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Plot Repeat Correction Model

plot_repeat_correction_model.Rd

Plots the results of the repeat correction model for a list of fragments.

Usage

plot_repeat_correction_model(
  fragments_list,
  batch_run_id_subset = NULL,
  n_facet_col = 1
)

Arguments

fragments_list

A list of fragments_repeats class objects obtained from the call_repeats() function when the correction = "repeat" parameter is used.

batch_run_id_subset

A character vector for a subset of batch_sample_id to plot. Or alternatively supply a number to select batch sample by position in alphabetical order.

n_facet_col

A numeric value indicating the number of columns for faceting in the plot.

Value

A base R graphic object displaying the repeat correction model results.

Details

This function makes plots for the model used to correct samples for each batch_run_id. The repeat correction algorithm assigns the user supplied repeat length to the modal peak of the sample, then pulls out a set of robust neighboring peaks to help get enough data to build an accurate linear model for the relationship between base-pair size and repeat length. So on this plot, each dot is an individual peak, with the colour indicating each sample, with the y-axis is the repeat length called from the user-supplied value in the metadata and the value assigned to each peak, with the x-axis showing the corresponding base-pair size.

Examples



fsa_list <- lapply(cell_line_fsa_list[16:19], function(x) x$clone())

find_ladders(fsa_list, show_progress_bar = FALSE)

fragments_list <- find_fragments(fsa_list, min_bp_size = 300)

test_alleles <- find_alleles(
  fragments_list 
)

add_metadata(
  fragments_list,
  metadata
)
#> Warning: The following unique ids in the metadata file do not have a corresponding sample: 20230413_A07.fsa, 20230413_A08.fsa, 20230413_A09.fsa, 20230413_C01.fsa, 20230413_C02.fsa, 20230413_C03.fsa, 20230413_D07.fsa, 20230413_D08.fsa, 20230413_D09.fsa, 20230413_F01.fsa, 20230413_F02.fsa, 20230413_F03.fsa, 20230413_G07.fsa, 20230413_G08.fsa, 20230413_G09.fsa


call_repeats(
  fragments_list = fragments_list,
  correction = "repeat"
)
#> Repeat correction model: 4 samples used to build model
#> Repeat correction model: 2.88 bp increase per repeat

# traces of bp size shows traces at different sizes
plot_repeat_correction_model(
  fragments_list,
  batch_run_id_subset = "20230414"
)