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update vignettes for v5
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‎vignettes/differential_nichenet_pEMT.Rmd‎

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@@ -70,7 +70,7 @@ seurat_obj@meta.data$celltype_aggregate %>% table() %>% sort(decreasing = TRUE)
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```{r}
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celltype_id = "celltype_aggregate" # metadata column name of the cell type of interest
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seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id]])
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seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id, drop=TRUE]])
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```
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## Read in the NicheNet ligand-receptor network and ligand-target matrix

‎vignettes/differential_nichenet_pEMT.md‎

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@@ -103,7 +103,7 @@ seurat_obj@meta.data$celltype_aggregate %>% table() %>% sort(decreasing = TRUE)
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``` r
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celltype_id = "celltype_aggregate" # metadata column name of the cell type of interest
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seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id]])
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seurat_obj = SetIdent(seurat_obj, value = seurat_obj[[celltype_id, drop=TRUE]])
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```
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## Read in the NicheNet ligand-receptor network and ligand-target matrix

‎vignettes/ligand_receptor_circos.svg‎

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‎vignettes/ligand_target_circos.svg‎

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‎vignettes/seurat_steps.Rmd‎

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---
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title: "Perform NicheNet analysis starting from a Seurat object: step-by-step analysis"
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author: "Robin Browaeys"
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date: "2019-11-12"
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date: "2023-10-02"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Perform NicheNet analysis starting from a Seurat object: step-by-step analysis}
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### Load Packages:
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```{r}
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library(nichenetr)
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library(Seurat) # please update to Seurat V4
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library(nichenetr) # Please update to v2.0.4
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library(Seurat)
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library(SeuratObject)
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library(tidyverse)
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```
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```{r}
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seuratObj = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
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seuratObj@meta.data %>% head()
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# For newer Seurat versions, you may need to run the following
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seuratObj <- UpdateSeuratObject(seuratObj)
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```
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Visualize which cell populations are present: CD4 T cells (including regulatory T cells), CD8 T cells, B cells, NK cells, dendritic cells (DCs) and inflammatory monocytes
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```{r}
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seurat_obj_receiver= subset(seuratObj, idents = receiver)
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate"]])
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate", drop=TRUE]])
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condition_oi = "LCMV"
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condition_reference = "SS"
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```{r}
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# ligand expression Seurat dotplot
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order_ligands_adapted = order_ligands
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order_ligands_adapted[order_ligands_adapted == "H2.M3"] = "H2-M3" # cf required use of make.names for heatmap visualization | this is not necessary if these ligands are not in the list of prioritized ligands!
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order_ligands_adapted[order_ligands_adapted == "H2.T23"] = "H2-T23" # cf required use of make.names for heatmap visualization | this is not necessary if these ligands are not in the list of prioritized ligands!
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order_ligands_adapted <- str_replace_all(order_ligands, "\\.", "-")
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rotated_dotplot = DotPlot(seuratObj %>% subset(celltype %in% sender_celltypes), features = order_ligands_adapted, cols = "RdYlBu") + coord_flip() + theme(legend.text = element_text(size = 10), legend.title = element_text(size = 12)) # flip of coordinates necessary because we want to show ligands in the rows when combining all plots
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```
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Top-ranked ligands and target genes shown here differ from the predictions shown in the respective case study in the NicheNet paper because 1) a different definition of expressed genes was used, and 2) we have updated the ligand-target matrix to include more data sources.
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```{r}
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sessionInfo()
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```
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# References
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‎vignettes/seurat_steps.md‎

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analysis
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================
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Robin Browaeys
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2019-11-12
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2023-10-02
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<!-- github markdown built using
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rmarkdown::render("vignettes/seurat_steps.Rmd", output_format = "github_document")
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``` r
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library(nichenetr)
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library(Seurat) # please update to Seurat V4
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library(Seurat)
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library(SeuratObject)
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library(tidyverse)
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```
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``` r
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seuratObj = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
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seuratObj@meta.data %>% head()
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## nGene nUMI orig.ident aggregate res.0.6 celltype nCount_RNA nFeature_RNA
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## W380370 880 1611 LN_SS SS 1 CD8 T 1607 876
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## W380378 847 1546 LN_SS SS 1 CD8 T 1537 838
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## W380379 839 1606 LN_SS SS 0 CD4 T 1603 836
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## W380381 517 844 LN_SS SS 0 CD4 T 840 513
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# For newer Seurat versions, you may need to run the following
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seuratObj <- UpdateSeuratObject(seuratObj)
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```
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Visualize which cell populations are present: CD4 T cells (including
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``` r
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seurat_obj_receiver= subset(seuratObj, idents = receiver)
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate"]])
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate", drop=TRUE]])
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condition_oi = "LCMV"
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condition_reference = "SS"
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## 8 H2-K1 0.605 0.268 0.122 0.142 8
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## 9 H2-Q4 0.605 0.268 0.122 0.141 10
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## 10 H2-Q6 0.605 0.268 0.122 0.141 10
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## # … with 63 more rows
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## # ℹ 63 more rows
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```
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The different ligand activity measures (auroc, aupr, pearson correlation
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``` r
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# ligand expression Seurat dotplot
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order_ligands_adapted = order_ligands
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order_ligands_adapted[order_ligands_adapted == "H2.M3"] = "H2-M3" # cf required use of make.names for heatmap visualization | this is not necessary if these ligands are not in the list of prioritized ligands!
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order_ligands_adapted[order_ligands_adapted == "H2.T23"] = "H2-T23" # cf required use of make.names for heatmap visualization | this is not necessary if these ligands are not in the list of prioritized ligands!
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order_ligands_adapted <- str_replace_all(order_ligands, "\\.", "-")
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rotated_dotplot = DotPlot(seuratObj %>% subset(celltype %in% sender_celltypes), features = order_ligands_adapted, cols = "RdYlBu") + coord_flip() + theme(legend.text = element_text(size = 10), legend.title = element_text(size = 12)) # flip of coordinates necessary because we want to show ligands in the rows when combining all plots
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```
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because 1) a different definition of expressed genes was used, and 2) we
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have updated the ligand-target matrix to include more data sources.
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``` r
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sessionInfo()
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## R version 4.3.1 (2023-06-16)
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## Platform: x86_64-redhat-linux-gnu (64-bit)
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## Running under: CentOS Stream 8
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##
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## Matrix products: default
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## BLAS/LAPACK: /usr/lib64/libopenblaso-r0.3.15.so; LAPACK version 3.9.0
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##
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## locale:
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## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
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## [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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##
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## time zone: Europe/Brussels
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## tzcode source: system (glibc)
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##
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## attached base packages:
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## [1] stats graphics grDevices utils datasets methods base
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##
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## other attached packages:
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## [1] forcats_0.5.1 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.2 readr_2.1.2 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.3 tidyverse_1.3.1 Seurat_4.4.0 nichenetr_2.0.3 testthat_3.1.2
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## [13] SeuratObject_4.1.4 sp_2.0-0
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##
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## loaded via a namespace (and not attached):
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## [1] fs_1.5.2 matrixStats_1.0.0 spatstat.sparse_3.0-2 bitops_1.0-7 devtools_2.4.3 lubridate_1.8.0 httr_1.4.2 RColorBrewer_1.1-2 doParallel_1.0.17
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## [10] tools_4.3.1 sctransform_0.4.0 backports_1.4.1 utf8_1.2.2 R6_2.5.1 lazyeval_0.2.2 uwot_0.1.16 GetoptLong_1.0.5 withr_2.5.0
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## [19] prettyunits_1.1.1 gridExtra_2.3 fdrtool_1.2.17 progressr_0.14.0 cli_3.6.1 DiceKriging_1.6.0 spatstat.explore_3.2-1 labeling_0.4.2 spatstat.data_3.0-1
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## [28] randomForest_4.7-1.1 proxy_0.4-27 ggridges_0.5.3 pbapply_1.5-0 foreign_0.8-84 smoof_1.6.0.3 parallelly_1.30.0 sessioninfo_1.2.2 limma_3.56.2
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## [37] readxl_1.3.1 rstudioapi_0.13 visNetwork_2.1.2 generics_0.1.2 shape_1.4.6 ica_1.0-2 spatstat.random_3.1-5 car_3.1-2 Matrix_1.6-1
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## [46] S4Vectors_0.38.1 fansi_1.0.2 abind_1.4-5 lifecycle_1.0.3 yaml_2.2.2 carData_3.0-5 recipes_1.0.7 Rtsne_0.15 grid_4.3.1
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## [55] promises_1.2.0.1 crayon_1.5.0 miniUI_0.1.1.1 lattice_0.21-8 haven_2.4.3 cowplot_1.1.1 mlr_2.19.1 pillar_1.9.0 knitr_1.37
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## [64] ComplexHeatmap_2.16.0 rjson_0.2.21 future.apply_1.8.1 codetools_0.2-19 fastmatch_1.1-3 leiden_0.3.9 glue_1.6.2 ParamHelpers_1.14.1 data.table_1.14.2
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## [73] remotes_2.4.2 vctrs_0.6.3 png_0.1-7 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 cachem_1.0.6 gower_1.0.1 xfun_0.40
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## [82] mime_0.12 prodlim_2023.03.31 survival_3.5-5 timeDate_4022.108 iterators_1.0.14 hardhat_1.3.0 lava_1.7.2.1 DiagrammeR_1.0.10 ellipsis_0.3.2
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## [91] fitdistrplus_1.1-6 ROCR_1.0-11 ipred_0.9-14 nlme_3.1-162 usethis_2.2.2 RcppAnnoy_0.0.19 rprojroot_2.0.2 irlba_2.3.5 KernSmooth_2.23-21
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## [100] rpart_4.1.19 DBI_1.1.2 BiocGenerics_0.46.0 colorspace_2.0-2 Hmisc_5.1-0 nnet_7.3-19 tidyselect_1.2.0 processx_3.5.2 compiler_4.3.1
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## [109] parallelMap_1.5.1 rvest_1.0.2 htmlTable_2.4.1 xml2_1.3.3 desc_1.4.2 plotly_4.10.0 shadowtext_0.1.2 checkmate_2.2.0 scales_1.2.1
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## [118] caTools_1.18.2 lmtest_0.9-39 callr_3.7.0 digest_0.6.29 goftest_1.2-3 spatstat.utils_3.0-3 rmarkdown_2.11 htmltools_0.5.6 pkgconfig_2.0.3
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## [127] base64enc_0.1-3 lhs_1.1.6 highr_0.9 dbplyr_2.1.1 fastmap_1.1.0 rlang_1.1.1 GlobalOptions_0.1.2 htmlwidgets_1.6.2 shiny_1.7.1
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## [136] BBmisc_1.13 farver_2.1.0 zoo_1.8-9 jsonlite_1.7.3 mlrMBO_1.1.5.1 ModelMetrics_1.2.2.2 magrittr_2.0.2 Formula_1.2-5 patchwork_1.1.1
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## [145] munsell_0.5.0 Rcpp_1.0.11 ggnewscale_0.4.9 reticulate_1.24 stringi_1.7.6 pROC_1.18.4 brio_1.1.3 MASS_7.3-60 plyr_1.8.6
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## [154] pkgbuild_1.3.1 parallel_4.3.1 listenv_0.8.0 ggrepel_0.9.3 deldir_1.0-6 splines_4.3.1 tensor_1.5 hms_1.1.1 circlize_0.4.15
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## [163] ps_1.6.0 igraph_1.5.1 ggpubr_0.6.0 spatstat.geom_3.2-4 ggsignif_0.6.4 reshape2_1.4.4 stats4_4.3.1 pkgload_1.2.4 reprex_2.0.1
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## [172] evaluate_0.14 modelr_0.1.8 tweenr_2.0.2 tzdb_0.4.0 foreach_1.5.2 httpuv_1.6.5 RANN_2.6.1 polyclip_1.10-0 clue_0.3-64
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## [181] future_1.23.0 scattermore_1.2 ggforce_0.4.1 broom_0.7.12 xtable_1.8-4 emoa_0.5-0.2 e1071_1.7-13 rstatix_0.7.2 later_1.3.0
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## [190] viridisLite_0.4.0 class_7.3-22 IRanges_2.34.1 memoise_2.0.1 cluster_2.1.4 globals_0.14.0 caret_6.0-94
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```
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# References
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<div id="refs" class="references csl-bib-body hanging-indent">

‎vignettes/seurat_steps_files/figure-gfm/unnamed-chunk-21-1.png‎

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‎vignettes/seurat_steps_prioritization.Rmd‎

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title: "Perform NicheNet analysis with prioritization"
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author: "Robin Browaeys & Chananchida Sang-aram"
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date: "2023-07-20"
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date: "2023-10-02"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Perform NicheNet analysis starting from a Seurat object: step-by-step analysis with prioritization}
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```{r}
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### Load Packages
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library(nichenetr)
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library(Seurat) # please update to Seurat V4
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library(nichenetr) # Please update to v2.0.4
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library(Seurat)
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library(SeuratObject)
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library(tidyverse)
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### Read in Seurat object
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seuratObj = readRDS(url("https://zenodo.org/record/3531889/files/seuratObj.rds"))
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# For newer Seurat versions, you may need to run the following
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seuratObj <- UpdateSeuratObject(seuratObj)
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```
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In this vignette, we will extend the basic NicheNet analysis analysis from [Perform NicheNet analysis starting from a Seurat object: step-by-step analysis](seurat_steps.md) by incorporating gene expression as part of the prioritization This is a generalization of the [Differential NicheNet](differential_nichenet.md) and [MultiNicheNet](https://github.com/saeyslab/multinichenetr) approach. While the original NicheNet only ranks ligands based on the ligand activity analysis, it is now also possible to prioritize ligands based on upregulation of the ligand/receptor, and the cell-type and condition specificity of hte ligand and receptor.
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lr_network = readRDS(url("https://zenodo.org/record/7074291/files/lr_network_mouse_21122021.rds"))
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ligand_target_matrix = readRDS(url("https://zenodo.org/record/7074291/files/ligand_target_matrix_nsga2r_final_mouse.rds"))
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weighted_networks = readRDS(url("https://zenodo.org/record/7074291/files/weighted_networks_nsga2r_final_mouse.rds"))
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lr_network = lr_network %>% distinct(from, to)
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head(lr_network)
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ligand_target_matrix[1:5,1:5] # target genes in rows, ligands in columns
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# 2. Define a gene set of interest: these are the genes in the “receiver/target” cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction)
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seurat_obj_receiver= subset(seuratObj, idents = receiver)
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate"]])
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seurat_obj_receiver = SetIdent(seurat_obj_receiver, value = seurat_obj_receiver[["aggregate", drop=TRUE]])
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condition_oi = "LCMV"
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condition_reference = "SS"
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make_mushroom_plot(prior_table, top_n = 30, true_color_range = TRUE)
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```
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```{r}
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sessionInfo()
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```
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### References

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