Closed GuangchuangYu closed 11 months ago
noriakis
commented
11 months ago Dear Prof. Yu,
Thank you for developing the ggsc package. I have been using the package and it is very useful. I would like to try to implement this feature in the name with something like sc_dot. If it is okay, may I make a pull request to the branch of the repository?
Sincerely,
Noriaki Sato
GuangchuangYu
commented
11 months ago @noriakis You are welcome to make a PR with your sc_dot implementation.
noriakis
commented
11 months ago @GuangchuangYu Thank you very much for your kind words. I will make a pull request when the implementation is ready.
GuangchuangYu
commented
11 months ago @noriakis I try to use sc_dot to visualize the spe object presented in https://yulab-smu.top/ggsc/visualizing-singlecellexperiment-or-spatialexperiment-objects.html and find it is not compatible. Can you figure it out?
noriakis
commented
11 months ago Dear Prof. Yu,
Thank you very much for your feedback. I have tested the codes below from the documentation, and in my environment the plot was shown as attached without errors. The sessionInfo() is attached below the codes. I may have missed something, but I would be grateful for your guidance.
Sincerely, Noriaki Sato
library(BiocParallel)
library(ggsc)
library(scater)
library(scran)
library(ggplot2)
library(STexampleData)
eh <- ExperimentHub()
myfiles <- query(eh, "STexampleData")
spe <- myfiles[["EH7538"]]
spe <- addPerCellQC(spe, subsets=list(Mito=grep("^MT-", rowData(spe)$gene_name)))
spe <- spe[, spe$in_tissue == 1]
clusters <- quickCluster(
spe,
BPPARAM = BiocParallel::MulticoreParam(workers=2),
block.BPPARAM = BiocParallel::MulticoreParam(workers=2)
)
spe <- computeSumFactors(spe, clusters = clusters, BPPARAM = BiocParallel::MulticoreParam(workers=2))
spe <- logNormCounts(spe)
dec <- modelGeneVar(spe)
top.hvgs <- getTopHVGs(dec, prop=0.15)
spe <- runPCA(spe, subset_row=top.hvgs)
output <- getClusteredPCs(reducedDim(spe), BPPARAM = BiocParallel::MulticoreParam(workers=2))
npcs <- metadata(output)$chosen
reducedDim(spe, "PCAsub") <- reducedDim(spe, "PCA")[,1:npcs,drop=FALSE]
g <- buildSNNGraph(spe, use.dimred="PCAsub", BPPARAM = MulticoreParam(workers=2))
cluster <- igraph::cluster_walktrap(g)$membership
colLabels(spe) <- factor(cluster)
set.seed(123)
spe <- runTSNE(spe, dimred="PCAsub", BPPARAM = MulticoreParam(workers=2))
genes <- c('MOBP', 'PCP4', 'SNAP25', 'HBB', 'IGKC', 'NPY')
target.features <- rownames(spe)[match(genes, rowData(spe)$gene_name)]
sc_dot(spe, target.features, slot="logcounts")sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /usr/local/package/r/4.3.0/lib64/R/lib/libRblas.so
LAPACK: /usr/local/package/r/4.3.0/lib64/R/lib/libRlapack.so; LAPACK version 3.11.0
locale:
[1] LC_CTYPE=ja_JP.UTF-8
[2] LC_NUMERIC=C
[3] LC_TIME=ja_JP.UTF-8
[4] LC_COLLATE=ja_JP.UTF-8
[5] LC_MONETARY=ja_JP.UTF-8
[6] LC_MESSAGES=ja_JP.UTF-8
[7] LC_PAPER=ja_JP.UTF-8
[8] LC_NAME=C
[9] LC_ADDRESS=C
[10] LC_TELEPHONE=C
[11] LC_MEASUREMENT=ja_JP.UTF-8
[12] LC_IDENTIFICATION=C
time zone: Asia/Tokyo
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils
[6] datasets methods base
other attached packages:
[1] STexampleData_1.8.0
[2] SpatialExperiment_1.10.0
[3] ExperimentHub_2.8.1
[4] AnnotationHub_3.8.0
[5] BiocFileCache_2.8.0
[6] dbplyr_2.3.3
[7] scran_1.28.2
[8] scater_1.28.0
[9] ggplot2_3.4.2
[10] scuttle_1.10.3
[11] SingleCellExperiment_1.22.0
[12] SummarizedExperiment_1.30.2
[13] Biobase_2.60.0
[14] GenomicRanges_1.52.1
[15] GenomeInfoDb_1.36.4
[16] IRanges_2.34.1
[17] S4Vectors_0.38.1
[18] BiocGenerics_0.46.0
[19] MatrixGenerics_1.12.3
[20] matrixStats_1.0.0
[21] ggsc_1.1.1.003
[22] testthat_3.1.10
[23] BiocParallel_1.34.2
loaded via a namespace (and not attached):
[1] fs_1.6.3
[2] spatstat.sparse_3.0-3
[3] bitops_1.0-7
[4] devtools_2.4.5
[5] httr_1.4.6
[6] RColorBrewer_1.1-3
[7] profvis_0.3.8
[8] tools_4.3.0
[9] sctransform_0.4.1
[10] utf8_1.2.3
[11] R6_2.5.1
[12] HDF5Array_1.28.1
[13] lazyeval_0.2.2
[14] uwot_0.1.16
[15] rhdf5filters_1.12.1
[16] urlchecker_1.0.1
[17] withr_2.5.0
[18] sp_2.1-1
[19] prettyunits_1.1.1
[20] gridExtra_2.3
[21] progressr_0.14.0
[22] textshaping_0.3.6
[23] cli_3.6.1
[24] spatstat.explore_3.2-5
[25] fastDummies_1.7.3
[26] labeling_0.4.2
[27] Seurat_5.0.1
[28] spatstat.data_3.0-3
[29] ggridges_0.5.4
[30] pbapply_1.7-2
[31] systemfonts_1.0.4
[32] yulab.utils_0.1.0
[33] R.utils_2.12.3
[34] parallelly_1.36.0
[35] sessioninfo_1.2.2
[36] limma_3.56.2
[37] rstudioapi_0.15.0
[38] RSQLite_2.3.3
[39] generics_0.1.3
[40] ica_1.0-3
[41] spatstat.random_3.2-1
[42] dplyr_1.1.2
[43] Matrix_1.6-3
[44] ggbeeswarm_0.7.2
[45] fansi_1.0.4
[46] abind_1.4-5
[47] R.methodsS3_1.8.2
[48] lifecycle_1.0.3
[49] yaml_2.3.7
[50] edgeR_3.42.4
[51] rhdf5_2.44.0
[52] Rtsne_0.16
[53] grid_4.3.0
[54] blob_1.2.4
[55] promises_1.2.1
[56] dqrng_0.3.1
[57] crayon_1.5.2
[58] miniUI_0.1.1.1
[59] lattice_0.21-8
[60] beachmat_2.16.0
[61] cowplot_1.1.1
[62] KEGGREST_1.40.1
[63] magick_2.8.1
[64] pillar_1.9.0
[65] metapod_1.8.0
[66] rjson_0.2.21
[67] future.apply_1.11.0
[68] codetools_0.2-19
[69] leiden_0.4.3.1
[70] glue_1.6.2
[71] ggfun_0.1.3
[72] data.table_1.14.8
[73] remotes_2.4.2.1
[74] vctrs_0.6.3
[75] png_0.1-8
[76] spam_2.10-0
[77] gtable_0.3.3
[78] cachem_1.0.8
[79] DropletUtils_1.20.0
[80] S4Arrays_1.0.5
[81] mime_0.12
[82] survival_3.5-5
[83] statmod_1.5.0
[84] bluster_1.10.0
[85] interactiveDisplayBase_1.38.0
[86] ellipsis_0.3.2
[87] fitdistrplus_1.1-11
[88] ROCR_1.0-11
[89] nlme_3.1-162
[90] usethis_2.2.2
[91] bit64_4.0.5
[92] filelock_1.0.2
[93] RcppAnnoy_0.0.21
[94] rprojroot_2.0.3
[95] irlba_2.3.5.1
[96] vipor_0.4.5
[97] KernSmooth_2.23-20
[98] colorspace_2.1-0
[99] DBI_1.1.3
[100] tidyselect_1.2.0
[101] processx_3.8.2
[102] bit_4.0.5
[103] compiler_4.3.0
[104] curl_5.0.1
[105] BiocNeighbors_1.18.0
[106] desc_1.4.2
[107] DelayedArray_0.26.7
[108] plotly_4.10.2
[109] scales_1.2.1
[110] lmtest_0.9-40
[111] callr_3.7.3
[112] rappdirs_0.3.3
[113] stringr_1.5.0
[114] digest_0.6.33
[115] goftest_1.2-3
[116] spatstat.utils_3.0-4
[117] XVector_0.40.0
[118] htmltools_0.5.6
[119] pkgconfig_2.0.3
[120] sparseMatrixStats_1.12.2
[121] fastmap_1.1.1
[122] rlang_1.1.1
[123] htmlwidgets_1.6.2
[124] shiny_1.7.4.1
[125] DelayedMatrixStats_1.22.6
[126] farver_2.1.1
[127] zoo_1.8-12
[128] jsonlite_1.8.7
[129] R.oo_1.25.0
[130] BiocSingular_1.16.0
[131] RCurl_1.98-1.13
[132] magrittr_2.0.3
[133] GenomeInfoDbData_1.2.10
[134] dotCall64_1.1-0
[135] patchwork_1.1.3
[136] Rhdf5lib_1.22.1
[137] munsell_0.5.0
[138] Rcpp_1.0.11
[139] viridis_0.6.4
[140] reticulate_1.34.0
[141] stringi_1.7.12
[142] brio_1.1.3
[143] zlibbioc_1.46.0
[144] MASS_7.3-58.4
[145] plyr_1.8.9
[146] pkgbuild_1.4.2
[147] parallel_4.3.0
[148] listenv_0.9.0
[149] ggrepel_0.9.4
[150] deldir_2.0-2
[151] Biostrings_2.68.1
[152] splines_4.3.0
[153] tensor_1.5
[154] locfit_1.5-9.8
[155] ps_1.7.5
[156] igraph_1.5.1
[157] spatstat.geom_3.2-7
[158] RcppHNSW_0.5.0
[159] reshape2_1.4.4
[160] ScaledMatrix_1.8.1
[161] pkgload_1.3.2.1
[162] BiocVersion_3.17.1
[163] SeuratObject_5.0.1
[164] RcppParallel_5.1.7
[165] BiocManager_1.30.22
[166] httpuv_1.6.11
[167] RANN_2.6.1
[168] tidyr_1.3.0
[169] purrr_1.0.2
[170] polyclip_1.10-6
[171] future_1.33.0
[172] scattermore_1.2
[173] rsvd_1.0.5
[174] xtable_1.8-4
[175] RSpectra_0.16-1
[176] tidydr_0.0.5
[177] later_1.3.1
[178] ragg_1.2.5
[179] viridisLite_0.4.2
[180] tibble_3.2.1
[181] memoise_2.0.1
[182] beeswarm_0.4.0
[183] AnnotationDbi_1.64.1
[184] cluster_2.1.4
[185] globals_0.16.2
An equivalent implementation in ggsc is still missing and it would be nice to have one.