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

Getting a summary from registration results

The total number of registered and non-registered genes can be obtained by running the function summarise_registration() with registration_results object as an input.

The function summarise_registration() returns a list containing three different objects:

  • summary is a data frame containing the summary of the registration results,
  • registered_genes is a vector of gene accessions or IDs which were successfully registered, and
  • non_registered_genes is a vector of non-registered gene accessions or IDs.
# Get registration summary
reg_summary <- summarise_registration(registration_results)

reg_summary$summary |>
Result Value
Total genes 10
Registered genes 9
Non-registered genes 1
Stretch [2.25, 4]
Shift [-6.24, 4.4]

The list of gene accessions which were registered or not registered can be viewed by calling:

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

Plotting registration results

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

# Plot registration result
  ncol = 2

Notice that the plot includes a label indicating if the particular genes are registered or not registered, as well as the registration parameters in case the registration was successful.

For more details on the other function paramaters, go to plot_registration_results().

Analysing similarity of expression profiles overtime before and after registering

Calculate sample distance

After registering the data, users can compare the overall similarity between datasets before and after registering using the function calculate_distance().

sample_distance <- calculate_distance(registration_results)

The function calculate_distance() returns a list of two data frames:

  • registered distance between scaled reference and query expressions using registered time points.
  • original distance between scaled reference and query expressions using original time points.

Plot heatmap of sample distances

Each of these data frames above can be visualised using the plot_heatmap() function, by selecting either type = "registered" or type = "original".

# Plot heatmap of mean expression profiles distance before registration process
  type = "original"

# Plot heatmap of mean expression profiles distance after registration process
  type = "registered",
  match_timepoints = TRUE

Notice that we use match_timepoints = TRUE to match the registered query time points to the reference time points.