For a detailed manual for this section please access these links:
Users can access the clinical data download in the TCGA data menu to verify the survival of different groups.
A CSV or R object (rda) file with the clinical information.
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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.
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.
Select a summarized Experiment object (rda)
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).
Expected input a CSV file with the following pattern:
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".
Checkbox option:
The option "points to highlight" can perform the following functions:
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In this sub-menu the user will be able to plot the mean DNA methylation by groups.
Expected input is an R object (rda) file with a summarized Experiment object.
Groups column: Select the column that will split the data into groups. This column is selected from the sample matrix (accessed with colData)
Subgroups column: Select the column that will highlight the different subgroups data in the groups.
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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.
Select a summarized Experiment object (rda)
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].
After the analysis is completed, the results will be saved into a CSV file.
The Delta column shown in the results is: $$\delta = |log{FC}| \times |mean (group 1) - mean (group 2)|$$.
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
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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.
Input a list of genes by:
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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).
Using the oncoPrint function from the ComplexHeatmap package, this sub-menu offers a way to visualize multiple genomic alterations.
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/).
For a detailed manual for this section please access this link: IDAT processing and glioma classifier
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