Welcome to GTBAdb

Guilt By Association Analysis

TwoGenePlot

Correlation analysis between two elements

GTBA

Gene annotation with GTBA

panGTBA

Perform GTBA with multiple tissue types

multiGTBA

Perform GTBA with multiple genes

sincorBatch

Batch correlation analysis in single tissue

pancorBatch

Batch correlation analysis in multiple tissue


Guilt By Association Analysis

Step1:first gene/gene set


Gene sets:

Gene sets were used to evaluate gene set score in tissue types by ssgsea method.
Gene set included are: Hallmark gene setsm, KEGG gene sets,Reactome Pathway gene sets, WikiPathways gene sets,GPSA gene sets

GPSA gene sets were used to evaluate gene set score in tissue types by ssGPSA method.
GPSA gene sets were derived from GPSAdb(http://guotosky.vip:13838/GPSA/).
ssGPSA is a method from GPSAdb to perform double gene set variation analysis using ssgsea method.
(ensembl id is also supported)

Step2:Second gene/gene set


(ensembl id is also supported)

Step3: Choose datasource and method


pancorPlot

Plot introduction:

Gene expression data was exracted from the selected datasource,
The the data was then splited into subtypes and correlation analysis was performed using r function cor.test()
Correlation results were then summarised into a data.frame which was shown in corTable panel.
The x axis of pancorPlot is -log10(p value) and y axis is correlation coefficient.

Plot adjustment:

Change genes, datasource, correlation methods, the plot changes accordingly and automatically.
Clikc the cog button at the up-left corner of this panel to change point label and point shape

Note:

Plots in GTBAdb can be customized if a cog button exsists at the up-left corner of that plot.
Plots in GTBAdb can be downloaded in three file types: PDF,SVG,JPEG. Choose whatever suitable
Loading...

corTable(Click rows to change)

Data calculation:

Gene expression data was exracted from the selected datasource,
The the data was then splited into subtypes and correlation analysis was performed using r function cor.test()
Correlation results were then summarised into a data.frame which was shown in this panel.

Click rows to change:

If usres are interested in correlation plot in a single subtype,
Click rows then correlation plot in that subtype will show in sincorPlot panel.

Note:

Tables in GTBAdb are clickable, feel free to try.


Loading...

sincorPlot

Plot introduction:

Gene expression data was exracted from the selected datasource and the selected subtype,
Correlation analysis was performed using r function cor.test()
The x axis of sincorPlot is the expression of first gene and and y axis is that of second gene.

Plot adjustment:

Change rug,point,line colors by clicking the cog button at the up-left corner of this plot.
Loading...

GTBA analysis


Loading...

Step1:single gene/gene set


Step2: Choose datasource and method


Step3: Choose gene sets


panGTBA analysis

GTBA analysis in multiple subtypes

Choose a single gene, and a datasource
panGTBA performs GTBA analysis across all subtypes in that datasource

Before you start:

It is ranther time-consuming to perform panGTBA analysis
It takes about 2 minutes to finish
Please Wait
Do not leave, if you really want to, TwoGenePlot panel is a place to go.
GTBAdb will take you back when panGTBA finishes.

Step1:single gene/gene set


Step2: Choose datasource and method


Step3: Choose gene sets


multiGTBA analysis

GTBA analysis with multiple genes

mutiGTBA performs GTBA analysis with multiple genes simultaneously,
It helps to explore the function similarity and difference among a list of genes,

Before you start:

It takes about 1 gene per 3 seconds to perform
Please Wait
Do not leave, watching the progress bar updates will make you happy.

Step1:genes you are interested

Paste or upload:

multiGTBA performs GTBA on multiple genes.Users can paste or upload a list of interested genes to explore their funtions and relations

For example, m6A related genes, genes from a specific gene set, TFs and their targets, miRNAs and their targets.

If you are sure what to paste or upload, click the example button to see examples, then click Run button to explore.

Note:

The number of genes should be 2 to 30
When miRNA exsists, do not choose GTEx or CCLE database

List of Names:


Upload a one column file:

Step2: Choose datasource and method


Step3: Choose gene sets


sincorBatch analysis

Batch correlation analysis in single tissue type

GTBA analysis is based on sincorBatch analysis which calculates correlation between input gene with all protein coding genes to annotate genes in a Guit By Association method
Meanwhile,sincorBatch supports correlation calculation between other types of genes as listed in step2
This helps users to explore gene functions in different relationships, like TF-targets, miRNA-targets,Pathway-members.
Note: when miRNA is chosen, either in step1 or setp2, miRNA targets prediction will be performed automatically.

Before you start:

It takes about 15 seconds to finish
Please Wait
Do not leave, it's not worth it.

Step1:single gene/gene set


Step2:choose correlation gene type

Protein coding genes:

Genes defined as protein coding genes in GTF

miRNA:

miRNAs detected in TCGA project.

Transcription Factor:

Gene defined as TFs are listed here:
http://bioinfo.life.hust.edu.cn/HumanTFDB/#!/download

Noncoding genes:

Genes defined as none coding genes in GTF

Gene sets:

Gene sets were used to evaluate gene set score in tissue types by ssgsea method.
Gene set included are: Hallmark gene setsm, KEGG gene sets,Reactome Pathway gene sets, WikiPathways gene sets,Immune infiltration gene sets.

GPSA double gene sets:

GPSA gene sets were used to evaluate gene set score in tissue types by ssGPSA method.
GPSA gene sets were derived from GPSAdb(http://guotosky.vip:13838/GPSA/).
ssGPSA is a method from GPSAdb to perform double gene set variation analysis using ssgsea method.

Step3: Choose datasource and method


pancorBatch analysis

Perform sincorBatch analysis in multiple subtypes

While sincorBatch analysis finds which gene correlates with the input gene most in single tissue type
pancorBatch analysis finds which gene correlates most in pantissue type.

Before you start:

It takes about 60 seconds to finish
Please Wait
Do not leave. If you do, it will still switch to pancorBatch panel when it finishes.

Step1:single gene/gene set


Step2:choose correlation gene type

Protein coding genes:

Genes defined as protein coding genes in GTF

miRNA:

miRNAs detected in TCGA project.

Transcription Factor:

Gene defined as TFs as listed here:
http://bioinfo.life.hust.edu.cn/HumanTFDB/#!/download

Noncoding genes:

Genes defined as none coding genes in GTF

Gene sets:

Gene sets were used to evaluate gene set score in tissue types by ssgsea method.
Gene set included are: Hallmark gene setsm, KEGG gene sets,Reactome Pathway gene sets, WikiPathways gene sets,Immune infiltration gene sets.

GPSA double gene sets:

GPSA gene sets were used to evaluate gene set score in tissue types by ssGPSA method.
GPSA gene sets were derived from GPSAdb(http://guotosky.vip:13838/GPSA/).
ssGPSA is a method from GPSAdb to perform double gene set variation analysis using ssgsea method.

Step3: Choose datasource and method