Update integration_introduction.Rmd

[simonmfr]

Matched the number of provided cell types to number of factor levels of immune.combined. Using 0:14 results in a warning:

> immune.combined <- RenameIdents(immune.combined, `0` = "CD14 Mono", `1` = "CD4 Naive T", `2` = "CD4 Memory T",
+                                 `3` = "CD16 Mono", `4` = "B", `5` = "CD8 T", `6` = "NK", `7` = "T activated", `8` = "DC", `9` = "B Activated",
+                                 `10` = "Mk", `11` = "pDC", `12` = "Eryth", `13` = "Mono/Mk Doublets", `14` = "HSPC")
Warning: Cannot find identity 14

Reason:

levels(Idents(immune.combined))
 [1] "0"  "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13"

[samuel-marsh]

Hi,

Not member of dev team but wanted to confirm what code you are running. When I copy and paste the following code from the vignette it runs without error for me and there are 15 idents (including ident 14):

library(Seurat)
library(SeuratData)
library(patchwork)

AvailableData()

# install dataset
InstallData("ifnb")
# load dataset
LoadData("ifnb")

# split the dataset into a list of two seurat objects (stim and CTRL)
ifnb.list <- SplitObject(ifnb, split.by = "stim")

# normalize and identify variable features for each dataset independently
ifnb.list <- lapply(X = ifnb.list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = ifnb.list)
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
# this command creates an 'integrated' data assay
immune.combined <- IntegrateData(anchorset = immune.anchors)
# specify that we will perform downstream analysis on the corrected data note that the
# original unmodified data still resides in the 'RNA' assay
DefaultAssay(immune.combined) <- "integrated"

# Run the standard workflow for visualization and clustering
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)


# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
p1 + p2


immune.combined <- RenameIdents(immune.combined, `0` = "CD14 Mono", `1` = "CD4 Naive T", `2` = "CD4 Memory T",
                                `3` = "CD16 Mono", `4` = "B", `5` = "CD8 T", `6` = "NK", `7` = "T activated", `8` = "DC", `9` = "B Activated",
                                `10` = "Mk", `11` = "pDC", `12` = "Eryth", `13` = "Mono/Mk Doublets", `14` = "HSPC")
DimPlot(immune.combined, label = TRUE)

[simonmfr]

@samuel-marsh If I run the same code I still get

> levels(Idents(immune.combined))
 [1] "0"  "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13"

I don't know why this is different from yours - but I guess it is better to leave the code as is and risk a warning. Thanks for mentioning.

[samuel-marsh]

@simonmfr interesting... ya no idea

leaving sessionInfo here in case devs are interested:

sessionInfo

> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

locale:
 [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=C              LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] patchwork_1.1.1         pbmc3k.SeuratData_3.1.4 ifnb.SeuratData_3.1.0   SeuratData_0.2.1        SeuratObject_4.0.4      Seurat_4.0.5           

loaded via a namespace (and not attached):
  [1] nlme_3.1-153          matrixStats_0.61.0    spatstat.sparse_2.0-0 RcppAnnoy_0.0.19      RColorBrewer_1.1-2    httr_1.4.2            sctransform_0.3.2     tools_4.1.2           utf8_1.2.2            R6_2.5.1             
 [11] irlba_2.3.5           rpart_4.1-15          KernSmooth_2.23-20    uwot_0.1.11           mgcv_1.8-38           DBI_1.1.1             lazyeval_0.2.2        colorspace_2.0-2      tidyselect_1.1.1      gridExtra_2.3        
 [21] compiler_4.1.2        cli_3.1.0             plotly_4.10.0         scales_1.1.1          lmtest_0.9-39         spatstat.data_2.1-0   ggridges_0.5.3        pbapply_1.5-0         rappdirs_0.3.3        goftest_1.2-3        
 [31] stringr_1.4.0         digest_0.6.29         spatstat.utils_2.2-0  pkgconfig_2.0.3       htmltools_0.5.2       parallelly_1.29.0     fastmap_1.1.0         htmlwidgets_1.5.4     rlang_0.4.12          rstudioapi_0.13      
 [41] shiny_1.7.1           generics_0.1.1        zoo_1.8-9             jsonlite_1.7.2        ica_1.0-2             dplyr_1.0.7           magrittr_2.0.1        Matrix_1.3-4          Rcpp_1.0.7            munsell_0.5.0        
 [51] fansi_0.5.0           abind_1.4-5           reticulate_1.22       lifecycle_1.0.1       stringi_1.7.6         MASS_7.3-54           Rtsne_0.15            plyr_1.8.6            grid_4.1.2            parallel_4.1.2       
 [61] listenv_0.8.0         promises_1.2.0.1      ggrepel_0.9.1         crayon_1.4.2          miniUI_0.1.1.1        deldir_1.0-6          lattice_0.20-45       cowplot_1.1.1         splines_4.1.2         tensor_1.5           
 [71] pillar_1.6.4          igraph_1.2.9          spatstat.geom_2.3-0   future.apply_1.8.1    reshape2_1.4.4        codetools_0.2-18      leiden_0.3.9          glue_1.5.1            data.table_1.14.2     png_0.1-7            
 [81] vctrs_0.3.8           httpuv_1.6.3          gtable_0.3.0          RANN_2.6.1            purrr_0.3.4           spatstat.core_2.3-2   polyclip_1.10-0       tidyr_1.1.4           scattermore_0.7       future_1.23.0        
 [91] assertthat_0.2.1      ggplot2_3.3.5         mime_0.12             xtable_1.8-4          RSpectra_0.16-0       later_1.3.0           survival_3.2-13       viridisLite_0.4.0     tibble_3.1.6          cluster_2.1.2        
[101] globals_0.14.0        fitdistrplus_1.1-6    ellipsis_0.3.2        ROCR_1.0-11