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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.
The total number of registered and non-registered genes can be
obtained by running the function summary() with
registration_results object as an input.
The function summary() returns a list with S3 class
summary.res_greatR containing four different objects:
summary is a data frame containing the summary of the
registration results (default S3 print).registered_genes is a vector of gene IDs which are
successfully registered.non_registered_genes is a vector of non-registered gene
IDs.reg_params is a data frame containing the distribution
of registration parameters.# Get registration summary
reg_summary <- summary(registration_results)
reg_summary$summary |>
knitr::kable()| Result | Value |
|---|---|
| Total genes | 10 |
| Registered genes | 9 |
| Non-registered genes | 1 |
| Stretch | [2.25, 4] |
| Shift | [-27.24, -4.36] |
The list of gene IDs which are registered or non-registered can be viewed by calling:
reg_summary$registered_genes
#> [1] "BRAA02G043220.3C" "BRAA03G023790.3C" "BRAA03G051930.3C" "BRAA04G005470.3C" "BRAA05G005370.3C" "BRAA06G025360.3C" "BRAA07G030470.3C" "BRAA07G034100.3C" "BRAA09G045310.3C"The function plot() allows users to plot the bivariate
distribution of the registration parameters. Non-registered genes can be
ignored by selecting type = "registered" instead of the
default type = "all". Similarly, the marginal distribution
type can be changed from type_dist = "histogram" (default)
to type_dist = "density".
The function plot() allows users to plot the
registration results of the genes of interest (by default only up to the
first 25 genes are shown, for more control over this, use the
genes_list argument).
Notice that the plot includes a label indicating if the particular genes are registered or non-registered, as well as the registration parameters in case the registration is successful.
For more details on the other function arguments, go to
plot().
After registering the data, users can compare the overall similarity
between datasets before and after registering using the function
calculate_distance(). By default all genes are considered
in this calculation, this can be changed by using the
genes_list argument.
The function calculate_distance() returns a list with S3
class dist_greatR of two data frames:
result is the distance between scaled reference and
query expressions using time points after registration.original is the distance between scaled reference and
query expressions using original time points before registration.Each of these data frames above can be visualised using the
plot() function, by selecting either
type = "result" (default) or
type = "original".
# Plot heatmap of mean expression profiles distance before registration process
plot(
sample_distance,
type = "original"
)
# Plot heatmap of mean expression profiles distance after registration process
plot(
sample_distance,
type = "result",
match_timepoints = TRUE
)
Notice that we use match_timepoints = TRUE to match the
registered query time points to the reference time points.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.