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Last updated: 2024-08-02
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Knit directory: KODAMA-Analysis/
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Giotto Suite
Introduction to Giotto
Giotto Suite is a collection of open-source software tools, including data structures and methods, for the comprehensive analysis and visualization of spatial multi-omics data at multiple scales and resolutions. More information can be found here.
Dataset Description
The data in this tutorial originates from a Visium Spatial Gene Expression slide of the adult mouse. This dataset is available on the 10X Genomics support site and can be downloaded using the following code.
Library loading
library(KODAMA)
library(KODAMAextra)
library(Giotto)Dataset loading
instrs = createGiottoInstructions(save_dir = '../Temporary',
save_plot = FALSE,
show_plot = TRUE,
python_path = NULL)
## directly from visium folder
visium_brain = createGiottoVisiumObject(visium_dir = "../Giotto_Mouse_brain/",
expr_data = 'raw',
png_name = 'tissue_lowres_image.png',
gene_column_index = 2,
instructions = instrs,
verbose = FALSE)
## check metadata
pDataDT(visium_brain) cell_ID in_tissue array_row array_col
<char> <int> <int> <int>
1: AAACAACGAATAGTTC-1 0 0 16
2: AAACAAGTATCTCCCA-1 1 50 102
3: AAACAATCTACTAGCA-1 1 3 43
4: AAACACCAATAACTGC-1 1 59 19
5: AAACAGAGCGACTCCT-1 1 14 94
---
4988: TTGTTTCACATCCAGG-1 1 58 42
4989: TTGTTTCATTAGTCTA-1 1 60 30
4990: TTGTTTCCATACAACT-1 1 45 27
4991: TTGTTTGTATTACACG-1 0 73 41
4992: TTGTTTGTGTAAATTC-1 1 7 51## show plot
spatPlot2D(gobject = visium_brain, cell_color = 'in_tissue', point_size = 2,
cell_color_code = c('0' = 'lightgrey', '1' = 'blue'))
| Version | Author | Date |
|---|---|---|
| 6f7daac | Stefano Cacciatore | 2024-07-19 |
###Loading and Preparing Data Create Giotto Visium Object
# Provide path to Visium data folder
data_path <- '../Giotto_Mouse_brain/'
# Create Giotto Visium object
visium_brain <- createGiottoVisiumObject(visium_dir = data_path,
expr_data = 'raw',
png_name = 'tissue_lowres_image.png',
gene_column_index = 2,
instructions = instrs)Preprocessing
## subset on spots that were covered by tissue
metadata = pDataDT(visium_brain)
in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID
visium_brain = subsetGiotto(visium_brain, cell_ids = in_tissue_barcodes)
## filter
visium_brain <- filterGiotto(gobject = visium_brain,
expression_threshold = 1,
feat_det_in_min_cells = 50,
min_det_feats_per_cell = 1000,
expression_values = c('raw'),
verbose = FALSE)Normalization and feature reduction
## normalize
visium_brain <- normalizeGiotto(gobject = visium_brain, scalefactor = 6000, verbose = FALSE)
## add gene & cell statistics
visium_brain <- addStatistics(gobject = visium_brain)
visium_brain <- calculateHVF(gobject = visium_brain)
gene_metadata = fDataDT(visium_brain)
featgenes = gene_metadata[hvf == 'yes' & perc_cells > 3 & mean_expr_det > 0.4]$feat_ID
## run PCA on expression values (default)
visium_brain <- runPCA(gobject = visium_brain, feats_to_use = featgenes)KODAMA analysis
visium_brain=RunKODAMAmatrix(visium_brain, f.par.pls = 50,FUN="PLS",n.cores=4)socket cluster with 4 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"
[1] "Calculation of dissimilarity matrix..."
================================================================================visium_brain=RunKODAMAvisualization(visium_brain,method="UMAP")Clustering and visualization
visium_brain <- createNearestNetwork(gobject = visium_brain,dim_reduction_to_use = "KODAMA", dim_reduction_name="KODAMA",dimensions_to_use = 1:2, k = 15)
## Leiden clustering
visium_brain <- doLeidenCluster(gobject = visium_brain, resolution = 0.5, n_iterations = 1000,network_name = "sNN.KODAMA")
dimPlot2D(gobject = visium_brain, dim_reduction_to_use ="KODAMA", dim_reduction_name="KODAMA",cell_color = 'leiden_clus',point_size = 2)
spatPlot2D(gobject = visium_brain,cell_color = 'leiden_clus',point_size = 2.5)
| Version | Author | Date |
|---|---|---|
| 6f7daac | Stefano Cacciatore | 2024-07-19 |
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Giotto_4.0.8 GiottoClass_0.3.1 KODAMAextra_1.0 e1071_1.7-14
[5] doParallel_1.0.17 iterators_1.0.14 foreach_1.5.2 KODAMA_3.1
[9] umap_0.2.10.0 Rtsne_0.17 minerva_1.5.10 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.16.0
[3] jsonlite_1.8.8 magrittr_2.0.3
[5] magick_2.8.4 farver_2.1.2
[7] rmarkdown_2.27 fs_1.6.4
[9] zlibbioc_1.50.0 vctrs_0.6.5
[11] GiottoUtils_0.1.9 askpass_1.2.0
[13] terra_1.7-78 htmltools_0.5.8.1
[15] S4Arrays_1.4.1 SparseArray_1.4.8
[17] parallelly_1.37.1 sass_0.4.9
[19] bslib_0.7.0 htmlwidgets_1.6.4
[21] plyr_1.8.9 plotly_4.10.4
[23] cachem_1.1.0 whisker_0.4.1
[25] igraph_2.0.3 lifecycle_1.0.4
[27] pkgconfig_2.0.3 rsvd_1.0.5
[29] Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 future_1.33.2
[33] GenomeInfoDbData_1.2.12 MatrixGenerics_1.16.0
[35] digest_0.6.36 colorspace_2.1-0
[37] S4Vectors_0.42.1 ps_1.7.7
[39] rprojroot_2.0.4 irlba_2.3.5.1
[41] RSpectra_0.16-1 GenomicRanges_1.56.1
[43] beachmat_2.20.0 labeling_0.4.3
[45] fansi_1.0.6 httr_1.4.7
[47] abind_1.4-5 compiler_4.4.1
[49] proxy_0.4-27 withr_3.0.0
[51] backports_1.5.0 BiocParallel_1.38.0
[53] highr_0.11 R.utils_2.12.3
[55] openssl_2.2.0 rappdirs_0.3.3
[57] DelayedArray_0.30.1 rjson_0.2.21
[59] gtools_3.9.5 GiottoVisuals_0.2.3
[61] tools_4.4.1 httpuv_1.6.15
[63] future.apply_1.11.2 R.oo_1.26.0
[65] glue_1.7.0 dbscan_1.2-0
[67] callr_3.7.6 promises_1.3.0
[69] grid_4.4.1 checkmate_2.3.1
[71] getPass_0.2-4 reshape2_1.4.4
[73] snow_0.4-4 generics_0.1.3
[75] gtable_0.3.5 R.methodsS3_1.8.2
[77] class_7.3-22 tidyr_1.3.1
[79] data.table_1.15.4 ScaledMatrix_1.12.0
[81] BiocSingular_1.20.0 sp_2.1-4
[83] utf8_1.2.4 XVector_0.44.0
[85] BiocGenerics_0.50.0 ggrepel_0.9.5
[87] pillar_1.9.0 stringr_1.5.1
[89] later_1.3.2 dplyr_1.1.4
[91] lattice_0.22-6 deldir_2.0-4
[93] tidyselect_1.2.1 SingleCellExperiment_1.26.0
[95] knitr_1.48 git2r_0.33.0
[97] IRanges_2.38.1 SummarizedExperiment_1.34.0
[99] scattermore_1.2 stats4_4.4.1
[101] xfun_0.45 Biobase_2.64.0
[103] matrixStats_1.3.0 stringi_1.8.4
[105] UCSC.utils_1.0.0 lazyeval_0.2.2
[107] yaml_2.3.9 evaluate_0.24.0
[109] codetools_0.2-20 tibble_3.2.1
[111] colorRamp2_0.1.0 cli_3.6.3
[113] reticulate_1.38.0 munsell_0.5.1
[115] processx_3.8.4 jquerylib_0.1.4
[117] Rcpp_1.0.12 GenomeInfoDb_1.40.1
[119] doSNOW_1.0.20 globals_0.16.3
[121] png_0.1-8 ggplot2_3.5.1
[123] listenv_0.9.1 SpatialExperiment_1.14.0
[125] viridisLite_0.4.2 scales_1.3.0
[127] purrr_1.0.2 crayon_1.5.3
[129] rlang_1.1.4 cowplot_1.1.3