When handling multiple samples, it’s possible that they exhibit a batch effect. This can be corrected using diverse methods. Giotto has the Harmony method available.
1 Setup and load example dataset
# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure Giotto Data is installed
if(!"GiottoData" %in% installed.packages()) {
pak::pkg_install("drieslab/GiottoData")
}
library(Giotto)
# Ensure the Python environment for Giotto has been installed
genv_exists <- checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment
installGiottoEnvironment()
}
# load the object
g <- GiottoData::loadGiottoMini("visium_multisample")2 Run harmony integration
g <- runGiottoHarmony(g,
vars_use = "list_ID")3 Clustering
Now that we have the integration, we can use it instead of the pca to calculate the downstream steps of the analysis.
- Calculate UMAP using the default pca
g <- runUMAP(g)
plotUMAP(g,
cell_color = "list_ID")
- Calculate UMAP using the harmony results
g <- runUMAP(g,
dim_reduction_name = "harmony",
dim_reduction_to_use = "harmony",
name = "umap_harmony")
plotUMAP(g,
dim_reduction_name = "umap_harmony",
cell_color = "list_ID")
- Calculate Nearest Network using the harmony results
g <- createNearestNetwork(gobject = g,
dim_reduction_to_use = "harmony",
dim_reduction_name = "harmony",
name = "NN.harmony",
dimensions_to_use = 1:10,
k = 15)- Calculate Leiden Clusters using the harmony results
g <- doLeidenCluster(gobject = g,
network_name = "NN.harmony",
resolution = 0.4,
n_iterations = 1000,
name = "leiden_harmony")- Plot
spatPlot2D(gobject = g,
cell_color = "leiden_harmony",
point_size = 1
)
4 Session Info
R version 4.4.2 (2024-10-31)
Platform: x86_64-apple-darwin20
Running under: macOS Sequoia 15.0.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Giotto_4.1.4 GiottoClass_0.4.4
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 viridisLite_0.4.2
[3] dplyr_1.1.4 GiottoVisuals_0.2.8
[5] fastmap_1.2.0 SingleCellExperiment_1.26.0
[7] lazyeval_0.2.2 digest_0.6.37
[9] lifecycle_1.0.4 terra_1.7-78
[11] dbscan_1.2-0 magrittr_2.0.3
[13] compiler_4.4.2 rlang_1.1.4
[15] tools_4.4.2 igraph_2.0.3
[17] utf8_1.2.4 yaml_2.3.10
[19] data.table_1.16.0 FNN_1.1.4.1
[21] knitr_1.48 S4Arrays_1.4.1
[23] htmlwidgets_1.6.4 reticulate_1.39.0
[25] DelayedArray_0.30.1 abind_1.4-8
[27] withr_3.0.1 purrr_1.0.2
[29] BiocGenerics_0.50.0 grid_4.4.2
[31] stats4_4.4.2 fansi_1.0.6
[33] colorspace_2.1-1 ggplot2_3.5.1
[35] scales_1.3.0 gtools_3.9.5
[37] SummarizedExperiment_1.34.0 cli_3.6.3
[39] rmarkdown_2.28 crayon_1.5.3
[41] generics_0.1.3 rstudioapi_0.16.0
[43] httr_1.4.7 rjson_0.2.23
[45] zlibbioc_1.50.0 parallel_4.4.2
[47] XVector_0.44.0 matrixStats_1.4.1
[49] vctrs_0.6.5 Matrix_1.7-1
[51] jsonlite_1.8.9 GiottoData_0.2.16
[53] IRanges_2.38.1 S4Vectors_0.42.1
[55] ggrepel_0.9.6 irlba_2.3.5.1
[57] scattermore_1.2 magick_2.8.5
[59] GiottoUtils_0.2.1 harmony_1.2.1
[61] plotly_4.10.4 tidyr_1.3.1
[63] glue_1.8.0 codetools_0.2-20
[65] uwot_0.2.2 cowplot_1.1.3
[67] gtable_0.3.5 GenomeInfoDb_1.40.1
[69] GenomicRanges_1.56.1 UCSC.utils_1.0.0
[71] munsell_0.5.1 tibble_3.2.1
[73] pillar_1.9.0 htmltools_0.5.8.1
[75] GenomeInfoDbData_1.2.12 R6_2.5.1
[77] evaluate_1.0.0 lattice_0.22-6
[79] Biobase_2.64.0 png_0.1-8
[81] backports_1.5.0 RhpcBLASctl_0.23-42
[83] SpatialExperiment_1.14.0 Rcpp_1.0.13
[85] SparseArray_1.4.8 checkmate_2.3.2
[87] colorRamp2_0.1.0 xfun_0.47
[89] MatrixGenerics_1.16.0 pkgconfig_2.0.3