ingenuity pathway analysis(ipa) · uniprot/ swiss-prot accession ucsc (hg18) kegg codelink...
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IPA資料庫使用介紹:如何查找資訊、繪製路徑及上傳dataset
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Clair Tsai 蔡宜庭
創源生技業務主任
Ingenuity Pathway Analysis(IPA)
Basic Training Course
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About GGA
CEO: Christopher Tsai, Ph.D. 蔡政憲 博士
Established: Nov. 2008
Main Product & Service Areas:
Genetic Testing & Molecular Diagnosis
Scientific Informatics & Bio IT
IPO Date: September 17, 2012
Stock Ticker: 4160 (Taiwan OTC)
GGA is part of the BIONET Group
(訊聯生物科技)
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Molecular Science Center in GGA
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生物資訊
奈米與材料科學
企業品質管理系統
電子化實驗室管理
生命科學
化學資訊
分子視算中心MSC
世界一流資訊產品
軟體與資料庫
安裝與調校合規電腦
系統確效(CSV)
在地化服務使用者教育訓練
* FIRST *
* ONLY *
02-2795-1777#3012
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Copyright and Disclaimer
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Copyright © 2019 GGA Corp. All rights reserved.
• This presentation and/or any related documents contains statements regarding our plans or
expectations for future features, enhancements or functionalities of current or future products
(collectively "Enhancements"). Our plans or expectations are subject to change at any time at our
discretion. Accordingly, GGA Corp. is making no representation, undertaking no commitment or legal
obligation to create, develop or license any product or Enhancements.
• The presentation, documents or any related statements are not intended to, nor shall, create any legal
obligation upon GGA Corp., and shall not be relied upon in purchasing any product. Any such
obligation shall only result from a written agreement executed by both parties.
• In addition, information disclosed in this presentation and related documents, whether oral or written,
is confidential or proprietary information of GGA Corp.. It shall be used only for the purpose of
furthering our business relationship, and shall not be disclosed to third parties.
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QIAGEN Bioinformatics Solution
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Ingenuity Variant Analysis
QIAGEN® Clinical Insight
Interpret (QCI-I)
Ingenuity Pathway
Analysis
HGMD®
Biomedical
Genomics
Workbench
&
Biomedical
Genomics Server
Solution
QIAGEN® Clinical
Insight Analyze
(QCI-A)
QIAseq FX
Library Kits
QIAseq 1-step
Amplicon KIt
QIAseq Ultralow
Input Kit
REPLI-g® Single
Cell Kits
QIAseqTargeted
RNA Panels
QIAseq DNAseq
Panels v3
QIAamp DNA kits
PAXgene RNA/DNA kits
GeneRead DNA FFPE Kit
QIAamp Circulating NA Kit
exoRNeasy Kits
RNeasy Kits
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QIAGEN BioX Product Portfolio
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By NGS Data analysis workflow
1st Analysis 2nd Analysis 3rd Analysis
From NGS sequencers
Generate NGS reads sequences
*.fastq
Reads processingQC check, Adapter TrimmingMapping (Resequencing)Variant callRNA-seq analysisEpigenetics analysisMetagenomics analysis
Annotation interpretation
Variant analysis
Pathway analysis
QIAGEN Clinical Insight - Analyze (QCI-A / QCI-AU) QCI-Interpret (QCI-I)
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Why are we using IPA?
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What is the difference?
pathwayYour
dataset
Disease
function
Relationship
With other
Observation
What are the
relationship
between each
molecules?
What do they
relate to each
other?
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What can Qiagen bioinformatics products do for you?
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QIAGEN solution: NGS 數據串接分析處理
定量並找出顯著表現的基因
生物路徑分析 (IPA)
定序資料之序列比對
找尋上游調控因子
產生可能因子假說
資料與描述型資料匯入
(CLC)
辨識定序資料內之變異資訊 (IVA)
變異位點註解以及找出潛在性生物意義變異點
CLC序列分析頁面:視覺化基因表現圖譜與變異圖譜,並能整合在同一個瀏覽頁面內
CLC製圖:多功能分析
2D 階層式分群PCA與火山圖文氏圖
IVA變異位點分析:區分變異點以及其生物、路
徑至表型關係
IPA:了解上下游基因表現對生物途徑的影響
IPA:分辨轉錄異構物與生物意義
標準化&
統計分析
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IPA
IPA application:
• Biomarker finding
• Toxicity Functions
• Diseases regulation
IPA supported platform :
• Gene expression: qPCR analysisMicroarray RNA-Seq (NGS)microRNAmRNA
• ProteomicsPhosphoProteomicsNew
• metabolomics
IPA orthologous :Arabidopsis thaliana
Bos taurus (bovine)
Caenorhabditis elegans
Gallus gallus (chicken)
Pan troglodytes (chimpanzee)
Danio rerio (zebrafish)
Canis lupus familiaris (canine)
Drosophila melanogaster
Macaca mulatta (Rhesus Monkey)
Saccharomyces cerevisiae
Schizosaccharomyces pombe
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Supported Identifiers for Data Upload
Vendor
IDsGene Protein Transcript microRNA SNP Chemical
AffymetrixEntrez Gene
(LocusLink)GenPept Ensembl
miRBase
(mature)
Affy SNP
IDs
CAS
Registry
Number
Agilent GenBank
International
Protein
Index (IPI)
RefSeqmiRBase
(stemloop)dbSNP HMDB
Life Tech
(ABI)
Symbol-
human
(HUGO/
HGNC, EG)
UniProt/
Swiss-Prot
Accession
UCSC
(hg18)KEGG
CodelinkSymbol-
mouse (EG)
UCSC
(hg19)
PubChem
CID
IlluminaSymbol- rat
(EG)
Ingenuity GI Number
UniGene
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Get more complete mapping during dataset upload!
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Peer-reviewed publications citing Ingenuity apps
>17,000 publications that used IPA -- and growing!
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Multi-Omics Analysis using IPA
Integrate and compare genomics, transcriptomics, proteomics and metabolomics data to see the big picture on your focus research
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Multi Experiment
Phosphorylation
Protein
Expression
mRNA
Expression
miRNA
Expression
+
+
+
IPA
analysis
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用IPA來發掘實驗資料中各類型關係
gene/protein/metabolite/drug
gene/protein/metabolite/drug
Biomarker
pathway Toxicity
Function
Disease
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Capturing molecular information from the literature
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Canonical Pathway
Drug
Upstream TF
Biomarker
DiseaseFunction
Downstream gene
Gather this type of information for nearly every gene. Inferences can be made from the resulting networks.
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How IPA content is different: context and direction of effect
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Third Party Database
Synonyms, Protein Family, Domains
– GO, Entrez Gene, Pfam
Tissue and Biofluid Expression & Location
– GNF, Plasma Proteome
Molecular Interactions
– BIND, DIP, MIPS, IntAct, BiogridNew, MINT, Cognia, etc.
miRNA/mRNA target databases
– TarBase, TargetScan, miRecords
Gene to Disease Associations
– OMIM, GWAS databases
Metabolomics
– HumanCycNew
Clinical Trial information
– ClinicalTrials.gov
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Gene View
Summaries
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Human & Mouse
Isoform Views
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
BioProfiler
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Diseases & Bio
Functions
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Canonical
Pathways
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Upstream Regulator
Analysis
Unique Tools for Biological Analysis and Interpretation
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Gene View
Summaries
Human & Mouse
Isoform Views
BioProfiler
Diseases & Bio
Functions
Canonical
Pathways
Upstream Regulator
Analysis
Build & Overlay
and Interactions
Build & Overlay
and Interactions
Unique Tools for Biological Analysis and Interpretation
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IsoProfiler
Multi-omics
Overlay
Comparison
Analysis
Phosphorylation
Analysis
Analysis Matchw/ OmicSoft Lands
Unique Tools for Biological Analysis and Interpretation
IsoProfiler
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IsoProfiler
Multi-omics
Overlay
Comparison
Analysis
Phosphorylation
Analysis
Analysis Matchw/ OmicSoft Lands
Unique Tools for Biological Analysis and Interpretation
Multi-omics
Overlay
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IsoProfiler
Multi-omics
Overlay
Comparison
Analysis
Phosphorylation
Analysis
Analysis Matchw/ OmicSoft Lands
Unique Tools for Biological Analysis and Interpretation
Phosphorylation
Analysis
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IsoProfiler
Multi-omics
Overlay
Comparison
Analysis
Phosphorylation
Analysis
Analysis Matchw/ OmicSoft Lands
Unique Tools for Biological Analysis and Interpretation
Comparison
Analysis
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IsoProfiler
Multi-omics
Overlay
Comparison
Analysis
Phosphorylation
Analysis
Analysis Matchw/ OmicSoft Lands
Unique Tools for Biological Analysis and Interpretation
Analysis Match*
w/ OmicSoft Lands
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Entry Points in IPA
Biological Questions
Search Experiment Data Custom Pathway
Expression arrays
Protein arrays
Mass spec
2D Gel electrophoresis Networks
Core
IPA-Tox
IPA-Biomarker
IPA-Metabolomics
Bio/Tox Functions
Diseases/Disorders
Canonical Pathways
Upstream regulators
Mechanistic/Casual Network
Interaction Network
Communicate & Collaborate
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Unprecedented discovery, together
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TCGA
GEO
Array Express
SRA
Array Suite
Curation, Processing, & QA
Journal articles
OMIM
Clinical Trials
Etc.
COSMIC
MGD
Etc.
Curation & QA
Datasets integrated into
OmicSoft LandsCurated
Findings
OncoLand
DiseaseLand
49,000+
Expression
comparison
datasets• Biological analyses of
each dataset
• Compare your analysis
to all OmicSoft analyses
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Analysis Match
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截至今日,本次更新增加了19,000組分析完成的數據至Analysis Match分析功能中,您可以在Analysis Match分析模組當中,比較您的實驗資料與外部資料庫如TCGA、LINCS等,在不同的癌症類型,疾病類型與實驗組中分析其相似及相異關係度。
並於七月中旬,會自LINCS(NIH Library of Integrated Network-Based Cellular Signatures)再添加大約28,000個分析組,總數將超過49,000個(相較於上季僅有8100+個分析組)
Your own
Dataset
Analysis
from your
dataset
Donor
Datasets
Analysis from
donor datasets
Compare
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Analysis Match
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截至今日,本次更新增加了19,000組分析完成的數據至Analysis Match分析功能中,您可以在Analysis Match分析模組當中,比較您的實驗資料與外部資料庫如TCGA、LINCS等,在不同的癌症類型,疾病類型與實驗組中分析其相似及相異關係度。
並於七月中旬,會自LINCS(NIH Library of Integrated Network-Based Cellular Signatures)再添加大約28,000個分析組,總數將超過49,000個(相較於上季僅有8100+個分析組)
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Training
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Login ID: [email protected]
Password: tsai1234
Dataset: https://goo.gl/mx2nuj
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Searching
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•Genes and chemicals
•Diseases and Functions
•Pathways and tox lists • Advanced search: Limiting results to a molecule type, family
or subcellular location
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Scales for Gene/Chemical, Disease, Pathway
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Gene
Chemical
Set of Genes and Chemicals
associated with Disease/Function
(without relationship information)
Set of Genes and Chemicals
associated with Disease/Function
(with relationship information)
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Gene View
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• Explore sample-level human tissue expression through OmicSoft Land Explorer
Now you can examine detailed expression patterns across human tissues directly
from IPA’s Isoform Views. IPA now offers access to a lite version of OmicSoft Land
Explorer. With this new feature, you can provide interactive plots of gene
expression in 51 different human tissues from the GTEx project, for both gene level
and individual splice variants. You can filter the view for a particular tissue, or filter
on metadata, such as tissue donor age or gender. You can also download the
detailed sample-level expression data for the gene.
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Gene View
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• View of human isoform-level expression in human tissue samples for FABP4
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Key Terminology
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• Finding:6.6 million
– A single piece of evidence from a literature source or database in the Ingenuity Knowledge Base
– Includes context of the fact such as experiment type, species, tissue/cell location, etc.
• Canonical Pathway (Signaling and Metabolic)
– Are generated prior to data input, based on the literature
– Do NOT change upon data input
– Do have directionality
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Key Terminology
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Networks:
Generated de novo based upon input data
Do NOT have directionality
Canonical Pathways (Signaling and Metabolic):
Are pre-built and generated prior to data input, based on the literature
Do have directionality (proceed “from A to Z”)
My Pathways and Path Designer Pathways:
Custom built pathways manually created based on user input
Relationship Type:
An interaction between two molecules in IPA (seen as a line)
Direct (physical contact) and Indirect (do NOT require physical contact)
Node
Single molecule in Network such as Gene, Chemicals, Disease, and Pathway
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Scales for Gene/Chemical, Disease, Pathway
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Gene
Chemical
Set of Genes and Chemicals
associated with Disease/Function
(without relationship information)
Set of Genes and Chemicals
associated with Disease/Function
(with relationship information)
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Build and Grow Networks of Molecules
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Grow Upstream from AKT1 to
kinases and phosphatases
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Grow
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• Using one molecule of interest as a starting point, the Grow feature allows you to find (and add) other molecules of interest to a pathway. You can also grow to functions and diseases.
B
A
C
starting point
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How Path Explorer Works
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• Calculates the “Shortest Path” between 2 molecules or 2 sets of molecules
• If 2 molecules/sets don’t have specific connections in IPA, Path Explorer will find how many and which molecules can be added to this pathway to create the shortest path
– Shortest Path (n)
– Shortest Path + 1 (n+1)
A n =1
n+1
BA
B
n =0
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Connect
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• Connect allows you to see specific molecular interactions between nodes or groups of nodes.
BA
C
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Overlay
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• Overlay tools allows you to compare and contrast different kinds of information within networks
BA
C
A
A
A
Expression Level
Disease
Drug target
Biomarker
C
C
Molecular Activity Prediction
Canonical Pathway
Lists
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Path Designer
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• Use Path Designer to transform your networks and pathways in IPA into publication quality pathway graphics rich with color, customized text and fonts, biological icons, organelles, and custom backdrops.
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Live Demo
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準備IPA分析用的Dataset
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ReplicatesAverage必須有一欄放入ID Other observations
(Comparison)
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準備IPA分析用的Dataset
重複性實驗的數值平均、p-vlaue或fold-change等統計計算,要先在IPA分析之前完成。
將實驗資料用 Excel 表格檔案儲存,檔案裡面只能有一個Sheet存在。
Excel Sheet當中必須要有一欄是列出分子的ID (如Gene Symbol, Refseq number,
Uniprot number, HMDB等常用命名皆支援)
每個Excel Sheet 最多可以放入 20個 observations (即20個實驗變因的資料欄的意思)
每個Observation可以有3個不同的表現值種類 (ex. p-Value,fold-change等)
表格欄位最上方只能有一個Head row (首行)
資料上傳到IPA後,可以在cut-off 值欄位進行設定,讓使用者決定門檻來決定表現顯著
有差異的生物分子。意味著原始實驗資料中有些分子的數值不夠顯著,可以用cut-off值
作為門檻排除於分析運算中。那些通過cut-off值的分子們在IPA中稱之為Analysis-
Ready Molecules。
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Get more complete mapping during dataset upload
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IPA can now improve your success of mapping identifiers in your datasets by evaluating
more than one column of gene or chemical IDs.
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Live Demo
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Statistical Analyses Used in IPA
z-score
• Predicts Activation or Inhibition
• Correlation between what is known (IPA Knowledge Base) and
your expression data
p-value of overlap
• Null hypothesis: No overlap between molecules from dataset and
disease/function/upstream regulator/pathway.
• Calculate using the right-tailed Fisher’s Exact Test.
• Significant p-value ≤ 0.05
Note: Benjamini-Hochberg correction for multiple testing can be
implemented in some cases
p-value of overlap
z-score
1-1 1 1 0 11 1 1
TR
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Network types in IPA
Upstream Analysis
Dataset Molecules
Upstream
Regulator
Dataset Molecules
Other upstream
regulators
Mechanistic Network of Upstream Regulators
Regulator Effect Network
Any
Diseases / functions
Interaction Network
Dataset Molecules
Function Analysis
Diseases / functions
Dataset Molecules
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IPA 分析結果
Summary: 將顯示前五名各頁籤之分析結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules,以及根據研究文獻預測它們是否是被啟動或是被抑制。
Disease&Function : 了解實驗結果在各分析疾病調控上之結果
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展,以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
Regulator effect:將上下游之調控路徑整合
Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性
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Interpret Downstream Biological Functions
Identify over-represented biological functions and predict how those
functions are increased or decreased in the experiment
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Downstream Effects Analysis介紹
Click to show bar chart
每個矩形可以經由點擊進入下一層分區: Mid-
level functional category (level 2) 與Specific functions (level 3)
方塊代表受實驗影響的生物功能與疾病,顏色可以用[Color by]指定是z-score, -log (p-value), 或是 # of genes上色。如果是用z-score上色的話,藍色區塊是預測被減低的功能,橘色則是此功能會增加。是根據實驗資料做出的演算。
63
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IPA 分析結果
Summary: 將顯示前五名各頁籤之分析結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules,以及根據研究文獻預測它們是否是被啟動或是被抑制。
Disease&Function : 了解實驗結果在各分析疾病調控上之結果
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展,以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
Regulator effect:將上下游之調控路徑整合
Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性
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Canonical Pathway Analysis
點擊特定Canonical Pathway名稱上方的Bar條,下方視窗會出現dataset 中有參與組成該 pathway的分子 ID
點擊“Open Pathway” 則可以展開那個Canonical Pathway,實驗資料中的分子會用顏色提示。
click
Canonical Pathways結果標籤:受影響的Signaling Pathway與Metabolic Pathway 依照顯著性用條狀圖排列
65
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IPA 分析結果
Summary: 將顯示前五名各頁籤之分析結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules,以及根據研究文獻預測它們是否是被啟動或是被抑制。
Disease&Function : 了解實驗結果在各分析疾病調控上之結果
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展,以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
Regulator effect:將上下游之調控路徑整合
Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
TR effect on downstream genes (Literature)
N = 8 genes
Upstream Analysis Activation z–score
Statistical measure of correlation between the transcription regulator (TR) and resulting gene expression
Actual z-score can be weighted by relationship types, relationship bias, data bias
z-score > 2 or < -2 is considered significant
1-1 1 1 0 11 1 1
Differential gene expression(Uploaded Data)
TR
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
IPA 分析結果
Summary: 將顯示前五名各頁籤之分析結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules,以及根據研究文獻預測它們是否是被啟動或是被抑制。
Disease&Function : 了解實驗結果在各分析疾病調控上之結果
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展,以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
Regulator effect:將上下游之調控路徑整合
Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
Concept of “Regulator Effects” - Spring 2014Hypotheses for how activated or inhibited upstream regulators cause downstream effects on biology
Upstream Regulators
A
Molecules in the dataset
Disease or
Function
Downstream Effects Analysis
Algorithm
First iteration
Simplest Regulator Effects result
A
Disease or
Function
Displays a relationship between the
regulator and disease/function if it exists
Causally consistent networks score higher
The algorithm runs iteratively to merge additional regulators with diseases and functions
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
Concept of “Regulator Effects”
70
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
IPA 分析結果
Summary: 將顯示前五名各頁籤之分析結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules,以及根據研究文獻預測它們是否是被啟動或是被抑制。
Disease&Function : 了解實驗結果在各分析疾病調控上之結果
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展,以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
Regulator effect:將上下游之調控路徑整合
Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
How Networks Are Generated
1. Focus molecules are “seeds”
2. Focus molecules with the most interactions to other focus molecules are then connected together to form a network
3. Non-focus molecules from the dataset are then added
4. Molecules from the Ingenuity’s Knowledge Base are added
5. Resulting Networks are scored and then sorted based on the score
72
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved. 73
Live Demo
73
Sample to Insight
Copyright© 2019 GGA Corp. All rights reserved.
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