shinyQC {MatrixQCvis} | R Documentation

## Shiny application for initial QC exploration of -omics data sets

### Description

The shiny application allows to explore -omics data sets especially with a focus on quality control. 'shinyQC' gives information on the type of samples included (if this was previously specified within the 'SummarizedExperiment' object). It gives information on the number of missing and measured values across features and across sets (e.g. quality control samples, control, and treatment groups, only displayed for 'SummarizedExperiment' objects that contain missing values). 'shinyQC' includes functionality to display (count/intensity) values across samples (to detect drifts in intensity values during the measurement), to display mean-sd plots, MA plots, ECDF plots, and distance plots between samples. 'shinyQC' includes functionality to perform dimensionality reduction (currently limited to PCA, PCoA, NMDS, tSNE, and UMAP). Additionally, it includes functionality to perform differential expression analysis (currently limited to moderated t-tests and the Wald test).

### Usage

    shinyQC(se, app_server = FALSE)

### Arguments

`se` | 'SummarizedExperiment' object (can be omitted)
`app_server` | 'logical' (set to 'TRUE' if run under a server environment)

### Details

'shinyQC' allows to subset the supplied 'SummarizedExperiment' object. On exit of the shiny application, the following objects are returned in a list: the matrix with (count/intensity) values for 'raw', 'normalized', 'transformed', 'batch corrected' (and 'imputed'). The object will only returned if 'app_server = FALSE'.

If the 'se' argument is omitted the app will load an interface that allows for data upload.

### Value

'shiny' application, 'SummarizedExperiment' upon exiting the 'shiny' application

### Author(s)

Thomas Naake

### Examples

    library(dplyr)
    library(SummarizedExperiment)

    ## create se
    set.seed(1)
    a <- matrix(rnorm(100, mean = 10, sd = 2), nrow = 10, ncol = 10,
                dimnames = list(1:10, paste("sample", 1:10)))
    a[c(1, 5, 8), 1:5] <- NA
    cD <- data.frame(name = colnames(a), type = c(rep("1", 5), rep("2", 5)))
    rD <- data.frame(spectra = rownames(a))
    se <- SummarizedExperiment(assay = a, rowData = rD, colData = cD)

    shinyQC(se)

[Package MatrixQCvis version 1.0.0 Index]