Getting Started with grayleafspotr
11 October 2026
Contents
0.1 What is grayleafspotr?
grayleafspotr automates the quantitative phenotyping of gray leaf spot
(Magnaporthe oryzae) fungal colonies grown on petri dishes. Given a
folder of plate photographs, the package:
- segments each colony with a bundled SmallUNet deep-learning model,
- extracts area, shape, texture, and crack-coverage features for each image,
- returns a tidy data frame ready for plotting and statistical analysis.
basilisk handles Python dependencies automatically, so you do not need to
set up Python yourself. For visual quality control, plot_grayleafspot_overlay()
uses the Bioconductor EBImage package to draw the detected dish boundary, colony
outline, and crack segments directly on the source photograph, so automated
segmentation results can be checked against the original image rather than
trusted as an opaque summary statistic.
0.2 Quick start with bundled data
The package ships pre-computed results from three example images. Load them
with example_grayleafspot_results() to try the plotting functions
immediately:
run <- example_grayleafspot_results()0.2.1 View the feature table
run$results[, c("filename", "day", "area_mm2", "diameter_mm",
"circularity", "crack_coverage_pct", "qc_status")]## # A tibble: 2 × 7
## filename day area_mm2 diameter_mm circularity crack_coverage_pct qc_status
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 demo_day1… 1 12 3.9 0.8 0 pass
## 2 demo_day2… 2 18 4.78 0.77 2.5 pass0.2.2 Template plots
plot_colony_expansion(run)
plot_growth_roughness(run)
plot_stress_remodeling(run)
plot_texture_organization(run)
plot_shape_vs_stress(run)
plot_feature_heatmap(run)
0.2.3 Convert to a plain data frame
df <- as_grayleafspot_growth_data(run)
df## # A tibble: 2 × 34
## id filename day area_mm2 radius_mm diameter_mm perimeter_mm circularity
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 demo-1 demo_day… 1 12 1.95 3.9 8 0.8
## 2 demo-2 demo_day… 2 18 2.39 4.78 9.1 0.77
## # ℹ 26 more variables: eccentricity <dbl>, edge_roughness <dbl>,
## # contrast <dbl>, correlation <dbl>, energy <dbl>, homogeneity <dbl>,
## # entropy <dbl>, center_edge_delta <dbl>, density_index <dbl>, core <dbl>,
## # middle <dbl>, outer <dbl>, crack_count <dbl>, crack_length_mm <dbl>,
## # crack_coverage_pct <dbl>, proportional_crack_coverage_pct <dbl>,
## # radial_velocity_mm_per_day <dbl>, area_growth_rate_mm2_per_day <dbl>,
## # relative_growth_rate_per_day <dbl>, radial_acceleration <dbl>, …0.3 Analyze your own images
Point grayleafspot_run() at a folder of plate photographs. The pipeline
uses the basilisk-managed Python environment. The chunks below run
the real pipeline against the three bundled 06FEB test images
(inst/extdata/testdata/06FEB/) so this vignette demonstrates the full,
working code path rather than a snippet you can’t execute.
img_dir <- system.file("extdata", "testdata", "06FEB", package = "grayleafspotr")
out_dir <- file.path(tempdir(), "grayleafspotr-run-demo")
res <- grayleafspot_run(input_dir = img_dir, output_dir = out_dir, run_name = "demo")
# Per-image feature table
res$results
# Run manifest (paths, timestamp)
res$runFor plotting support, use grayleafspot_analyze() instead:
run_own <- grayleafspot_analyze(
input_dir = img_dir, output_dir = out_dir,
run_name = "demo-analyze", verbose = FALSE
)
plot_colony_expansion(run_own)0.3.1 Visualize detected regions of interest
plot_grayleafspot_overlay() draws the detected dish boundary (yellow),
colony outline (red), and crack segments (cyan) on top of the original
image, using EBImage:
image_file <- list.files(img_dir, full.names = TRUE)[1]
plot_grayleafspot_overlay(run_own, image_path = image_file)0.3.2 Reload saved results
Outputs are written to a timestamped folder and can be reloaded later:
saved_dir <- run_own$run$output_dir
reloaded <- read_grayleafspot_results(saved_dir)
plot_colony_expansion(reloaded)0.4 Next steps
See the grayleafspotr Workflow vignette for:
- a detailed description of all features extracted by the pipeline,
- a complete gallery of template plots with biological interpretation,
- guidance on image naming conventions and folder structure,
- instructions for saving publication-quality figures.
vignette("grayleafspotr-workflow", package = "grayleafspotr")Session information
sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] grayleafspotr_0.99.14 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 utf8_1.2.6 generics_0.1.4
## [4] lattice_0.23-1 hms_1.1.4 digest_0.6.39
## [7] magrittr_2.0.5 evaluate_1.0.5 grid_4.6.1
## [10] RColorBrewer_1.1-3 bookdown_0.48 fastmap_1.2.0
## [13] jsonlite_2.0.0 Matrix_1.7-6 tinytex_0.61
## [16] BiocManager_1.30.27 scales_1.4.0 jquerylib_0.1.4
## [19] cli_3.6.6 rlang_1.3.0 crayon_1.5.3
## [22] bit64_4.8.6 withr_3.0.3 cachem_1.1.0
## [25] yaml_2.3.12 otel_0.2.0 tools_4.6.1
## [28] dir.expiry_1.21.0 parallel_4.6.1 tzdb_0.5.0
## [31] dplyr_1.2.1 ggplot2_4.0.3 filelock_1.0.3
## [34] basilisk_1.25.0 reticulate_1.47.0 vctrs_0.7.3
## [37] R6_2.6.1 png_0.1-9 magick_2.9.1
## [40] lifecycle_1.0.5 bit_4.6.0 vroom_1.7.1
## [43] pkgconfig_2.0.3 pillar_1.11.1 bslib_0.12.0
## [46] gtable_0.3.6 glue_1.8.1 Rcpp_1.1.2
## [49] xfun_0.61 tibble_3.3.1 tidyselect_1.2.1
## [52] knitr_1.52 dichromat_2.0-1 farver_2.1.2
## [55] htmltools_0.5.9 labeling_0.4.3 rmarkdown_2.32
## [58] readr_2.2.0 compiler_4.6.1 S7_0.2.2