Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
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Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
Steps:
library(MatrixQCvis)
library(SummarizedExperiment)
# Assuming 'se' is a SummarizedExperiment object
se_sds <- apply(assay(se), 1, sd, na.rm = TRUE)
se <- se[!is.na(se_sds) & se_sds > 0, ]
# Start the interactive Shiny application
# qc <- shinyQC(se)
Input: A SummarizedExperiment object. Output: An interactive Shiny application that returns a processed SummarizedExperiment object upon exit.
limma) or Wald tests (proDA) directly within the Shiny interface.ShortRead or FastQC.limma or sva directly.SummarizedExperiment object where rownames(se) are feature names, colnames(se) are sample names, and colnames(se), colnames(assay(se)), and rownames(colData(se)) are all identical.SummarizedExperiment object containing the quantitative assay matrix and metadata.apply(assay(se), 1, sd, na.rm = TRUE) before launching shinyQC.colnames(se), colnames(assay(se)), and rownames(colData(se)) are identical to prevent errors.shinyQC with rownames(se) or colnames(se) set to NULL: Ensure feature and sample names are properly assigned.limma for non-interactive batch correction and differential expression.proDA for intensity-dependent probabilistic modeling of missing values in label-free proteomics.sva for ComBat-based batch effect correction.还没有评价。欢迎分享你的使用感受。
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