIngenuity® Expert Findings – Manually

curated Findings that are reviewed, from the

full-text, rich with contextual details, and are

derived from top journals.

Ingenuity® ExpertAssist Findings –

Automated text Findings that are reviewed,

from abstracts, timely, and cover a broad

range of publications.

Ingenuity Modeled KnowledgeIngenuity® Expert Knowledge – Content

we model such as pathways, toxicity lists,

etc.

Ingenuity® Supported Third Party

Information – Content areas include

Protein-Protein, miRNA, biomarker, clinical

trial information, and others

Ingenuity Knowledge Base

Ingenuity Knowledge Base

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Directional Finding (Example)

genezygosity effect on disease

species evidence mutation type

disease

Activity of the

molecule in this

finding (decreased)

Infer

These findings power our analytics as well as our pathway building functionality

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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Thru Jul2015

14,311 publications and growing!

Peer-reviewed publications citing QIAGEN’s Ingenuity products

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Training Agenda

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SEARCHING THE KNOWLEDGEBASE:

– BIOPROFILER

– ISOPROFILER

– SEARCH & EXPLORE

DATASET ANALYSIS:

– CORE ANALYSIS

– COMPARISON ANALYSIS

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Resources

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Tutorials:

http://ingenuity.force.com/ipa/IPATutorials

Help and support:

http://ingenuity.force.com/ipa/IPA2SupportPage

[email protected]

Whitepapers:

http://www.ingenuity.com/products/ipa#/?tab=resources

Training Webinars:

http://www.ingenuity.com/science/training

http://www.ingenuity.com/products/ipa#/?tab=resources

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Searching the knowledge base

Title, Location, Date 13

BioProfiler: Quickly profile a disease or phenotype by understanding its associated genes and compounds

IsoProfiler: Quickly profile isoforms in datasets

Search and explore: build your own pathway with knowledge base

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Search and explore: build your own pathway with knowledge base

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BioProfiler: Quickly profile a disease, phenotype, or function

Get access to Ingenuity’s Knowledge Base:

use the KB to identify list of compelling genes/proteins/compounds that have

demonstrated some specified activity.

• Filter down to genes known to be causally associated with

Alzheimer’s

• Which genes when decreased in activity increase liver

cholestasis?

• What types of genetic evidence support this?

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BioProfiler: Examples

Targets of toxicity:

Which genes when [decreased] in activity [increase] [liver cholestasis]? What

types of [genetic] evidence support this?

Target discovery:

What [heterozygous knockouts] in [mouse] can [decrease] [asthma]?

Which drugs or which targets have been in late stage clinical trials or approved

to decrease [diabetes]?

Biomarker research:

Which genes are potential [diagnosis OR prognosis] biomarkers of [breast

cancer] and are [upregulated] in breast cancer?

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BioProfiler

Identify possible Drug Targets for Breast Cancer:

Which proteins/genes when decreased in activity are shown to decrease breast cancer phenotype in Mice?

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IsoProfiler: Quickly profile biological functions of isoforms in datasets

Search and explore: building your own pathway with knowledge base

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filter to determine if certain isoforms (splice variants and their products) are known to drive a disease or process

IsoProfiler

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IsoProfiler: Quickly profile biological functions of isoforms in datasets

Search and explore: building your own pathway with knowledge base

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Building networks based solely on current literature

Organize existing data: Visualize what is currently known in the literature and

databases.

‘What is known to be affected by SNAI1 gene?’

‘Is SNAI1 associated with breast cancer?’

Maximize biological interpretation: Once have obtained a subset of genes from core

analysis, further explore the biological network. (This will be demonstrated in the

afternoon)

‘what pathways are potentially regulated by predicted upstream regulator TGFB1?’

What drugs are targeting the ILK signaling pathway which is perturbed according to

my RNA-seq data?

Search & Explore

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Mesenchymal / stem cell-like breast cancerLuminal Breast cancer Basal HER2-enriched

SNAI1 is overexpressed in

Claudin-low cell lineLuminal cell lines Claudin-low cell lines

Epithelial to Mesenchymal Transition

Case study: breast cancer

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Questions:

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Given that SNAI1 is overexpressed in Claudin-low cell line:

• What genes/proteins are known to be affected by SNAI1 activity?

• Are these SNAI1-affected molecules involved in a common

biological function/process?

• If so, what would be the ultimate impact of SNAI1

activation/suppression on that process?

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SNAI1 downstream effect modeling

Search and Explore

Modeled effect on

EMT when

activating

expression of

SNAI1

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Search and explore: build your own pathway with

knowledge base

‘What is known to be affected by SNAI1

gene?’

BioProfiler: Quickly profile a disease or

phenotype by understanding its associated

genes and compounds

‘Which kinases when decreased in activity are

shown to decrease breast cancer phenotype in

Mice?’


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Core Analysis

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Mesenchymal / stem cell-like breast cancerLuminal Breast cancer Basal HER2-enriched

Ratio Claudin-low to Luminal5 vs 5 cell lines, RNA-Seq dataLuminal cell lines Claudin-low cell lines

Epithelial to Mesenchymal Transition

Case study: breast cancer

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Given the large dataset, You may want to ask these questions:

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How are Claudin-low and Luminal cell lines different regarding genes and

pathways?

What known biological pathways appear most significantly affected by the

genes in my data set?

What genes within a pathway are changing in expression and what effect

might that change have on the pathway?

Can I identify a drug or drug target?

What are the downstream effects of EMT? Can I learn more about the biology

of claudin-low cell line? What cellular functions are affected?

Can I create hypotheses that explain what may be occurring upstream to cause

particular phenotypic or functional outcomes downstream?

How are the genes in my dataset connect to each other? Can I visualize the

gene network?

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IPA core analysis: data-derived networks

Canonical Pathway Analysis

Predicts pathways that are changing

based on your dataset

Predict directional effects on the

pathway molecules not in dataset

(MAP overlay tool)

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Upstream Regulator Analysis

Predicts activated/inhibited regulators

responsible for observed data

Predicts master regulators (causal

network)

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Diseases and Functions Analysis

Predicts the directional biological

effects (cellular processes, biological

function) of gene/protein set

– “Increase in cell cycle”

– “Decrease in apoptosis”

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Regulator Effects

Identifies specific hypothesis:

upstream regulator pathways leading

to a downstream phenotype.

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Networks

Identifies gene networks within

dataset.

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Data upload

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Three file formats:

Excel spreadsheet

(single sheet only)

tab delimited text file

Cuffdiff file

One ID column and

header row

Multiple observation or

single observation

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Dataset with Multiple Observations


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Supported identifiers for data upload

Vendor IDs Gene Protein RNA-Seq MicroRNA SNP Chemical

Affymetrix Entrez Gene

(LocusLink)*

GenPept Ensembl miRBase

(mature)

Affy SNP IDs CAS Registry

Number

Agilent GenBank International

Protein Index

(IPI)

RefSeq miRBase

(stemloop)

dbSNP HMDB

ABI Gene Symbol-

human (HUGO/

HGNC, EG)

UniProt/ Swiss-

Prot Accession

UCSC

(hg18)

KEGG

Codelink Gene Symbol-

mouse (EG)

UCSC

(hg19)

PubChem CID

Illumina Gene Symbol- rat

(EG)

Ingenuity GI Number

UniGene

INGENUITY PATHWAY ANALYSIS

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Data Upload Format Examples

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Typical value-types that are uploaded to IPA

Identifier List

+differential

expression

+significance stat

+RPKM

(maximum RPKM between

experimental condition and control

recommended for RNAseq)

+variant gain/loss

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Data Upload Format Examples

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Typical value-types that are uploaded to IPA

Identifier List

+differential

expression

+significance stat

+RPKM

(maximum RPKM between

experimental condition and control

recommended for RNAseq)

+variant gain/loss

Required

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IPA Upstream Regulator Analysis

↑ ↓↓ ↑ ↑ Differential Gene Expression (Uploaded Data)↑

1 -11 1 1 1

+++-

Note that the actual z-score is weighted by the underlying findings, the relationship bias, and dataset bias

• z-score is a statistical measure of the match between expected relationship

direction and observed gene expression

• z-score > 2 or < -2 is considered significant

Literature-based effect TF/UR has on

downstream genes

Every possible TF & Upstream Regulator in the

Ingenuity Knowledge Base is analyzed

++

= (7-1)/√8 = 2.12 (= predicted activation)

↓

-

1

↑

1

+

Predicted activation state of TF/UR:1 = Consistent with activation of UR

-1 = Consistent with inhibition of UR

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Causal Network Analysis

Advanced Analytics

Alternate method of predicting upstream regulators based on causal

relationships and allowing multiple interaction steps to gene expression

changes

Identify potential novel master-regulators of your gene expression by creating

pathways of literature-based relationships

Expands predictions to include indirect upstream regulators not in mechanistic

networks

Upstream Regulators

A

Targets in the dataset

Upstream

Regulator

Scoring

Casual connection to

disease, phenotype,

function, or gene of

interest

B

Causal Networks

Master Regulator

TF

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Upstream regulator

Causal network

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Regulator Effects

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Targets in the dataset

Upstream

Regulator

Disease or

Function

Algorithm

Causally consistent networks score higher

The algorithm runs iteratively to merge additional regulators with diseases and functions

First iteration

Hypotheses for how activated or inhibited upstream

regulators cause downstream effects on biology

Displays a relationship between the

regulator and disease/function if it exists

Downstream Effects Analysis

Disease or

Function


Upstream

Regulator

Simplest Regulator Effects result

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Question: How are Claudin-low and Luminal

cell lines different regarding genes and known

pathways?

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Question: What upstream molecules are

regulating the changes of EMT? Can I identify

a drug or drug target?

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

Genes/P

roteins

affected

in

dataset

Known

targets

of UR

Molecules detected in

dataset AND targets of

UR

Enrichment p-value

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Question: What are the downstream

effects of EMT? Can I learn more about

the biology of EMT? What cellular

functions are affected?

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Question: Can I create hypotheses that

explain what may be occurring upstream

to cause particular phenotypic or

functional outcomes downstream?

Hypothesis: SNAI1/Mek/ZEB1 regulate

EMT by activating or repressing ten

genes in the dataset.

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Question: How are the genes in my

dataset connect to each other? Can I

visualize the gene network?

Regulatory

Effects

Disease and

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Networks

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Search and ExploreBuild, Overlay and other tools

Bioprofiler and IsoprofilerProfiling genes, isoforms, phenotypes and diseases

Core AnalysisPathways, Diseases, regulators enrichment

Tools are interconnected

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Core Data Analysis- Summary

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Canonical Pathway Analysis

Predicts pathways that are changing based on your dataset

Predict directional effects on the pathway molecules not in dataset (MAP overlay

tool)


Predicts activated/inhibited regulators responsible for observed data

Predicts master regulators

Diseases and Functions Analysis

Predicts the directional biological effects (cellular processes, biological function)

of gene/protein set

– “Increase in cell cycle”

– “Decrease in apoptosis”

Regulator Effects

Identifies specific hypothesis: upstream regulator pathways leading to a

downstream phenotype.

Networks

Identifies gene networks within dataset.

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Comparison Analysis

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Comparison Analysis

Compare data from multiple observation datasets

Timecourse: 12hr, 24hr, 48hr

Different treatment: drug 1, drug 2, drug 3

Same treatment, different models: human cells, mice model 1/2/3

Questions:

Between three different core analyses/observations,

Any canonical pathway/upstream regulator/disease and function in

common?

How are they different?

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Data Upload: one or multiple excel files

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MCF7 breast cancer cell line treated with estrogen: 12hr, 24hr, 48hr

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Gene heatmap

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Sort

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microRNA Target Filter

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microRNA analysis in IPA

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Core analysis directly on microRNA IDs:

Canonical pathways affected by microRNA

Upstream regulators that act on microRNA

Downstream diseases and functions affected by

microRNA

Derive regulator effects networks

Little is known about pathways that involve microRNAs and phenotypes associated with microRNAs directly.

Much is known about which genes your microRNAs target!

Analyze known gene targets of expressed/repressed microRNAs

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microRNA target filter workflow


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Case Study: Metastatic melanoma


Experimental conditions: IGR37 metastatic melanoma cell line ratio'ed to NHEM normal skin cell line

Same cells – miRNA differential expression (miRNA expression profiling)

-- mRNA differential expression (microarray)

Questions:

1. What are the potential miRNA targets?

2. Any of those miRNA targets overlap with mRNA data?

3. What are the potential impact of miRNA differential expression?

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microRNA Target Filter: identify relevant gene targets

miRNA measured Relationship Target mRNA

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miRNA measured Relationship Target mRNA Target info

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Identify target relationships with best evidence

miRNA measured Relationship Target mRNA Target info

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miRNA measured Relationship Target mRNATarget info


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Identify pathways and downstream gene functions with Core Analysis

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Visualize miRNA-mRNA relationships

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ADVANCED ANALYTICS

– CAUSAL NETWORK

– BIOPROFILER

– ISOPROFILER

– RELATIONSHIP EXPORT

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Relationship Export


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Ingenuity Pathway Analysis

Learn about genes, proteins, and metabolites and explore their relationships with

biological processes and diseases in Ingenuity Knowledge Base™

Discover and explore known relationships between drugs and targets

Derive biological pathways, regulatory mechanisms, and interaction networks from

your ‘omics data

Identify changes in pathways and regulatory mechanisms across experimental

conditions (time course, dose response, etc)

Compare pathways and regulators across experimental groups

QUESTIONS?CONTACTS:

General: [email protected]

mailto:[email protected]

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