Registering data without optimisation
In greatR: Gene Registration from Expression and Time-Courses in R

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This article will show users how to register data using some specified shift and stretch parameters. This demo will use one of the genes from the sample data provided by the package.

Load sample data

greatR provides an example of data frame containing two different species A. thaliana and B. rapa with two and three different replicates, respectively. This data frame can be read as follows:

# Load the package
library(greatR)
library(data.table)

# Load a data frame from the sample data
b_rapa_data <- system.file("extdata/brapa_arabidopsis_data.csv", package = "greatR") |>
  data.table::fread()

Registering without optimisation

The illustrative table below shows the major differences between runing register() with and without optimisation.

knitr::include_graphics("figures/registration_params_table.png")

Here, we will only use a single gene with gene_id = "BRAA03G023790.3C" from the sample data, but this feature can also be used when registering multiple genes.

gene_BRAA03G023790.3C_data <- b_rapa_data[gene_id == "BRAA03G023790.3C"]

Before registering, we can use the helper function get_approximate_stretch() to approximate the stretch factor between our sample datasets.

get_approximate_stretch(
  gene_BRAA03G023790.3C_data,
  reference = "Ro18",
  query = "Col0"
)

We can now use the estimated stretch calculated above in the registration process below. Users need to set use_optimisation = FALSE to disable the automated optimisation process.

registration_results <- register(
  gene_BRAA03G023790.3C_data,
  reference = "Ro18",
  query = "Col0",
  scaling_method = "z-score",
  stretches = 2.25,
  shifts = -4.36,
  use_optimisation = FALSE
)
#> ── Validating input data ───────────────────────────────────────────────────────
#> ℹ Will process 1 gene.
#> ℹ Using estimated standard deviation, as no `exp_sd` was provided.
#> ℹ Using `scaling_method` = "z-score".
#>
#> ── Starting manual registration ────────────────────────────────────────────────
#> ℹ Using `overlapping_percent` = 50% as a registration criterion.
#> ✔ Applying registration for genes (1/1) [38ms]

To check whether the gene is registered or not, we can get the summary results by accessing the model_comparison table from the registration result.

registration_results$model_comparison |>
  knitr::kable()

As we can see, using the given stretch and shift parameter above, the B. rapa gene BRAA03G023790.3C can be registered.

Registering multiple gene with different pre-defined registration parameters

Users can also specify a list of parameters rather than a single value. Similar to the registration process above, users need to set use_optimisation = FALSE to disable the automated optimisation process.

registration_results <- register(
  gene_BRAA03G023790.3C_data,
  reference = "Ro18",
  query = "Col0",
  scaling_method = "z-score",
  stretches = seq(1, 3, 0.1),
  shifts = seq(0, 4, 0.1),
  use_optimisation = FALSE
)
#> ── Validating input data ───────────────────────────────────────────────────────
#> ℹ Will process 1 gene.
#> ℹ Using estimated standard deviation, as no `exp_sd` was provided.
#> ℹ Using `scaling_method` = "z-score".
#>
#> ── Starting manual registration ────────────────────────────────────────────────
#> ℹ Using `overlapping_percent` = 50% as a registration criterion.
#> ✔ Applying registration for genes (1/1) [1.3s]