Mc4r in postnatal mouse PVN
Evgenii Tretiakov
2021-05-14
Last updated: 2021-05-14
Checks: 6 1
Knit directory: mc4r/
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Published subset of adult hypothalamic data (Nature + Cell datasets)
The following `from` values were not present in `x`: 26, 31, 43
Step back to see stratification by sequencing samples
PVN neurons?
What we showed could be even better
PVN neurons
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
MC-Rs genes
Warning: Could not find Mc1r in the default search locations, found in RNA assay
insteadWarning: Could not find Mc2r in the default search locations, found in RNA assay
insteadWarning: Could not find Mc3r in the default search locations, found in RNA assay
insteadWarning in FeaturePlot(rar2020.srt.pvn, features = mcr_genes, pt.size = 0.7, :
All cells have the same value (0) of RNA_Mc2r.
Differential Gene Expression (DGE) test of published groups
Idents(rar2020.srt.pvn) <- "ident"
all_markers_pvn_wtree_final %>%
group_by(cluster) %>%
filter(p_val_adj < 0.01) %>%
slice_max(n = 7, order_by = avg_log2FC) %>%
print(., n = 35)# A tibble: 35 x 7
# Groups: cluster [5]
p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
<dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 2.76e-22 6.25 0.972 0.572 3.65e-18 mneOXY Oxt
2 1.28e-28 2.73 0.917 0.181 1.69e-24 mneOXY Gm28928
3 1.50e-15 2.17 0.917 0.446 1.98e-11 mneOXY Fam19a1
4 3.67e-12 2.03 0.944 0.612 4.86e- 8 mneOXY Gpc5
5 2.11e-24 1.88 1 0.27 2.79e-20 mneOXY Foxp2
6 1.82e-31 1.87 0.806 0.1 2.41e-27 mneOXY S100a10
7 7.47e-18 1.50 1 0.42 9.87e-14 mneOXY A830018L16Rik
8 7.19e-40 6.63 0.978 0.768 9.50e-36 mneVAS Avp
9 7.62e-41 2.48 1 0.732 1.01e-36 mneVAS Pde4b
10 4.03e-33 2.12 1 0.829 5.33e-29 mneVAS Galntl6
11 5.66e-22 1.84 0.888 0.521 7.48e-18 mneVAS Zfp804a
12 1.22e-15 1.68 0.685 0.302 1.61e-11 mneVAS Gal
13 4.35e-21 1.64 0.966 0.591 5.75e-17 mneVAS Zfp804b
14 4.31e-43 1.62 0.91 0.165 5.70e-39 mneVAS Stxbp6
15 1.53e-33 2.39 0.462 0.014 2.02e-29 pneCRH Crh
16 3.90e-25 1.65 0.877 0.369 5.16e-21 pneCRH Nr3c2
17 6.31e-16 1.39 0.846 0.511 8.35e-12 pneCRH Fmnl2
18 1.65e-15 1.34 1 0.759 2.19e-11 pneCRH Nrxn3
19 1.76e-14 1.32 0.908 0.591 2.33e-10 pneCRH Gpc5
20 1.93e-24 1.15 0.554 0.077 2.55e-20 pneCRH Zbtb16
21 1.58e-15 1.05 0.431 0.08 2.09e-11 pneCRH Ppp1r17
22 8.85e-38 5.55 1 0.198 1.17e-33 pneSS Sst
23 1.22e-19 2.38 1 0.557 1.61e-15 pneSS Trpm3
24 2.62e-17 2.07 0.974 0.509 3.46e-13 pneSS Sorcs1
25 1.29e-15 1.99 1 0.673 1.70e-11 pneSS Ghr
26 2.92e-14 1.77 0.842 0.359 3.87e-10 pneSS Alk
27 1.37e-15 1.75 0.868 0.359 1.81e-11 pneSS Col25a1
28 9.32e- 8 1.50 0.684 0.369 1.23e- 3 pneSS Cntn3
29 3.21e-42 2.38 0.968 0.689 4.25e-38 pneTRH Lingo2
30 8.87e-43 1.98 0.947 0.57 1.17e-38 pneTRH March1
31 2.97e-43 1.95 0.64 0.013 3.93e-39 pneTRH Cbln2
32 3.98e-39 1.95 0.884 0.456 5.26e-35 pneTRH Nav3
33 2.07e- 8 1.88 0.561 0.382 2.74e- 4 pneTRH Il1rapl2
34 1.79e-23 1.69 0.757 0.311 2.37e-19 pneTRH Pcdh11x
35 4.58e-29 1.68 0.788 0.219 6.06e-25 pneTRH Trh Percent of cells expressing MC-Rs in these cells (very low?)
NULLMarker genes for these clusters
Particular combinations of markers in Mc4r containing cells (n sets = 10)
Particular combinations of markers in Mc4r containing cells (n sets = 5)
Particular combinations of markers in Mc4r containing cells (n sets = 3)
Absolute correlation of Mc4r expression with Slc17a6
Absolute correlation of Mc4r expression with Alk
Absolute correlation of Mc4r expression with Crh
Absolute correlation of Mc4r expression with Trh
Absolute correlation of Mc4r expression with Sst
Absolute correlation of Mc4r expression with Avp
Absolute correlation of Mc4r expression with Oxt
Density of Mc4r expressing cells in UMAP (cells similarity) space
Density of expressing cells in UMAP space for whole MC-R gene family
Step back to estimate density of Mc4r and Alk expression across whole adult dataset
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
Examine shared attributes density in PVN (example)
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
Density of main PVN markers expressing cells -
Density of main PVN markers expressing cells -
Density of main PVN markers expressing cells -
Density of main PVN markers expressing cells -
Checking expectation for high density of shared Crh and Mc4r we again see contradiction
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
It’s on the edge
Wierd Trh part of Crh cluster
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
Small lyrical digression about Trh patterns: Onecut3+Trh density (1/4)
Small lyrical digression about Trh patterns: Zic5+Trh density (2/4)
Small lyrical digression about Trh patterns: Onecut3-> Zic5 -> Trh density (3/4)
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.
Small lyrical digression about Trh patterns: Onecut3-> Zic5 -> Trh density (4/4)
Excluded from original publication
Back to Mc4r: blend plots of Mc4r and Trh expression
Back to Mc4r: blend plots of Mc4r and Crh expression
Back to Mc4r: blend plots of Trh and Crh expression
Back to Mc4r: Particular combinations of markers in PVN
NULL
R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.10
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-r0.3.10.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_AT.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_AT.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_AT.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Nebulosa_1.0.2 patchwork_1.1.1 UpSetR_1.4.0
[4] SeuratDisk_0.0.0.9019 SeuratWrappers_0.3.0 SeuratObject_4.0.1
[7] Seurat_4.0.1 future_1.21.0 magrittr_2.0.1
[10] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[16] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.1
[19] here_1.0.1
loaded via a namespace (and not attached):
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[3] workflowr_1.6.2 plyr_1.8.6
[5] igraph_1.2.6 lazyeval_0.2.2
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[9] scattermore_0.7 GenomeInfoDb_1.26.7
[11] digest_0.6.27 htmltools_0.5.1.1
[13] fansi_0.4.2 tensor_1.5
[15] cluster_2.1.2 ks_1.12.0
[17] ROCR_1.0-11 remotes_2.3.0
[19] globals_0.14.0 modelr_0.1.8
[21] matrixStats_0.58.0 spatstat.sparse_2.0-0
[23] colorspace_2.0-1 rvest_1.0.0
[25] ggrepel_0.9.1 haven_2.4.1
[27] xfun_0.22 RCurl_1.98-1.3
[29] crayon_1.4.1 jsonlite_1.7.2
[31] spatstat.data_2.1-0 survival_3.2-11
[33] zoo_1.8-9 glue_1.4.2
[35] polyclip_1.10-0 gtable_0.3.0
[37] zlibbioc_1.36.0 XVector_0.30.0
[39] leiden_0.3.7 DelayedArray_0.16.3
[41] future.apply_1.7.0 SingleCellExperiment_1.12.0
[43] BiocGenerics_0.36.1 abind_1.4-5
[45] scales_1.1.1 mvtnorm_1.1-1
[47] DBI_1.1.1 miniUI_0.1.1.1
[49] Rcpp_1.0.6 viridisLite_0.4.0
[51] xtable_1.8-4 reticulate_1.20
[53] spatstat.core_2.1-2 bit_4.0.4
[55] rsvd_1.0.5 mclust_5.4.7
[57] stats4_4.0.5 htmlwidgets_1.5.3
[59] httr_1.4.2 RColorBrewer_1.1-2
[61] ellipsis_0.3.2 ica_1.0-2
[63] farver_2.1.0 pkgconfig_2.0.3
[65] sass_0.4.0 uwot_0.1.10
[67] dbplyr_2.1.1 deldir_0.2-10
[69] utf8_1.2.1 labeling_0.4.2
[71] tidyselect_1.1.1 rlang_0.4.11
[73] reshape2_1.4.4 later_1.2.0
[75] munsell_0.5.0 cellranger_1.1.0
[77] tools_4.0.5 cli_2.5.0
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[101] reprex_2.0.0 bslib_0.2.4
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[105] lattice_0.20-44 Matrix_1.3-3
[107] vctrs_0.3.8 pillar_1.6.0
[109] lifecycle_1.0.0 BiocManager_1.30.15
[111] spatstat.geom_2.1-0 lmtest_0.9-38
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[115] bitops_1.0-7 data.table_1.14.0
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[133] sctransform_0.3.2 GenomeInfoDbData_1.2.4
[135] S4Vectors_0.28.1 mgcv_1.8-35
[137] parallel_4.0.5 hms_1.0.0
[139] grid_4.0.5 rpart_4.1-15
[141] rmarkdown_2.8 MatrixGenerics_1.2.1
[143] Rtsne_0.15 git2r_0.28.0
[145] Biobase_2.50.0 shiny_1.6.0
[147] lubridate_1.7.10