Visualising results

After running registration function scale_and_register_data() as shown in the registering data article, users can summarise and visualise the results as illustrated in the figure below.

Get summary from registration results

The total number of registered and non-registered genes can be obtained by running function summary_model_comparison() with registration_results$model_comparison_df as an input.

Function summary_model_comparison() returns a list which contains three different objects:

# Get all of summary
all_summary <- summary_model_comparison(registration_results$model_comparison_df)

all_summary$df_summary %>%
Result Value
Total genes 10
Registered genes 10
Non-registered genes 0
Stretch 1.5, 2, 2.5, 3
Shift [-3.5, 1.5]

The list of gene accessions which were registered can be viewed by calling:

#>  [1] "BRAA04G005470.3C" "BRAA09G045310.3C" "BRAA03G051930.3C" "BRAA06G025360.3C"
#>  [5] "BRAA02G043220.3C" "BRAA03G023790.3C" "BRAA05G005370.3C" "BRAA02G018970.3C"
#>  [9] "BRAA07G030470.3C" "BRAA07G034100.3C"

Plot registration results

Function plot_registration_results() allows users to plot registration results of the genes of interest.

# Plot registration result
  ncol = 3

Users also have an option to include information or label on the plot whether particular genes are registered or not, as well as the registration parameters by include model comparison data frame as shown below.

# Plot registration result
  ncol = 3,
  sync_timepoints = TRUE

Notice that to only include same time points between samples, users can set sync_timepoints = TRUE.

Analyse similarity of expression profiles overtime before and after registering

Calculate sample distance

After registering the sample data, users can compare the overall similarity before and after registering using the function calculate_between_sample_distance().

sample_distance <- calculate_between_sample_distance(
  accession_data_ref = "Ro18"

Function calculate_between_sample_distance() returns a list of seven data frames:

Plot heatmap of sample distances

Each of these data frames above can be visualised using function plot_heatmap().

# Plot heatmap of mean expression profiles distance before scaling

# Plot heatmap of mean expression profiles distance after scaling

# Plot heatmap of mean expression profiles distance after registration process
  same_max_timepoint = TRUE, 
  same_min_timepoint = TRUE