plot_with_fitting_curve {proBatch} | R Documentation

## Plot peptide measurements across multi-step analysis

### Description

Plot Intensity of a few representative peptides for each step of the analysis including the fitting curve

### Usage

    plot_with_fitting_curve(pep_name, df_long, sample_annotation, fit_df,
      fit_value_var = "fit", order_col = "order",
      sample_id_col = "FullRunName", batch_col = "MS_batch",
      measure_col = "Intensity", feature_id_col = "peptide_group_label",
      geom = c("point", "line"), color_by_batch = FALSE,
      color_scheme = "brewer", facet_by_batch = FALSE,
      plot_title = sprintf("Fitting curve of %s peptide", pep_name),
      color_by_col = NULL, color_by_value = NULL, theme = "classic",
      vline_color = "grey", ...)

### Arguments

`pep_name` | name of the peptide for diagnostic profiling
`df_long` | data frame where each row is a single feature in a single sample. It minimally has a `sample_id_col`, a `feature_id_col` and a `measure_col`, but usually also an `m_score` (in OpenSWATH output result file)
`sample_annotation` | data matrix with: `sample_id_col` (this can be repeated as row names) biological covariates technical covariates (batches etc)
`fit_df` | data frame typically output generated from nonlinear curve fitting by `normalize_custom_fit`
`fit_value_var` | column denoting intensity values, typically fitted to curve
`order_col` | column in `sample_annotation` that determines sample order. It is used for certain diagnostics and normalisations.
`sample_id_col` | name of the column in sample_annotation file, where the filenames (colnames of the data matrix are found)
`batch_col` | column in `sample_annotation` that should be used for batch comparison
`measure_col` | if `df_long` is among the parameters, it is the column with expression/abundance/intensity; otherwise, it is used internally for consistency
`feature_id_col` | name of the column with feature/gene/peptide/protein ID used in the long format representation `df_long`. In the wide formatted representation `data_matrix` this corresponds to the row names.
`geom` | for the intensity `measure_col` profile
`color_by_batch` | (logical) whether to color points by batch
`color_scheme` | color scheme for `ggplot` representation
`facet_by_batch` | (logical) whether to plot each batch in its own facet
`plot_title` | the string indicating the source of the peptides
`color_by_col` | column to color by certain value denoted by `color_by_value`
`color_by_value` | value in `color_by_col` to color
`theme` | plot theme (default is 'classical'; other options not implemented)
`vline_color` | color of vertical lines, typically denoting different MS batches in ordered runs; should be `NULL` for experiments without intrinsic order
`...` | additional arguments to `plot_single_feature` function

### Value

`ggplot`-class plot with minimally two facets (before and after non-linear fit) with `measure_col` (Intensity) vs `order_col` (injection order) for selected peptides (specified in `pep_name`)

### See Also

Other feature-level diagnostic functions: `plot_iRT`, `plot_peptides_of_one_protein`, `plot_single_feature`, `plot_spike_in`

### Examples

    loess_fit_70 <- adjust_batch_trend(example_proteome_matrix,
    example_sample_annotation, span = 0.7)

    fitting_curve_plot <- plot_with_fitting_curve(
    pep_name = "10231_QDVDVWLWQQEGSSK_2",
    df_long = example_proteome, example_sample_annotation,
    fit_df = loess_fit_70$fit_df, plot_title = "Curve fitting with 70% span")

[Package proBatch version 1.0.0 Index]