This function creates a quality control (QC) plot for a `SummarizedExperiment` object, highlighting samples that pass or fail specified QC thresholds.
Usage
plot_qc_filters(
gexp,
fname = "results/qc/plot_QC_filters.pdf",
min_nfeats = 5000,
min_ncounts = 5e+06,
out = c("plotly", "ggplot")[1]
)Arguments
- gexp
A `SummarizedExperiment` object containing gene expression data. The `colData` must include `nfeats` (number of detected features) and `ncounts` (total counts per sample).
- fname
A character string specifying the file path to save the plot as a PDF. Default is `"results/qc/plot_QC_filters.pdf"`. Set to `NULL` to skip saving.
- min_nfeats
An integer specifying the minimum number of features for a sample to pass QC. Default is `5000`.
- min_ncounts
A numeric value specifying the minimum total counts for a sample to pass QC. Default is `5e6`.
- out
A character string indicating the output type: `"plotly"` (interactive Plotly plot) or `"ggplot"` (static ggplot). Default is `"plotly"`.
Value
A QC plot, either as a `plotly` interactive object or a `ggplot` static object, depending on the `out` parameter.
Details
The function identifies samples that fail QC based on thresholds for the number of detected features (`min_nfeats`) and total counts (`min_ncounts`). It creates a scatter plot with: - The x-axis representing the number of features (`nfeats`). - The y-axis representing the total counts (`ncounts`). - Points color-coded based on QC status (`pass` or `fail`). - Dashed lines indicating the QC thresholds.
If `out = "plotly"`, the plot is interactive, allowing for sample tooltips. If a file name (`fname`) is provided, the static ggplot is saved as a PDF.