Help Pages

1. PCA Plot

The PCA plot tab displays the biplot by default. You can select the principle component to plot on the x and y axis of the plot from the drop-down menu. You can also specify the number of top genes showing maximum variance to be used as the input for the bioplot as well as the number of genes you want to view in the plot. The Display variances of PC tab displays the barplot showing the proportion of variance retained by each principle component. The 3D plot tab displays the 3D plot of the top 3 principle components

2. Project Summary and Results

The Project Summary and Results tab, for the selected project and comparison, will display the limma (differential expression analysis) output for that comparison in a table. Clicking on any row will display the dot plot for that gene. The dot-plot can display the data by any category selected from the options provided. The results can be filtered from the options provided in the Controls box and downloaded as a csv file.

Note:Make sure the radio button 'None' is not selected when setting FC and P.Value cutoffs

The Volcano plot subtab, as the name suggests, displays the volcano plot.The 'View Limma results of multiple contrasts' subtab displays a table of just the foldchange and adjusted pvalues of multiple comparisons.

3. Raw Expression Data

The Raw expression data tab displays the log-normalized expression data from voom

4. Sample Data

The Sample data tab has all the sample information from the RNA-Seq Experiment

5. Heatmap

The Heatmap tab displays, by default, an interactive heatmap of the top 50 differentially expressed genes in the selected comparison, plotted using d3heatmap package. This tab provides additional plotting functionalities like number of genes to plot, changing color palettes and clustering options. In addition to the default, users can also plot a heatmap by uploading a genelist

6. GSEA using Camera

The Camera function in the limma package tests whether a set of genes is highly ranked relative to other genes in terms of differential expression. It takes into account the inter-gene correlation.CAMERA, an acronym for Correlation Adjusted MEan RAnk gene set test, is based on the idea of estimating the variance inflation factor associated with inter-gene correlation, and incorporating this into parametric or rank-based test procedures. It returns the number of genes in the set, the inter-gene correlation value, the direction of change (Up or Down), the two-tailed p-value and the Benjamini & Hochberg FDR adjusted P-value

7. Pathway Analysis using SPIA

The Pathway Analysis using SPIA tab displays the results from the Bioconductor package SPIA (Signaling Pathway Impact Analysis) that uses the information from a set of differentially expressed genes and their fold changes to find the significant pathways from the KEGG database for the selected condition

8. Pathway Analysis using ReactomePA

ReactomePA is a Bioconductor package that performs pathway enrichment analysis. This tab provides plenty of enrichment visualization options in form of barplot, dotplot and enrichment map. The analysis results are based on the differentially expressed set of genes.

9. Gene Ontology using GAGE

GAGE performs GSEA and GO Analysis on the data. The default Ontology is set to Biological process.