Detailed explanation

For a detailed manual for this section please access these links:

  1. Clinical analysis menu manual
  2. Epigenetics analysis menu manual
  3. Transcriptomic analysis menu manual
  4. Genomic analysis menu manual

Menu: Clinical analysis

Sub-menu: Survival plot

Users can access the clinical data download in the TCGA data menu to verify the survival of different groups.

Survival plot menu: Main window.

Data

A CSV or R object (rda) file with the clinical information.

Parameters

Size control

Changes the size of the plot

Menu: Manage summarized Experiment object

To facilitate visualization and modification of the SummarizedExperiment object, we created this menu in which it is possible to visualize the three matrices of the object (assay matrix [i.e. gene expresssion values], features matrix [i.e. gene information] and sample information matrix). Also, it is possible to download the sample information matrix as a CSV file, and, after modifying it, to upload and update the SummarizedExperiment object. This might be useful if for example the user wants to compare two groups not already pre-defined.

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Tutorial Video: Managing a SummarizedExperiment object - (http://www.youtube.com/watch?v=54NBug9ycwM)

Menu: Epigenetic analysis

Sub-menu: Differential methylation analysis

The user will be able to perform a Differential methylation regions (DMR) analysis. The output will be a file with the following pattern: DMR_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv Also, the summarized Experiment will be saved with all the results inside it and the new object will be saved with _result suffix.

Obs: Depending on the number of samples and the number of probes of interest, this analysis can last anywhere from minutes to days. Duration of the analysis also depends on the type of machine and hardware on which it is run.

Differential methylation analysis menu: Main window.

Data

Select a summarized Experiment object (rda)

Parameters control

Sub-menu: Volcano plot

In this sub-menu the user will be able to plot the results from the Differentially methylated regions (DMR) analysis and the differential expression analysis (DEA).

Volcano plot menu: Main window.

Data

Expected input a CSV file with the following pattern:

Volcano options

This box will control the x-axis thresholds "Log FC threshold" for expression and "DNA methylation threshold" for DNA methylation and the y-axis thresholds "P-value adj cut-off".

Highlighting options

Checkbox option:

The option "points to highlight" can perform the following functions:

Color control

Change the color of the plot

Size control

Change the size of the plot

Other

Sub-menu: Mean DNA methylation plot

In this sub-menu the user will be able to plot the mean DNA methylation by groups.

Mean DNA methylation plot menu: Main window.

Data

Expected input is an R object (rda) file with a summarized Experiment object.

Parameters control

Size control

Change the size of the plot

Menu: Transcriptomic analysis

In this sub-menu the user will be able to perform a gene ontology enrichment analysis for the following processes: biological, cellular component, and molecular function. In addition, a network analysis for the groups of genes will be performed.

Sub-menu: Differential expression analysis

Gene expression object box

Select a summarized Experiment object (rda)

Normalization of genes

Using the TCGAanalyze_Normalization function you can normalize mRNA transcripts and miRNA, using EDASeq package. This function uses Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization [@risso2011gc] and between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization [@bullard2010evaluation].

Quantile filter of genes

DEA analysis

After the analysis is completed, the results will be saved into a CSV file. The Delta column shown in the results is: $$\delta = |log{FC}| \times |mean (group 1) - mean (group 2)|$$. Results DEA

Pathway graphs

Pathway graphs output.

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Tutorial Video: Visualizing DEA results using pathview graphs - (http://www.youtube.com/watch?v=MtEVe7_ULlQ)

Sub-menu: Heatmap plot

Heatmap plot menu: Main window.

Data

DEA result file should have the following pattern: DEA_result_groupCol_group1_group2_pcut_0.05_logFC.cut_0.csv DMR result file should have the following pattern: DMR_results_groupCol_group1_group2_pcut_0.05_meancut_0.3.csv

Genes/Probes selection

Annotation options

Other options

Size control

Change the size of the plot and the number of bars to plot

Sub-menu: Enrichment analysis

To better understand the underlying biological processes, researchers often retrieve a functional profile of a set of genes that might have an important role. This can be done by performing an enrichment analysis.

Given a set of genes that are up-regulated under certain conditions, an enrichment analysis will identify classes of genes or proteins that are over or under-represented using gene set annotations.

Enrichment analysis  menu: Main window.

Gene selection

Input a list of genes by:

Parameter control

Plot selection

Colors control

Change the color of the plot

Size control

Change the size of the plot and the number of bars to plot

Sub-menu: Network inference

Inference of gene regulatory networks. Starting with the set of differentially expressed genes, we infer gene regulatory networks using the following state-of-the art inference algorithms: ARACNE[@Mar06], CLR[@faith2007large], MRNET[@meyer2007information] and C3NET[@altay2010inferring]. These methods are based on mutual inference and use different heuristics to infer the edges in the network. These methods have been made available via Bioconductor/CRAN packages (MINET[@Meyer2008] and c3net[@altay2010inferring], respectively).

Menu: Genomic analysis

Sub-menu: Oncoprint

Using the oncoPrint function from the ComplexHeatmap package, this sub-menu offers a way to visualize multiple genomic alterations.

Oncoprint plot menu: Main window.

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Tutorial Video: Download Mutation Annotation files (MAF) and visualize through an oncoprint plot - (http://www.youtube.com/watch?v=cp1AwT8Ogmg)

Data

Gene selection

Parameters control

Menu: Classifier

Sub-menu: Glioma classifier

In this menu, users can select ther processed DNA methylation data from glioma samples and classify them into molecular subtypes as defined by Ceccareli et al. [@Cell] using a RandomForest trained model from the DNA methylation signatures available at (https://tcga-data.nci.nih.gov/docs/publications/lgggbm_2015/).

Glioma classifier menu:  Predicting molecular subtypes based on DNA methylation using data from GEO (accession GSE61160).

For a detailed manual for this section please access this link: IDAT processing and glioma classifier

References



BioinformaticsFMRP/TCGAbiolinksGUI documentation built on Aug. 10, 2021, 11:46 p.m.