inst/helpfiles/tabPanel_DE.md

In tnaake/MatrixQCvis: Shiny-based interactive data-quality exploration for omics data

Differential expression

Currently, two methods/tests are implemented for calculating differential expression between conditions: moderated t-tests from limma and the Wald test from proDA. The approach of proDA does not require imputed values and will take the normalized+batch corrected+transformed (transformed) data set as input. If there are missing values (NAs) in the raw data set, the limma approach takes normalized+batch corrected+transformed+imputed intensities (imputed) as input, otherwise limma takes the transformed data set.

Levels

Enter here the formula for the levels. The formula has to start with a ~ (tilde) and the R-specific symbolic form: - + to add terms, - : to denote an interaction term, - * to denote factor crossing (a*b is interpreted as a+b+a:b), - - to remove the specified term, e.g. ~ x - 1 to specify no intercept, - + 0 to alternatively specify a model without intercept.

The colnames of Sample meta-data can be added as terms.

Contrasts

The colnames of the Model matrix can be used to calculate contrasts, e.g. a - b to specify the contrast between a and b.

The tabs Model matrix and Contrast matrix show the model and contrast matrix upon correct specification of the levels and contrasts. The tab Top DE shows the differential features in a tabular format, while the tab Volcano plot displays the information of log fold change (for limma)/difference (for proDA) against the p-values (displayed as -log10(p-value).

tnaake/MatrixQCvis documentation built on Nov. 4, 2024, 7:42 a.m.