feature_level_diagnostics {proBatch} | R Documentation

## Ploting peptide measurements

### Description

Creates a peptide faceted ggplot2 plot of the value in `measure_col` vs `order_col` (if 'NULL', x-axis is simply a sample name order). Additionally, the resulting plot can also be colored either by batch factor, by quality factor (e.g. imputated/non-imputed) and, if needed, faceted by another batch factor, e.g. an instrument. If the non-linear curve was fit, this can also be added to the plot, see functions specific to each case below

### Usage

    plot_single_feature(
      feature_name,
      df_long,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      feature_id_col = "peptide_group_label",
      geom = c("point", "line"),
      qual_col = NULL,
      qual_value = NULL,
      batch_col = "MS_batch",
      color_by_batch = FALSE,
      color_scheme = "brewer",
      order_col = "order",
      vline_color = "red",
      facet_col = NULL,
      filename = NULL,
      width = NA,
      height = NA,
      units = c("cm", "in", "mm"),
      plot_title = NULL,
      theme = "classic",
      ylimits = NULL
    )

    plot_peptides_of_one_protein(
      protein_name,
      peptide_annotation = NULL,
      protein_col = "ProteinName",
      df_long,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      feature_id_col = "peptide_group_label",
      geom = c("point", "line"),
      qual_col = NULL,
      qual_value = NULL,
      batch_col = "MS_batch",
      color_by_batch = FALSE,
      color_scheme = "brewer",
      order_col = "order",
      vline_color = "red",
      facet_col = NULL,
      filename = NULL,
      width = NA,
      height = NA,
      units = c("cm", "in", "mm"),
      plot_title = sprintf("Peptides of %s protein", protein_name),
      theme = "classic"
    )

    plot_spike_in(
      spike_ins = "BOVIN",
      peptide_annotation = NULL,
      protein_col = "ProteinName",
      df_long,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      feature_id_col = "peptide_group_label",
      geom = c("point", "line"),
      qual_col = NULL,
      qual_value = NULL,
      batch_col = "MS_batch",
      color_by_batch = FALSE,
      color_scheme = "brewer",
      order_col = "order",
      vline_color = "red",
      facet_col = NULL,
      filename = NULL,
      width = NA,
      height = NA,
      units = c("cm", "in", "mm"),
      plot_title = sprintf("Spike-in %s plots", spike_ins),
      theme = "classic"
    )

    plot_iRT(
      irt_pattern = "iRT",
      peptide_annotation = NULL,
      protein_col = "ProteinName",
      df_long,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      feature_id_col = "peptide_group_label",
      geom = c("point", "line"),
      qual_col = NULL,
      qual_value = NULL,
      batch_col = "MS_batch",
      color_by_batch = FALSE,
      color_scheme = "brewer",
      order_col = "order",
      vline_color = "red",
      facet_col = NULL,
      filename = NULL,
      width = NA,
      height = NA,
      units = c("cm", "in", "mm"),
      plot_title = "iRT peptide profile",
      theme = "classic"
    )

    plot_with_fitting_curve(
      feature_name,
      fit_df,
      fit_value_col = "fit",
      df_long,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      measure_col = "Intensity",
      feature_id_col = "peptide_group_label",
      geom = c("point", "line"),
      qual_col = NULL,
      qual_value = NULL,
      batch_col = "MS_batch",
      color_by_batch = FALSE,
      color_scheme = "brewer",
      order_col = "order",
      vline_color = "grey",
      facet_col = NULL,
      filename = NULL,
      width = NA,
      height = NA,
      units = c("cm", "in", "mm"),

        plot_title = sprintf("Fitting curve of %s \n                                                         peptide",
        paste(feature_name, collapse = " ")),
      theme = "classic"
    )

### Arguments

`feature_name` | name of the selected feature (e.g. 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). See `help("example_proteome")` for more details.
`sample_annotation` | data frame with: `sample_id_col` (this can be repeated as row names) biological covariates technical covariates (batches etc) . See `help("example_sample_annotation")`
`sample_id_col` | name of the column in `sample_annotation` table, where the filenames (colnames of the `data_matrix` are found).
`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` | whether to show the feature as points and/or connect by lines (accepted values are: 1. `point`, `line` and `c('point', 'line')`)
`qual_col` | column to color point by certain value denoted by `color_by_qual_value`. Design with inferred/requant values in OpenSWATH output data, which means argument value has to be set to `m_score`.
`qual_value` | value in `qual_col` to color. For OpenSWATH data, this argument value has to be set to `2` (this is an `m_score` value for imputed values (requant values).
`batch_col` | column in `sample_annotation` that should be used for batch comparison (or other, non-batch factor to be mapped to color in plots).
`color_by_batch` | (logical) whether to color points and connecting lines by batch factor as defined by `batch_col`.
`color_scheme` | a named vector of colors to map to `batch_col`, names corresponding to the levels of the factor. For continuous variables, vector doesn't need to be named.
`order_col` | column in `sample_annotation` that determines sample order. It is used for in initial assessment plots (plot_sample_mean_or_boxplot) and feature-level diagnostics (feature_level_diagnostics). Can be 'NULL' if sample order is irrelevant (e.g. in genomic experiments). For more details, order definition/inference, see define_sample_order and date_to_sample_order
`vline_color` | color of vertical lines, typically separating different MS batches in ordered runs; should be 'NULL' for experiments without intrinsic order
`facet_col` | column in `sample_annotation` with a batch factor to separate plots into facets; usually 2nd to `batch_col`. Most meaningful for multi-instrument MS experiments (where each instrument has its own order-associated effects (see `order_col`) or simultaneous examination of two batch factors (e.g. preparation day and measurement day). For single-instrument case should be set to 'NULL'
`filename` | path where the results are saved. If null the object is returned to the active window; otherwise, the object is save into the file. Currently only pdf and png format is supported
`width` | option determining the output image width
`height` | option determining the output image width
`units` | units: 'cm', 'in' or 'mm'
`plot_title` | title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc))
`theme` | ggplot theme, by default `classic`. Can be easily overriden
`ylimits` | range of y-axis to plot feature-level trends
`protein_name` | name of the protein as defined in `ProteinName`
`peptide_annotation` | long format data frame with peptide ID and their corresponding protein and/or gene annotations. See `help("example_peptide_annotation")`.
`protein_col` | column where protein names are specified
`spike_ins` | name of feature(s), typically proteins that were spiked in for control
`irt_pattern` | substring used to identify iRT proteins in the column 'ProteinName'
`fit_df` | data frame output of `adjust_batch_trend_df` to be plotted with the line
`fit_value_col` | column in `fit_df` where the values for fitting trend are found

### Value

ggplot2 type plot of `measure_col` vs `order_col`, faceted by `feature_name` and (optionally) by `batch_col`

### Examples

    single_feature_plot <- plot_single_feature(feature_name = "46213_NVGVSFYADKPEVTQEQK_2",
    df_long = example_proteome, example_sample_annotation,
    qual_col = NULL)

    #color measurements by factor, related to order (MS_batch)
    plot_single_feature(feature_name = "46213_NVGVSFYADKPEVTQEQK_2",
    df_long = example_proteome, example_sample_annotation,
    qual_col = NULL, color_by_batch = TRUE, batch_col = 'MS_batch')

    #color measurements by factor, with order-unrelated factor
    single_feature_plot <- plot_single_feature(feature_name = "46213_NVGVSFYADKPEVTQEQK_2",
    df_long = example_proteome, example_sample_annotation,
    qual_col = NULL, color_by_batch = TRUE, batch_col = 'Diet', geom = 'point',
    vline_color = NULL)

    #saving the plot
    ## Not run:
    single_feature_plot <- plot_single_feature(feature_name = "46213_NVGVSFYADKPEVTQEQK_2",
    df_long = example_proteome, example_sample_annotation,
    qual_col = NULL, filename = 'test_peptide.png',
    width = 28, height = 18, units = 'cm')

    ## End(Not run)

    #to examine peptides of a single protein:
    peptides_of_one_protein_plot <- plot_peptides_of_one_protein (
    protein_name = "Haao", peptide_annotation = example_peptide_annotation,
    protein_col = "Gene", df_long = example_proteome,
    sample_annotation = example_sample_annotation,
    order_col = 'order', sample_id_col = 'FullRunName',
    batch_col = 'MS_batch')

    #saving the peptides of one protein
    ## Not run:
     peptides_of_one_protein_plot <- plot_peptides_of_one_protein (
    protein_name = "Haao", peptide_annotation = example_peptide_annotation,
    protein_col = "Gene", df_long = example_proteome,
    sample_annotation = example_sample_annotation,
    order_col = 'order', sample_id_col = 'FullRunName',
    batch_col = 'MS_batch',
    filename = 'test_protein.png', width = 14, height = 9, units = 'in')
    ## End(Not run)

    #to illustrate spike-ins:
    spike_in_plot <- plot_spike_in(spike_ins = "BOVINE_A1ag",
    peptide_annotation = example_peptide_annotation, protein_col = 'Gene',
    df_long = example_proteome, sample_annotation = example_sample_annotation,
    sample_id_col = 'FullRunName',
    plot_title = "Spike-in BOVINE protein peptides")

    #to illustrate iRT peptides:
    irt_plot <- plot_iRT(irt_pattern = "iRT",
    peptide_annotation = example_peptide_annotation,
    df_long = example_proteome, sample_annotation = example_sample_annotation,
    protein_col = 'Gene')

    #illustrate the fitting curve:
    special_peptide = example_proteome$peptide_group_label == "10231_QDVDVWLWQQEGSSK_2"
    loess_fit_70 <- adjust_batch_trend_df(example_proteome[special_peptide,],
    example_sample_annotation, span = 0.7)

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

    #with curves colored by the corresponding batch:
    fitting_curve_plot <- plot_with_fitting_curve(feature_name = "10231_QDVDVWLWQQEGSSK_2",
    df_long = example_proteome, sample_annotation = example_sample_annotation,
    fit_df = loess_fit_70, plot_title = "Curve fitting with 70% span",
    color_by_batch = TRUE, batch_col = 'MS_batch')

[Package proBatch version 1.4.0 Index]