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<title>4 Session information | Predicting cell states and their variability in single-cell or spatial omics data</title>
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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>Introduction</a></li>
<li class="chapter" data-level="1" data-path="overview-of-svp.html"><a href="overview-of-svp.html"><i class="fa fa-check"></i><b>1</b> Overview of SVP</a></li>
<li class="chapter" data-level="2" data-path="quantification-of-cell-states-using-svp.html"><a href="quantification-of-cell-states-using-svp.html"><i class="fa fa-check"></i><b>2</b> Quantification of cell states using SVP</a>
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<li class="chapter" data-level="2.1" data-path="quantification-of-cell-states-using-svp.html"><a href="quantification-of-cell-states-using-svp.html#quantification-of-cancersea-etc-function"><i class="fa fa-check"></i><b>2.1</b> Quantification of CancerSEA etc function</a></li>
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<li class="chapter" data-level="2.2.2" data-path="quantification-of-cell-states-using-svp.html"><a href="quantification-of-cell-states-using-svp.html#quantification-of-cell-type-using-the-reference-single-cell-data"><i class="fa fa-check"></i><b>2.2.2</b> Quantification of cell-type using the reference single-cell data</a></li>
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<li class="chapter" data-level="3" data-path="spatial-statistical-analysis.html"><a href="spatial-statistical-analysis.html"><i class="fa fa-check"></i><b>3</b> Spatial statistical analysis</a>
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<li class="chapter" data-level="3.2.2" data-path="spatial-statistical-analysis.html"><a href="spatial-statistical-analysis.html#local-bivariate-spatial-analysis"><i class="fa fa-check"></i><b>3.2.2</b> Local bivariate spatial analysis</a></li>
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<li class="chapter" data-level="4" data-path="session-information.html"><a href="session-information.html"><i class="fa fa-check"></i><b>4</b> Session information</a></li>
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<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">Predicting cell states and their variability in single-cell or spatial omics data</a>
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<section class="normal" id="section-">
<div id="session-information" class="section level1 hasAnchor" number="4">
<h1><span class="header-section-number">4</span> Session information<a href="session-information.html#session-information" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>Here is the output of <code>sessionInfo()</code> on the system on which this document was compiled:</p>
<pre><code>#> R Under development (unstable) (2024-12-10 r87437)
#> Platform: x86_64-pc-linux-gnu
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /biostack/tools/devtools/R/devel_241212/lib64/R/lib/libRblas.so
#> LAPACK: /biostack/tools/devtools/R/devel_241212/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
#>
#> 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: Asia/Shanghai
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] scatterpie_0.2.4 magrittr_2.0.3
#> [3] ggplot2_3.5.1 SVP_0.99.0
#> [5] scran_1.35.0 scuttle_1.17.0
#> [7] ggsc_1.5.0 SpatialExperiment_1.17.0
#> [9] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
#> [11] Biobase_2.67.0 GenomicRanges_1.59.1
#> [13] GenomeInfoDb_1.43.2 IRanges_2.41.2
#> [15] S4Vectors_0.45.2 BiocGenerics_0.53.3
#> [17] generics_0.1.3 MatrixGenerics_1.19.0
#> [19] matrixStats_1.4.1 bookdown_0.41
#>
#> loaded via a namespace (and not attached):
#> [1] RcppAnnoy_0.0.22 splines_4.5.0
#> [3] later_1.4.1 ggplotify_0.1.2
#> [5] tibble_3.2.1 polyclip_1.10-7
#> [7] fastDummies_1.7.4 lifecycle_1.0.4
#> [9] edgeR_4.5.1 globals_0.16.3
#> [11] lattice_0.22-6 MASS_7.3-61
#> [13] limma_3.63.2 plotly_4.10.4
#> [15] sass_0.4.9 rmarkdown_2.29
#> [17] jquerylib_0.1.4 yaml_2.3.10
#> [19] metapod_1.15.0 httpuv_1.6.15
#> [21] Seurat_5.1.0 sctransform_0.4.1
#> [23] spam_2.11-0 sp_2.1-4
#> [25] spatstat.sparse_3.1-0 reticulate_1.40.0
#> [27] cowplot_1.1.3 pbapply_1.7-2
#> [29] RColorBrewer_1.1-3 abind_1.4-8
#> [31] zlibbioc_1.53.0 Rtsne_0.17
#> [33] purrr_1.0.2 pracma_2.4.4
#> [35] yulab.utils_0.1.8 tweenr_2.0.3
#> [37] GenomeInfoDbData_1.2.13 ggrepel_0.9.6
#> [39] irlba_2.3.5.1 listenv_0.9.1
#> [41] spatstat.utils_3.1-1 tidytree_0.4.6
#> [43] goftest_1.2-3 RSpectra_0.16-2
#> [45] spatstat.random_3.3-2 dqrng_0.4.1
#> [47] fitdistrplus_1.2-1 parallelly_1.40.1
#> [49] DelayedMatrixStats_1.29.0 leiden_0.4.3.1
#> [51] codetools_0.2-20 DelayedArray_0.33.3
#> [53] ggforce_0.4.2 tidyselect_1.2.1
#> [55] aplot_0.2.3 UCSC.utils_1.3.0
#> [57] farver_2.1.2 ScaledMatrix_1.15.0
#> [59] spatstat.explore_3.3-3 jsonlite_1.8.9
#> [61] BiocNeighbors_2.1.2 progressr_0.15.1
#> [63] ggridges_0.5.6 survival_3.7-0
#> [65] tools_4.5.0 treeio_1.31.0
#> [67] ggstar_1.0.4 ica_1.0-3
#> [69] Rcpp_1.0.13-1 glue_1.8.0
#> [71] gridExtra_2.3 SparseArray_1.7.2
#> [73] xfun_0.49 dplyr_1.1.4
#> [75] withr_3.0.2 fastmap_1.2.0
#> [77] bluster_1.17.0 fansi_1.0.6
#> [79] digest_0.6.37 rsvd_1.0.5
#> [81] gridGraphics_0.5-1 R6_2.5.1
#> [83] mime_0.12 colorspace_2.1-1
#> [85] scattermore_1.2 tensor_1.5
#> [87] spatstat.data_3.1-4 utf8_1.2.4
#> [89] tidyr_1.3.1 data.table_1.16.4
#> [91] httr_1.4.7 htmlwidgets_1.6.4
#> [93] S4Arrays_1.7.1 uwot_0.2.2
#> [95] pkgconfig_2.0.3 gtable_0.3.6
#> [97] lmtest_0.9-40 XVector_0.47.0
#> [99] htmltools_0.5.8.1 dotCall64_1.2
#> [101] SeuratObject_5.0.2 scales_1.3.0
#> [103] png_0.1-8 spatstat.univar_3.1-1
#> [105] ggfun_0.1.8 knitr_1.49
#> [107] reshape2_1.4.4 rjson_0.2.23
#> [109] nlme_3.1-166 cachem_1.1.0
#> [111] zoo_1.8-12 stringr_1.5.1
#> [113] KernSmooth_2.23-24 parallel_4.5.0
#> [115] miniUI_0.1.1.1 pillar_1.9.0
#> [117] grid_4.5.0 vctrs_0.6.5
#> [119] RANN_2.6.2 promises_1.3.2
#> [121] BiocSingular_1.23.0 tidydr_0.0.5
#> [123] beachmat_2.23.4 xtable_1.8-4
#> [125] cluster_2.1.8 evaluate_1.0.1
#> [127] magick_2.8.5 locfit_1.5-9.10
#> [129] cli_3.6.3 compiler_4.5.0
#> [131] rlang_1.1.4 crayon_1.5.3
#> [133] future.apply_1.11.3 labeling_0.4.3
#> [135] plyr_1.8.9 fs_1.6.5
#> [137] stringi_1.8.4 viridisLite_0.4.2
#> [139] deldir_2.0-4 BiocParallel_1.41.0
#> [141] munsell_0.5.1 lazyeval_0.2.2
#> [143] spatstat.geom_3.3-4 Matrix_1.7-1
#> [145] RcppHNSW_0.6.0 patchwork_1.3.0
#> [147] sparseMatrixStats_1.19.0 future_1.34.0
#> [149] statmod_1.5.0 shiny_1.9.1
#> [151] ROCR_1.0-11 igraph_2.1.2
#> [153] RcppParallel_5.1.9 bslib_0.8.0
#> [155] ggtree_3.15.0 fastmatch_1.1-4
#> [157] ape_5.8</code></pre>
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<div class="csl-entry">
Chang, Yuzhou, Jixin Liu, Yi Jiang, Anjun Ma, Yao Yu Yeo, Qi Guo, Megan McNutt, et al. 2024. <span>“Graph Fourier Transform for Spatial Omics Representation and Analyses of Complex Organs.”</span> <em>Nature Communications</em> 15 (1): 7467.
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Cortal, Akira, Loredana Martignetti, Emmanuelle Six, and Antonio Rausell. 2021. <span>“Gene Signature Extraction and Cell Identity Recognition at the Single-Cell Level with Cell-ID.”</span> <em>Nature Biotechnology</em> 39 (9): 1095–1102.
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Hao, Yuhan, Tim Stuart, Madeline H Kowalski, Saket Choudhary, Paul Hoffman, Austin Hartman, Avi Srivastava, et al. 2023. <span>“Dictionary Learning for Integrative, Multimodal and Scalable Single-Cell Analysis.”</span> <em>Nature Biotechnology</em>. <a href="https://doi.org/10.1038/s41587-023-01767-y">https://doi.org/10.1038/s41587-023-01767-y</a>.
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Miller, Brendan F, Dhananjay Bambah-Mukku, Catherine Dulac, Xiaowei Zhuang, and Jean Fan. 2021. <span>“Characterizing Spatial Gene Expression Heterogeneity in Spatially Resolved Single-Cell Transcriptomic Data with Nonuniform Cellular Densities.”</span> <em>Genome Research</em> 31 (10): 1843–55.
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Moncada, Reuben, Dalia Barkley, Florian Wagner, Marta Chiodin, Joseph C Devlin, Maayan Baron, Cristina H Hajdu, Diane M Simeone, and Itai Yanai. 2020. <span>“Integrating Microarray-Based Spatial Transcriptomics and Single-Cell RNA-Seq Reveals Tissue Architecture in Pancreatic Ductal Adenocarcinomas.”</span> <em>Nature Biotechnology</em> 38 (3): 333–42.
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