Extra figures
Ha Tran
2024-01-12
Last updated: 2024-08-02
Checks: 6 1
Knit directory: 5_Treg_uNK/1_analysis/
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# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
library(readxl)
library(showtext)
library(qs)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(DT)
library(extrafont)
library(VennDiagram)
# Custom ggplot
library(ggplotify)
library(ComplexHeatmap)
library(gridExtra)
library(ggbiplot)
library(ggrepel)
library(rrvgo)
library(plotly)
library(GOSemSim)
library(data.table)
library(igraph)
# Bioconductor packages:
library(RColorBrewer)
library(CellChat)
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(patchwork)
library(pandoc)
library(knitr)
opts_knit$set(progress = FALSE, verbose = FALSE)
opts_chunk$set(warning=FALSE, message=FALSE, echo=FALSE)
# test <- ggplot(cars,aes(speed,dist))+ geom_point()
#
# ggsave(filename = "test.svg", plot = test, path = here::here("2_plots/"))
# ggsave(filename = "sampleHeat.svg",plot = sampleHeatmap,path = here::here("2_plots/"),width = 25.5,height = 20,units = "cm")Functional heatmap
KEGG + Reactome
CellchatDB & Jia-Peng et al 2022 sc paper
See Jia-Peng et al (2022) paper for more details
CellchatDB subsetted by Jia-Peng et al 2022
Heatmaps
Jiang et al (2023) trophoblast SC paper
See Jiang et al (2023) for full paper
Large Heatmaps
Large heatmaps contains all significant DEGs that matched genes
associated with known trophoblast cell types identified in Jiang et al
2023 from DT vs veh or
DT+Treg vs DT. Therefore DEGs that are COMMON and UNIQUE to
both comparisons.
Heatmap 1
LogCPM of all 127 significant DEGs that are expressed by various trophoblasts cell types identified in Jiang et al (2023) paper. Cell type annotations not include as that will be too overwhelming.
Heatmap 2
Grey means that gene was not significantly differentially expressed in that comparison.
Heatmap 3
Heatmap with all logFC values where significance indicated by asterisks
Small Heatmaps
These heatmaps contains only DEGs that were significant in BOTH DT vs veh AND DT+Treg vs DT.
Heatmap 1
Heatmap 2
MGI data combined
This includes significant DEGs that matched genes associated with known expression in placenta BUT NOT in uterus (based on MGI gene expression data)
Of these 38 genes, those which were also found in the sc trophoblast paper from Jiang et al 2023 paper were also annotated
IMPORTANTLY, this version contains only DEGs that were significant in BOTH DT vs veh AND DT+Treg vs DT.
Again, note that logFC were included for all DEGs but the significant ones in each comparison is marked by asterisk.
ONE CAVEAT: Since this list of 38 genes were obtained programmatically and not manually, this list may contains genes which may have only a few publications which supports it. I suggest using these 38 and going back to MGI for more exploration.
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Adelaide
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] knitr_1.48 pandoc_0.2.0 patchwork_1.2.0
[4] enrichplot_1.24.2 org.Mm.eg.db_3.19.1 AnnotationDbi_1.66.0
[7] IRanges_2.38.1 S4Vectors_0.42.1 clusterProfiler_4.12.2
[10] Glimma_2.14.0 edgeR_4.2.1 limma_3.60.4
[13] CellChat_2.1.2 Biobase_2.64.0 BiocGenerics_0.50.0
[16] RColorBrewer_1.1-3 igraph_2.0.3 data.table_1.15.4
[19] GOSemSim_2.30.0 plotly_4.10.4 rrvgo_1.16.0
[22] ggrepel_0.9.5.9999 ggbiplot_0.6.2 gridExtra_2.3
[25] ComplexHeatmap_2.20.0 ggplotify_0.1.2 VennDiagram_1.7.3
[28] futile.logger_1.4.3 extrafont_0.19 DT_0.33
[31] kableExtra_1.4.0 qs_0.26.3 showtext_0.9-7
[34] showtextdb_3.0 sysfonts_0.8.9 readxl_1.4.3
[37] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[40] purrr_1.0.2 tidyr_1.3.1 ggplot2_3.5.1
[43] tidyverse_2.0.0 reshape2_1.4.4 tibble_3.2.1
[46] readr_2.1.5 magrittr_2.0.3 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] vroom_1.6.5 Biostrings_2.72.1
[3] vctrs_0.6.5 RApiSerialize_0.1.3
[5] digest_0.6.36 png_0.1-8
[7] shape_1.4.6.1 registry_0.5-1
[9] git2r_0.33.0 parallelly_1.38.0
[11] magick_2.8.4 MASS_7.3-61
[13] httpuv_1.6.15 foreach_1.5.2
[15] qvalue_2.36.0 withr_3.0.1
[17] xfun_0.46 ggfun_0.1.5
[19] ggpubr_0.6.0 memoise_2.0.1
[21] gson_0.1.0 systemfonts_1.1.0
[23] ragg_1.3.2 tidytree_0.4.6
[25] GlobalOptions_0.1.2 pbapply_1.7-2
[27] KEGGREST_1.44.1 promises_1.3.0
[29] httr_1.4.7 rstatix_0.7.2
[31] globals_0.16.3 stringfish_0.16.0
[33] rstudioapi_0.16.0 UCSC.utils_1.0.0
[35] generics_0.1.3 DOSE_3.30.2
[37] ggalluvial_0.12.5 zlibbioc_1.50.0
[39] ggraph_2.2.1 polyclip_1.10-7
[41] GenomeInfoDbData_1.2.12 SparseArray_1.4.8
[43] xtable_1.8-4 doParallel_1.0.17
[45] evaluate_0.24.0 S4Arrays_1.4.1
[47] hms_1.1.3 GenomicRanges_1.56.1
[49] irlba_2.3.5.1 colorspace_2.1-1
[51] ggnetwork_0.5.13 NLP_0.2-1
[53] reticulate_1.38.0 treemap_2.4-4
[55] later_1.3.2 viridis_0.6.5
[57] ggtree_3.12.0 lattice_0.22-6
[59] NMF_0.27 future.apply_1.11.2
[61] shadowtext_0.1.4 cowplot_1.1.3
[63] matrixStats_1.3.0 pillar_1.9.0
[65] nlme_3.1-165 iterators_1.0.14
[67] sna_2.7-2 gridBase_0.4-7
[69] compiler_4.4.1 RSpectra_0.16-2
[71] stringi_1.8.4 SummarizedExperiment_1.34.0
[73] plyr_1.8.9 crayon_1.5.3
[75] abind_1.4-5 gridGraphics_0.5-1
[77] locfit_1.5-9.10 graphlayouts_1.1.1
[79] bit_4.0.5 fastmatch_1.1-4
[81] whisker_0.4.1 codetools_0.2-20
[83] textshaping_0.4.0 openssl_2.2.0
[85] crosstalk_1.2.1 bslib_0.8.0
[87] slam_0.1-52 GetoptLong_1.0.5
[89] tm_0.7-13 mime_0.12
[91] splines_4.4.1 circlize_0.4.16
[93] Rcpp_1.0.13 HDO.db_0.99.1
[95] cellranger_1.1.0 Rttf2pt1_1.3.12
[97] blob_1.2.4 utf8_1.2.4
[99] here_1.0.1 clue_0.3-65
[101] fs_1.6.4 listenv_0.9.1
[103] ggsignif_0.6.4 Matrix_1.7-0
[105] statmod_1.5.0 tzdb_0.4.0
[107] svglite_2.1.3 tweenr_2.0.3
[109] pkgconfig_2.0.3 pheatmap_1.0.12
[111] network_1.18.2 tools_4.4.1
[113] cachem_1.1.0 RSQLite_2.3.7
[115] viridisLite_0.4.2 DBI_1.2.3
[117] fastmap_1.2.0 rmarkdown_2.27
[119] scales_1.3.0 broom_1.0.6
[121] sass_0.4.9 coda_0.19-4.1
[123] FNN_1.1.4 carData_3.0-5
[125] farver_2.1.2 tidygraph_1.3.1
[127] scatterpie_0.2.3 yaml_2.3.10
[129] workflowr_1.7.1 MatrixGenerics_1.16.0
[131] cli_3.6.3 lifecycle_1.0.4
[133] askpass_1.2.0 lambda.r_1.2.4
[135] backports_1.5.0 BiocParallel_1.38.0
[137] timechange_0.3.0 gtable_0.3.5
[139] rjson_0.2.21 umap_0.2.10.0
[141] parallel_4.4.1 ape_5.8
[143] jsonlite_1.8.8 bit64_4.0.5
[145] yulab.utils_0.1.5 BiocNeighbors_1.22.0
[147] RcppParallel_5.1.8 futile.options_1.0.1
[149] jquerylib_0.1.4 highr_0.11
[151] lazyeval_0.2.2 shiny_1.9.1
[153] htmltools_0.5.8.1 GO.db_3.19.1
[155] rappdirs_0.3.3 formatR_1.14
[157] glue_1.7.0 XVector_0.44.0
[159] rprojroot_2.0.4 treeio_1.28.0
[161] extrafontdb_1.0 R6_2.5.1
[163] DESeq2_1.44.0 labeling_0.4.3
[165] cluster_2.1.6 rngtools_1.5.2
[167] wordcloud_2.6 aplot_0.2.3
[169] GenomeInfoDb_1.40.1 statnet.common_4.9.0
[171] DelayedArray_0.30.1 tidyselect_1.2.1
[173] ggforce_0.4.2 xml2_1.3.6
[175] car_3.1-2 future_1.34.0
[177] munsell_0.5.1 htmlwidgets_1.6.4
[179] fgsea_1.30.0 rlang_1.1.4
[181] fansi_1.0.6