Correlation between technical replicates

This function generates scatter plots to visualize the correlation between a given pair of technical replicates (Eg: 1 vs 2) for each sample.

Usage

corr_plot(
  raw_df,
  rep_1,
  rep_2,
  save = FALSE,
  file_type = "pdf",
  palette = "viridis",
  text_size = 5,
  n_row = 4,
  n_col = 4,
  dpi = 80,
  file_path = NULL
)

Arguments

raw_df

A raw_df object (output of create_df) containing technical replicates.

rep_1

Numerical. Technical replicate number.

rep_2

Numerical. Number of the second technical replicate to compare to rep1.

save

Logical. If TRUE saves a copy of the plot in the directory provided in file_path.

file_type

File type to save the scatter plots. Default is "pdf".

palette

Viridis color palette option for plots. Default is "viridis". See viridis for available options.

text_size

Text size for plot labels, axis labels etc. Default is 10.

n_row

Numerical. Number of plots to print in a row in a single page. Default is 4.

n_col

Numerical. Number of plots to print in a column in a single page. Default is 4.

dpi

Plot resolution. Default is 80.

file_path

A string containing the directory path to save the file.

Value

A list of ggplot2 plot objects.

Details

• Note: n_row * n_col should be equal to the number of samples to display in a single page.

See also

create_df

Author

Chathurani Ranathunge

Examples

## Use a data set containing technical replicates to create a raw_df object
raw_df <- create_df(
prot_groups = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg2.txt",
exp_design = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed2.txt",
tech_reps = TRUE
)
#> 0 empty row(s) removed.
#> 1 empty column(s) removed.
#> 12 reverse protein(s) (rows) removed.
#> 29 protein contaminant(s) (rows) removed.
#> 188 protein(s) identified by 2 or fewer unique peptides removed.
#> Zeros have been replaced with NAs.
#> Data have been log-transformed.


# \donttest{
## Compare technical replicates 1 vs. 2 for all samples
corr_plot(raw_df, rep_1 = 1, rep_2 = 2)
#> Warning: Removed 285 rows containing missing values (`geom_point()`).
#> Warning: Removed 285 rows containing missing values (`geom_text()`).
#> Warning: Removed 302 rows containing missing values (`geom_point()`).
#> Warning: Removed 302 rows containing missing values (`geom_text()`).
#> Warning: Removed 259 rows containing missing values (`geom_point()`).
#> Warning: Removed 259 rows containing missing values (`geom_text()`).
#> Warning: Removed 352 rows containing missing values (`geom_point()`).
#> Warning: Removed 352 rows containing missing values (`geom_text()`).
#> Warning: Removed 249 rows containing missing values (`geom_point()`).
#> Warning: Removed 249 rows containing missing values (`geom_text()`).
#> Warning: Removed 303 rows containing missing values (`geom_point()`).
#> Warning: Removed 303 rows containing missing values (`geom_text()`).

# }