MatrixQCvis: shiny-based interactive data quality exploration for omics data.

ABSTRACT:

Motivation

First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality datasets and subsequent biological question-driven inference.

Results

We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standa

SUBMITTER: Naake T 

PROVIDER: S-EPMC8796383 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature