The goal of greatR (Gene
Registration from Expression
and Time-courses in
R) to register (align) gene expression profiles between
two species (reference data and data to transform). Non-reference gene
expression profiles will be stretched and shifted. The optimality of
registration parameters (shifts and stretches) will be estimated using
least-squares criterion. This package is also designed to compare a
registration model versus a non-registration model, as well as determine
whether registration model performed better than non-registration
transformation.
Flowchart below illustrates the workflow of the package given an input data:
More details on how to use this package are available on function documentations and vignettes:
You can install the released version of greatR from CRAN with:
install.packages("greatR")And the development version of greatR from GitHub with:
# install.packages("devtools")
devtools::install_github("ruthkr/greatR")This is a basic example which shows you how to register (align) gene expression profiles over time:
# Load the package
library(greatR)# Load a data frame from the sample data
all_data_df <- system.file("extdata/brapa_arabidopsis_all_replicates.csv", package = "greatR") %>%
utils::read.csv()
# Running the registration
registration_results <- scale_and_register_data(
input_df = all_data_df,
stretches = c(3, 2.5, 2, 1.5, 1),
shifts = seq(-4, 4, length.out = 33),
min_num_overlapping_points = 4,
initial_rescale = FALSE,
do_rescale = TRUE,
accession_data_to_transform = "Col0",
accession_data_ref = "Ro18",
start_timepoint = "reference"
)
#>
#> ── Information before registration ─────────────────────────────────────────────
#> ℹ Max value of expression_value of all_data_df: 262.28
#>
#> ── Analysing models for all stretch and shift factor ───────────────────────────
#>
#> ── Analysing models for stretch factor = 3 ──
#> ✓ Calculating score for all shifts (10/10) [2.6s]
#> ✓ Normalising expression by mean and sd of compared values (10/10) [85ms]
#> ✓ Applying best shift (10/10) [91ms]
#> ✓ Calculating registration vs non-registration comparison BIC (10/10) [140ms]
#> ✓ Finished analysing models for stretch factor = 3
#>
#> ── Analysing models for stretch factor = 2.5 ──
#> ✓ Calculating score for all shifts (10/10) [2.8s]
#> ✓ Normalising expression by mean and sd of compared values (10/10) [81ms]
#> ✓ Applying best shift (10/10) [103ms]
#> ✓ Calculating registration vs non-registration comparison BIC (10/10) [160ms]
#> ✓ Finished analysing models for stretch factor = 2.5
#>
#> ── Analysing models for stretch factor = 2 ──
#> ✓ Calculating score for all shifts (10/10) [2.9s]
#> ✓ Normalising expression by mean and sd of compared values (10/10) [95ms]
#> ✓ Applying best shift (10/10) [82ms]
#> ✓ Calculating registration vs non-registration comparison BIC (10/10) [164ms]
#> ✓ Finished analysing models for stretch factor = 2
#>
#> ── Analysing models for stretch factor = 1.5 ──
#> ✓ Calculating score for all shifts (10/10) [3s]
#> ✓ Normalising expression by mean and sd of compared values (10/10) [107ms]
#> ✓ Applying best shift (10/10) [84ms]
#> ✓ Calculating registration vs non-registration comparison BIC (10/10) [171ms]
#> ✓ Finished analysing models for stretch factor = 1.5
#>
#> ── Analysing models for stretch factor = 1 ──
#> ✓ Calculating score for all shifts (10/10) [2.7s]
#> ✓ Normalising expression by mean and sd of compared values (10/10) [85ms]
#> ✓ Applying best shift (10/10) [90ms]
#> ✓ Calculating registration vs non-registration comparison BIC (10/10) [154ms]
#> ✓ Finished analysing models for stretch factor = 1
#>
#> ── Model comparison results ────────────────────────────────────────────────────
#> ℹ BIC finds registration better than non-registration for: 10/10
#>
#> ── Applying the best-shifts and stretches to gene expression ───────────────────
#> ✓ Normalising expression by mean and sd of compared values (10/10) [85ms]
#> ✓ Applying best shift (10/10) [98ms]
#> ℹ Max value of expression_value: 9.05
#> ✓ Imputing transformed expression values (10/10) [209ms]
#> Calderwood, A., Hepworth, J., Woodhouse, … Morris, R. (2021). Comparative transcriptomics reveals desynchronisation of gene expression during the floral transition between Arabidopsis and Brassica rapa cultivars. Quantitative Plant Biology, 2, E4. doi:10.1017/qpb.2021.6