In statistical terms it peform the analysis tests the null hypothesis that, for any particular ontology term, there is no diffeerence in the proportion of genes annotated to it in the reference list and the proportion annotated to it in the test list. Researchers, in order to better understand the underlying biological processes, often want to 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 find identify classes of genes or proteins that are 'over-represented using annotations for that gene set. For istance returns all mRNA or miRNA with mean across all samples, higher than the threshold defined quantile mean across all samples. A filtered dataframe or numeric matrix where each row represents a gene, each column represents a sample.
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 Risso et al. Between-lane normalization procedures to adjust for distributional differences between lanes e. Rnaseq matrix normalized with counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit gene or the like , and one column for each sample.
It defines a square symmetric matrix of spearman correlation among samples. According this matrix and boxplot of correlation samples by samples it is possible to find samples with low correlation that can be identified as possible outliers.
It performed Kaplan-Meier survival univariate using complete follow up with all days taking one gene a time from Genelist of gene symbols. Creates a survival plot from TCGA patient clinical data using survival library. One can also use ComBat for batch correction for exploratory analysis. If batch. If no batch factor is provided, the data will be voom corrected only.
Otherwise refer to the vignettes to see how to format the documentation. User can specify which project and which tissue to query. The figure shows canonical pathways significantly overrepresented enriched by the DEGs differentially expressed genes. The most statistically significant canonical pathways identified in DEGs list are listed according to their p value corrected FDR -Log colored bars and the ratio of list genes found in each pathway over the total number of genes in that pathway Ratio, red line.
It shows in the end a network build with community of genes with similar range of pvalues from Cox regression same color and that interaction among those genes is already validated in literatures using the STRING database version 9.
An igraph object that contains a functional protein association network in human. Creates a mean methylation boxplot for groups groupCol , subgroups will be highlited as shapes if the subgroupCol was set.
Create Starburst plot for comparison of DNA methylation and gene expression. Candidate biologically significant are the genes that respect the expression logFC. A data frame with rows and 3 variables: list " ", " ", list list "samples" , list "Sample ID from TCGA barcodes, character string" , " ", " ", list list "subtype" , list "Pam50 classification, character string" , " ", " ", list list "color" , list "color, character string" , " ", " A dataset containing the Sample Ids from TCGA tumor purity measured according to 4 estimates attributes of tumor patients.
A data frame with rows and 7 variables: list " ", " ", list list "Sample. Filtered return object similar to DataPrep with genes removed after normalization and filtering process. Verify if the data is significant between two groups. For the methylation we search for probes that have a difference in the mean methylation and also a significant value. Saves in the rowRages data the columns: mean. This function is a auxiliary function to visualize GAIA ouptut all significant aberrant regions.
Using biogrid database, it will create a matrix of gene interations. If columns A and row B has value 1, it means the gene A and gene B interatcs. Get the results table from query, it can select columns with cols argument and return a number of rows using rows argument.
The dataframe returned has columnns for 'project', 'tss','participant', 'sample', "portion", "plate", and "center". Differential expression analysis DEA using edgeR or limma package. Batch correction using ComBat and Voom transformation using limma package. Barplot of subtypes and clinical info in groups of gene expression clustered.
Survival analysis with univariate Cox regression package dnet. A list of data frames with clinical data parsed from XML code in vignettes. Creates a plot for GAIA ouptut all significant aberrant regions. Link to this function GDCdownload. Link to this function GDCprepare. Link to this function GDCquery. Link to this function GeneSplitRegulon.
In this example we will download gene expression data from legacy database data aligned against genome of reference hg19 using GDC api method and we will show object data and metadata. In this example we will download gene expression quantification from harmonized database data aligned against genome of reference hg Also, it shows the object data and metadata.
This function is still under development, it is not working for all cases. See the tables below with the status. Examples of query, download, prepare can be found in this gist. Downloading and preparing data for analysis Data download: Methods differences.
If the size and the number of the files are too big this tar. To solve that we created the files. Link the complete complete code. According to this matrix we found no samples with low correlation cor.
This function does use 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 [ rissogc] and between-lane normalization procedures to adjust for distributional differences between lanes e.
Then we applied two Hierarchical cluster analysis on mRNAs after the three filters described above, the first cluster using as method ward.
D2, and the second with ConsensusClusterPlus. Finally, we will take a look in the mutation genes. In this example we will investigate the gene "ATRX". In recent years, it has been described the relationship between DNA methylation and gene expression and the study of this relationship is often difficult to accomplish. This case study will show the steps to investigate the relationship between the two types of data.
We will use this classification to do our examples. The output can be seen in a volcano plot. Note: Depending on the number of samples this function can be very slow due to the wilcoxon test, taking from hours to days. For the expression analysis, we do a DEA differential expression analysis which will give the fold change of gene expression and their significance value.
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