Regional and functional heterogeneity of hypothalamic astrocytes
Evgenii Tretiakov
Last updated: 2023-03-23
Checks: 6 1
Knit directory: 1_heteroAstrocytes/
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combined_sct <- LoadH5Seurat(here(data_dir, "comb_astrocytes.bak.h5Seurat"))skim(combined_sct@meta.data)── Data Summary ────────────────────────
Values
Name combined_sct@meta.data
Number of rows 51734
Number of columns 78
_______________________
Column type frequency:
character 38
factor 31
numeric 9
________________________
Group variables None
── Variable type: character ────────────────────────────────────────────────────
skim_variable n_missing complete_rate min max empty n_unique
1 Cell_ID 0 1 2 45 0 50286
2 Dataset 0 1 2 16 0 19
3 SRA_ID 0 1 2 11 0 94
4 Sample_ID 0 1 2 31 0 104
5 GEO_ID 0 1 2 10 0 83
6 Run10x 0 1 2 9 0 11
7 Technology 0 1 2 7 0 5
8 Strain 0 1 2 14 0 9
9 Diet 0 1 2 13 0 6
10 Pooled 0 1 2 3 0 4
11 Age 0 1 2 9 0 4
12 Author_Region 0 1 12 41 0 9
13 inferred_sex 0 1 1 2 0 4
14 Author_Exclude 0 1 2 3 0 3
15 Author_Class 0 1 2 16 0 11
16 Author_CellType 0 1 2 47 0 71
17 Phase 0 1 1 3 0 4
18 Batch_ID 0 1 14 25 0 27
19 Author_Condition 0 1 2 34 0 24
20 Sex 0 1 1 2 0 4
21 Author_Batch 0 1 2 2 0 5
22 Author_Class_Curated 0 1 2 16 0 11
23 C2 0 1 2 4 0 2
24 C7 0 1 2 4 0 2
25 C25 0 1 2 6 0 2
26 C66 0 1 2 6 0 3
27 C185 0 1 2 8 0 10
28 C286 0 1 2 8 0 19
29 C465 0 1 2 8 0 33
30 C2_named 0 1 2 17 0 2
31 C7_named 0 1 2 21 0 2
32 C25_named 0 1 2 18 0 2
33 C66_named 0 1 2 25 0 3
34 C185_named 0 1 2 38 0 10
35 C286_named 0 1 2 49 0 19
36 C465_named 0 1 2 60 0 33
37 Region_predicted 0 1 2 2 0 1
38 Region_summarized 0 1 2 2 0 1
whitespace
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
── Variable type: factor ───────────────────────────────────────────────────────
skim_variable n_missing complete_rate ordered
1 integrated_snn_res.0.01 0 1 FALSE
2 integrated_snn_res.0.0128607900386842 0 1 FALSE
3 integrated_snn_res.0.0159261774514492 0 1 FALSE
4 integrated_snn_res.0.019218924726995 0 1 FALSE
5 integrated_snn_res.0.022765300986261 0 1 FALSE
6 integrated_snn_res.0.0265957842901118 0 1 FALSE
7 integrated_snn_res.0.0307459397798481 0 1 FALSE
8 integrated_snn_res.0.0352575272462741 0 1 FALSE
9 integrated_snn_res.0.040179911243401 0 1 FALSE
10 integrated_snn_res.0.0455718747630189 0 1 FALSE
11 integrated_snn_res.0.0515039779635528 0 1 FALSE
12 integrated_snn_res.0.0580616631325161 0 1 FALSE
13 integrated_snn_res.0.0653493966487537 0 1 FALSE
14 integrated_snn_res.0.0734962758235247 0 1 FALSE
15 integrated_snn_res.0.0826637429070769 0 1 FALSE
16 integrated_snn_res.0.0930563918997702 0 1 FALSE
17 integrated_snn_res.0.104937418437845 0 1 FALSE
18 integrated_snn_res.0.118651219606498 0 1 FALSE
19 integrated_snn_res.0.13465732664678 0 1 FALSE
20 integrated_snn_res.0.153582905371072 0 1 FALSE
21 integrated_snn_res.0.176306867240956 0 1 FALSE
22 integrated_snn_res.0.204100272968758 0 1 FALSE
23 integrated_snn_res.0.238872500602171 0 1 FALSE
24 integrated_snn_res.0.283629472972472 0 1 FALSE
25 integrated_snn_res.0.34339279578797 0 1 FALSE
26 integrated_snn_res.0.427229620759052 0 1 FALSE
27 integrated_snn_res.0.553354125594305 0 1 FALSE
28 integrated_snn_res.0.764552146711845 0 1 FALSE
29 integrated_snn_res.1.19070222717945 0 1 FALSE
30 integrated_snn_res.2.5 0 1 FALSE
31 seurat_clusters 0 1 FALSE
n_unique top_counts
1 4 1: 51727, 2: 3, 3: 2, 4: 2
2 4 1: 51727, 2: 3, 3: 2, 4: 2
3 4 1: 51727, 2: 3, 3: 2, 4: 2
4 4 1: 51727, 2: 3, 3: 2, 4: 2
5 4 1: 51727, 2: 3, 3: 2, 4: 2
6 4 1: 51727, 2: 3, 3: 2, 4: 2
7 4 1: 51727, 2: 3, 3: 2, 4: 2
8 4 1: 51727, 2: 3, 3: 2, 4: 2
9 4 1: 51727, 2: 3, 3: 2, 4: 2
10 4 1: 51727, 2: 3, 3: 2, 4: 2
11 4 1: 51727, 2: 3, 3: 2, 4: 2
12 4 1: 51727, 2: 3, 3: 2, 4: 2
13 4 1: 51727, 2: 3, 3: 2, 4: 2
14 5 1: 51176, 2: 551, 3: 3, 4: 2
15 5 1: 51171, 2: 556, 3: 3, 4: 2
16 5 1: 44312, 2: 7415, 3: 3, 4: 2
17 5 1: 41420, 2: 10307, 3: 3, 4: 2
18 5 1: 44047, 2: 7680, 3: 3, 4: 2
19 6 1: 37718, 2: 10497, 3: 3512, 4: 3
20 6 1: 21686, 2: 19551, 3: 10490, 4: 3
21 6 1: 21571, 2: 19552, 3: 10604, 4: 3
22 7 1: 19522, 2: 17995, 3: 10659, 4: 3551
23 9 1: 19326, 2: 16224, 3: 9844, 4: 3554
24 9 1: 19103, 2: 16431, 3: 9813, 4: 3577
25 9 1: 19008, 2: 16385, 3: 9780, 4: 3609
26 10 1: 12766, 2: 10476, 3: 9389, 4: 8879
27 12 1: 10608, 2: 9988, 3: 8097, 4: 6133
28 17 1: 6961, 2: 6764, 3: 6305, 4: 5333
29 23 1: 6881, 2: 5347, 3: 5035, 4: 4241
30 36 1: 3681, 2: 3413, 3: 3207, 4: 2545
31 36 1: 3681, 2: 3413, 3: 3207, 4: 2545
── Variable type: numeric ──────────────────────────────────────────────────────
skim_variable n_missing complete_rate mean sd p0
1 nCount_RNA 0 1 3196. 3467. 591
2 nFeature_RNA 0 1 1557. 867. 449
3 nCount_SCT 0 1 2920. 2060. 876
4 nFeature_SCT 0 1 1501. 733. 484
5 percent_mt 0 1 4.05 2.84 0
6 percent_exclude_features 1449 0.972 0.195 0.0652 0.0117
7 S.Score 1449 0.972 -0.00939 0.0430 -0.133
8 G2M.Score 1449 0.972 -0.0206 0.0555 -0.138
9 k_tree 0 1 5.20 3.29 1
p25 p50 p75 p100 hist
1 1668 2340 3467 150220 ▇▁▁▁▁
2 980 1358 1851 11663 ▇▁▁▁▁
3 1811 2505 3278 16615 ▇▁▁▁▁
4 968 1342 1820 6664 ▇▃▁▁▁
5 1.59 3.90 6.11 15.0 ▇▇▅▁▁
6 0.144 0.197 0.236 2.54 ▇▁▁▁▁
7 -0.0392 -0.0166 0.0131 1.06 ▇▁▁▁▁
8 -0.0524 -0.0256 0.00210 1.76 ▇▁▁▁▁
9 2 5 7 17 ▇▅▃▁▁combined_sct <-
Store_Palette_Seurat(
seurat_object = combined_sct,
palette = rev(brewer.pal(n = 11, name = "Spectral")),
palette_name = "expr_Colour_Pal"
)Plot by nucleus
plEmbCombBatch <- DimPlot(combined_sct, reduction = "umap",
group.by = "Batch_ID",
label = TRUE, repel = TRUE) + NoLegend()
plEmbCombReg <- DimPlot(combined_sct, reduction = "umap",
group.by = "Author_Region",
label = TRUE, repel = TRUE) + NoLegend()
plEmbCombBatch + plEmbCombReg
p1 <- DimPlot(combined_sct, label = T, repel = T) + ggtitle("Unsupervised clustering") + NoLegend()
p2 <- DimPlot(combined_sct, label = T, repel = T, group.by = "k_tree") + ggtitle("MRTree") + NoLegend()
p1 | p2
library(schex)
combined_sct <- make_hexbin(combined_sct, nbins = 60, dimension_reduction = "UMAP")
plot_hexbin_density(combined_sct)
plot_hexbin_meta(combined_sct, col = "nCount_RNA", action = "median")
combined_sct$k_tree <- factor(combined_sct$k_tree)
plot_hexbin_meta(combined_sct, col = "k_tree", action = "majority")
label_df <- make_hexbin_label(combined_sct, col = "k_tree")
pp <- plot_hexbin_meta(combined_sct, col = "k_tree", action = "majority")
pp + ggrepel::geom_label_repel(data = label_df, aes(x = x, y = y, label = label), colour = "black",
label.size = NA, fill = NA)
reg_df <- make_hexbin_label(combined_sct,
col = "Author_Region")
pp2 <- plot_hexbin_meta(combined_sct,
col = "Author_Region",
action = "majority")
pp2 + ggrepel::geom_label_repel(data = reg_df,
aes(x = x, y = y,
label = label),
colour = "black",
label.size = NA, fill = NA)
plEmbCombCMrk <- FeaturePlot_scCustom(
combined_sct,
colors_use = combined_sct@misc$expr_Colour_Pal,
features = genes.embed[genes.embed %in%
rownames(combined_sct@assays$SCT@data)],
max.cutoff = "q99"
)
plEmbCombCMrk
genes.embed[genes.embed %in% rownames(combined_sct@assays$SCT@scale.data)] |>
map(~ plot_density(combined_sct, .x))[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
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
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] schex_1.12.0 shiny_1.7.4
[3] SingleCellExperiment_1.20.0 SummarizedExperiment_1.28.0
[5] Biobase_2.58.0 GenomicRanges_1.50.2
[7] GenomeInfoDb_1.34.9 IRanges_2.32.0
[9] S4Vectors_0.36.1 BiocGenerics_0.44.0
[11] MatrixGenerics_1.10.0 matrixStats_0.63.0
[13] mrtree_0.0.0.9000 RColorBrewer_1.1-3
[15] scCustomize_1.1.1 Nebulosa_1.8.0
[17] swne_0.6.20 patchwork_1.1.2.9000
[19] UpSetR_1.4.0 glmGamPoi_1.10.2
[21] sctransform_0.3.5 SeuratDisk_0.0.0.9020
[23] SeuratWrappers_0.3.1 SeuratObject_4.1.3
[25] Seurat_4.3.0 kableExtra_1.3.4
[27] future_1.31.0 skimr_2.1.5
[29] magrittr_2.0.3 lubridate_1.9.0
[31] timechange_0.1.1 forcats_0.5.2
[33] stringr_1.5.0 dplyr_1.1.0
[35] purrr_1.0.1 readr_2.1.3
[37] tidyr_1.3.0 tibble_3.1.8
[39] ggplot2_3.4.1 tidyverse_1.3.2.9000
[41] here_1.0.1
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-3 svglite_2.1.0
[4] lmtest_0.9-40 rprojroot_2.0.3 crayon_1.5.2
[7] MASS_7.3-58.1 nlme_3.1-161 backports_1.4.1
[10] rlang_1.0.6 XVector_0.38.0 ROCR_1.0-11
[13] irlba_2.3.5.1 data.tree_1.0.0 bit64_4.0.5
[16] glue_1.6.2 parallel_4.2.2 vipor_0.4.5
[19] spatstat.sparse_3.0-0 spatstat.geom_3.0-6 tidyselect_1.2.0
[22] liger_2.0.1 fitdistrplus_1.1-8 zoo_1.8-11
[25] xtable_1.8-4 evaluate_0.20 cli_3.6.0
[28] zlibbioc_1.44.0 rstudioapi_0.14 miniUI_0.1.1.1
[31] sp_1.6-0 bslib_0.4.2 fastmatch_1.1-3
[34] treeio_1.23.0 maps_3.4.1 xfun_0.37
[37] askpass_1.1 usedist_0.4.0 cluster_2.1.4
[40] tidygraph_1.2.2 clusterGeneration_1.3.7 expm_0.999-6
[43] SymSim_0.0.0.9000 ggrepel_0.9.2.9999 ape_5.6-2
[46] listenv_0.9.0 dendextend_1.16.0 png_0.1-8
[49] withr_2.5.0 bitops_1.0-7 ggforce_0.4.1.9000
[52] plyr_1.8.8 pracma_2.4.2 coda_0.19-4
[55] pillar_1.8.1 GlobalOptions_0.1.2 cachem_1.0.6
[58] fs_1.6.1 scatterplot3d_0.3-42 hdf5r_1.3.7
[61] paletteer_1.5.0 vctrs_0.5.2 ellipsis_0.3.2
[64] generics_0.1.3 tools_4.2.2 beeswarm_0.4.0
[67] entropy_1.3.1 munsell_0.5.0 tweenr_2.0.2
[70] proxy_0.4-27 DelayedArray_0.24.0 fastmap_1.1.0
[73] compiler_4.2.2 abind_1.4-5 httpuv_1.6.9
[76] ggimage_0.3.1 plotly_4.10.1 GenomeInfoDbData_1.2.9
[79] gridExtra_2.3 workflowr_1.7.0 lattice_0.20-45
[82] deldir_1.0-6 snow_0.4-4 utf8_1.2.3
[85] later_1.3.0 jsonlite_1.8.4 concaveman_1.1.0
[88] scales_1.2.1 tidytree_0.4.2 pbapply_1.7-0
[91] lazyeval_0.2.2 promises_1.2.0.1 R.utils_2.12.2
[94] goftest_1.2-3 spatstat.utils_3.0-1 reticulate_1.28
[97] checkmate_2.1.0 rmarkdown_2.20 cowplot_1.1.1
[100] webshot_0.5.4 Rtsne_0.16 uwot_0.1.14
[103] igraph_1.3.5 survival_3.4-0 numDeriv_2016.8-1.1
[106] yaml_2.3.7 plotrix_3.8-2 systemfonts_1.0.4
[109] htmltools_0.5.4 graphlayouts_0.8.4 quadprog_1.5-8
[112] viridisLite_0.4.1 digest_0.6.31 mime_0.12
[115] repr_1.1.4 yulab.utils_0.0.6 future.apply_1.10.0
[118] ggmin_0.0.0.9000 remotes_2.4.2 data.table_1.14.8
[121] R.oo_1.25.0 splines_4.2.2 labeling_0.4.2
[124] rematch2_2.1.2 RCurl_1.98-1.9 ks_1.14.0
[127] hms_1.1.2 colorspace_2.1-0 base64enc_0.1-3
[130] BiocManager_1.30.19 mnormt_2.1.1 ggbeeswarm_0.7.1.9000
[133] shape_1.4.6 aplot_0.1.9 ggrastr_1.0.1
[136] sass_0.4.5 Rcpp_1.0.10 mclust_6.0.0
[139] RANN_2.6.1 mvtnorm_1.1-3 circlize_0.4.15
[142] NNLM_0.4.4 fansi_1.0.4 tzdb_0.3.0
[145] parallelly_1.34.0 R6_2.5.1 grid_4.2.2
[148] ggridges_0.5.4 lifecycle_1.0.3 phytools_1.2-0
[151] leiden_0.4.3 phangorn_2.10.0 jquerylib_0.1.4
[154] snakecase_0.11.0 Matrix_1.5-3 RcppAnnoy_0.0.20
[157] spatstat.explore_3.0-6 htmlwidgets_1.6.1 umap_0.2.9.0
[160] polyclip_1.10-4 gridGraphics_0.5-1 optimParallel_1.0-2
[163] rvest_1.0.3 mgcv_1.8-41 globals_0.16.2
[166] openssl_2.0.5 spatstat.random_3.1-3 progressr_0.13.0
[169] codetools_0.2-18 FNN_1.1.3.1 RSpectra_0.16-1
[172] R.methodsS3_1.8.2 gtable_0.3.1 git2r_0.30.1
[175] ggfun_0.0.9 tensor_1.5 httr_1.4.4
[178] highr_0.10 KernSmooth_2.23-20 stringi_1.7.12
[181] vroom_1.6.0 reshape2_1.4.4 farver_2.1.1
[184] viridis_0.6.2 hexbin_1.28.2 magick_2.7.3
[187] ggtree_3.7.1.002 xml2_1.3.3 combinat_0.0-8
[190] ggplotify_0.1.0 scattermore_0.8 bit_4.0.5
[193] clustree_0.5.0 spatstat.data_3.0-0 ggraph_2.1.0.9000
[196] janitor_2.2.0.9000 pkgconfig_2.0.3 ggprism_1.0.4
[199] knitr_1.42