feature_level_diagnostics: Ploting peptide measurements
In proBatch: Tools for Diagnostics and Corrections of Batch Effects in Proteomics

Description Usage Arguments Value Examples

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

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"
)

`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

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

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')