Dataset integration using Seurat and other R tools
scRNA-seq Dataset Integration using R-based tools
## Warning: package 'knitr' was built under R version 4.0.5The code below will install some packages if you don’t have them already.
if (!require("RColorBrewer")) install.packages("RColorBrewer")
if (!require("knitr")) install.packages("knitr")
if (!require("dplyr")) install.packages("dplyr")
if (!require("cowplot")) install.packages("cowplot")
if (!require("harmony")) install.packages("harmony")
if (!require("rliger")) install.packages("rliger")
if (!require("remotes")) install.packages("remotes")
if (!require("SeuratWrappers")) remotes::install_github('satijalab/seurat-wrappers')## sitmo (2.0.1 -> 2.0.2 ) [CRAN]
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## if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
if (!require("glmGamPoi")) BiocManager::install("glmGamPoi")Other packages required for this tutorial should already be installed in your JupyterHub by default:
library(Seurat)
library(SeuratWrappers)
library(DropletUtils)
library(ggplot2)
library(RColorBrewer)
library(knitr)
library(dplyr)
library(harmony)
library(rliger)
library(glmGamPoi)
library(patchwork)
library(cowplot)Let’s also load a custom function, plot_integrated_clusters, that is stored in /data as custom_seurat_functions.R:
source("../data/custom_seurat_functions.R")Introduction
As more and more scRNA-seq datasets become available, carrying merged_seurat comparisons between them is key. There are two main approaches to comparing scRNASeq datasets. The first approach is “label-centric” which is focused on trying to identify equivalent cell-types/states across datasets by comparing individual cells or groups of cells. The other approach is “cross-dataset normalization” which attempts to computationally remove experiment-specific technical/biological effects so that data from multiple experiments can be combined and jointly analyzed.
We will illustrate this process using two very different datasets: 1) 10k PBMC (3’ v3 chemistry) downloaded from 10x Genomics website; 2) major cell populations isolated from whole human blood using FACS, and sequenced using STRT-seq (GSE149938).
Although we already have all the necessary files in our /data folder, we can download the necessary files from GEO database:
download.file("https://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149938/suppl/GSE149938_umi_matrix.csv.gz",
destfile = "GSE149938_umi_matrix.csv.gz")
download.file("https://cf.10xgenomics.com/samples/cell-exp/4.0.0/Parent_NGSC3_DI_PBMC/Parent_NGSC3_DI_PBMC_filtered_feature_bc_matrix.h5",
destfile = "pbmc10k_filt.h5")Let’s read the processed file available via GEO, and the 10x file we’ve processed using SoupX (see another notebook in this repository):
umi_gz <- gzfile("../data/GSE149938_umi_matrix.csv.gz",'rt')
umi <- read.csv(umi_gz,check.names = F,quote = "")
matrix_3p <- Read10X("../data/soupX_pbmc10k_filt")Next, let’s make Seurat objects and re-define some of the metadata columns (GEO dataset simply puts the cell type into the orig.ident slot, which will interfere with what we want to do next):
srat_wb <- CreateSeuratObject(t(umi),project = "whole_blood")## Warning: Non-unique features (rownames) present in the input matrix, making
## uniquesrat_3p <- CreateSeuratObject(matrix_3p,project = "pbmc10k_3p")
rm(umi_gz)
rm(umi)
rm(matrix_3p)colnames(srat_wb@meta.data)[1] <- "cell_type"
srat_wb@meta.data$orig.ident <- "whole_blood"
srat_wb@meta.data$orig.ident <- as.factor(srat_wb@meta.data$orig.ident)
head(srat_wb[[]])## cell_type nCount_RNA nFeature_RNA orig.ident
## BNK_spBM1_L1_bar25 BNK 24494 1869 whole_blood
## BNK_spBM1_L1_bar26 BNK 61980 2051 whole_blood
## BNK_spBM1_L1_bar27 BNK 124382 3872 whole_blood
## BNK_spBM1_L1_bar28 BNK 8144 1475 whole_blood
## BNK_spBM1_L1_bar29 BNK 53612 2086 whole_blood
## BNK_spBM1_L1_bar30 BNK 33582 2038 whole_bloodDo basic quality control. STRT-Seq is quite different from 10x and has a lot more detected genes per cell. Also, for some reason we don’t have the MT genes in the quantified matrix of the whole blood dataset. That’s unfortunate, but not critical.
srat_wb <- SetIdent(srat_wb,value = "orig.ident")
srat_wb[["percent.mt"]] <- PercentageFeatureSet(srat_wb, pattern = "^MT-")
srat_wb[["percent.rbp"]] <- PercentageFeatureSet(srat_wb, pattern = "^RP[SL]")
srat_3p[["percent.mt"]] <- PercentageFeatureSet(srat_3p, pattern = "^MT-")
srat_3p[["percent.rbp"]] <- PercentageFeatureSet(srat_3p, pattern = "^RP[SL]")
VlnPlot(srat_wb, features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rbp"), ncol = 4)
VlnPlot(srat_3p, features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rbp"), ncol = 4)
The annotation that was used to process the GEO whole blood dataset is quite different from the Cell Ranger GRCh38-2020A. Let’s see how many common genes are there:
table(rownames(srat_3p) %in% rownames(srat_wb))##
## FALSE TRUE
## 18050 18551common_genes <- rownames(srat_3p)[rownames(srat_3p) %in% rownames(srat_wb)]Let’s filter the cells with too high or too low number of genes, or too high MT gene content. Generally speaking, cleaner datasets (both in terms of ambient RNA and in terms of low-quality cells) integrate better.
Also, let’s limit the individual matrices to common genes only:
srat_3p <- subset(srat_3p, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 15)
srat_wb <- subset(srat_wb, subset = nFeature_RNA > 1000)
srat_3p <- srat_3p[rownames(srat_3p) %in% common_genes,]
srat_wb <- srat_wb[rownames(srat_wb) %in% common_genes,]As previously for Seurat v3, let’s make a list and normalize/find HVG for each object:
wb_list <- list()
wb_list[["pbmc10k_3p"]] <- srat_3p
wb_list[["whole_blood"]] <- srat_wb
for (i in 1:length(wb_list)) {
wb_list[[i]] <- NormalizeData(wb_list[[i]], verbose = F)
wb_list[[i]] <- FindVariableFeatures(wb_list[[i]], selection.method = "vst", nfeatures = 2000, verbose = F)
}Here we actually do the integration. Seurat 3 does it in two steps.
wb_anchors <- FindIntegrationAnchors(object.list = wb_list, dims = 1:30)## Computing 2000 integration features## Scaling features for provided objects## Finding all pairwise anchors## Running CCA## Merging objects## Finding neighborhoods## Finding anchors## Found 11481 anchors## Filtering anchors## Retained 3178 anchorswb_seurat <- IntegrateData(anchorset = wb_anchors, dims = 1:30)## Merging dataset 2 into 1## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating datarm(wb_list)
rm(wb_anchors)Let’s do the basic processing and visualization of the uncorrected dataset:
DefaultAssay(wb_seurat) <- "RNA"
wb_seurat <- NormalizeData(wb_seurat, verbose = F)
wb_seurat <- FindVariableFeatures(wb_seurat, selection.method = "vst", nfeatures = 2000, verbose = F)
wb_seurat <- ScaleData(wb_seurat, verbose = F)
wb_seurat <- RunPCA(wb_seurat, npcs = 30, verbose = F)
wb_seurat <- RunUMAP(wb_seurat, reduction = "pca", dims = 1:30, verbose = F)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per sessionDimPlot(wb_seurat, reduction = "umap") + plot_annotation(title = "10k 3' PBMC and whole blood, before integration")
DimPlot(wb_seurat, reduction = "umap", group.by = "orig.ident", pt.size = .1, split.by = 'orig.ident') + NoLegend()
Now, let’s take a look at the integrated dataset (it’s already normalized and HVGs are selected):
DefaultAssay(wb_seurat) <- "integrated"
wb_seurat <- ScaleData(wb_seurat, verbose = F)
wb_seurat <- RunPCA(wb_seurat, npcs = 30, verbose = F)
wb_seurat <- RunUMAP(wb_seurat, reduction = "pca", dims = 1:30, verbose = F)
DimPlot(wb_seurat, reduction = "umap") + plot_annotation(title = "10k 3' PBMC and white blood cells, after integration (Seurat 3)")
Let’s look at some markers:
FeaturePlot(wb_seurat,c("MS4A1","LYZ","NKG7","PPBP","LTF","HBA1","FCER1A","IL7R","FCGR3B")) & scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "Spectral")))
From the plot we can see that there are some significant cell types that are absent from PBMC dataset, but exist in the whole blood dataset. LTF gene is the most prominent marker of neutrophils, and HBA1 is a hemoglobin gene expressed in erythrocytes.
Now let’s cluster the integrated matrix and look how clusters are distributed between the two sets:
wb_seurat <- FindNeighbors(wb_seurat, dims = 1:30, k.param = 10, verbose = F)
wb_seurat <- FindClusters(wb_seurat, verbose = F)
DimPlot(wb_seurat,label = T) + NoLegend()
Cluster composition shows many clusters unique to the whole blood dataset:
count_table <- table(wb_seurat@meta.data$seurat_clusters, wb_seurat@meta.data$orig.ident)
count_table##
## pbmc10k_3p whole_blood
## 0 1466 217
## 1 1553 94
## 2 1326 197
## 3 1213 109
## 4 1087 107
## 5 0 906
## 6 354 460
## 7 7 677
## 8 437 216
## 9 1 555
## 10 348 187
## 11 321 157
## 12 0 450
## 13 364 83
## 14 3 413
## 15 288 124
## 16 0 377
## 17 11 366
## 18 8 353
## 19 0 341
## 20 13 313
## 21 204 8
## 22 0 205
## 23 0 203
## 24 19 178
## 25 0 185
## 26 118 17
## 27 99 19
## 28 0 68
## 29 0 58plot_integrated_clusters(wb_seurat)## Using cluster as id variables
We can take advantage of the metadata that was present in GSE149938:
meta <- wb_seurat[[]]
table(meta[meta$seurat_clusters == '5',]$cell_type) ## erythrocytes##
## ery immB MEP preB proB
## 900 2 1 2 1table(meta[meta$seurat_clusters == '20',]$cell_type) ## neutrophils##
## BNK CLP ery GMP immB kineNK LMPP MLP NKP preB proB
## 74 75 6 8 1 1 2 35 30 33 28
## regB
## 20table(meta[meta$seurat_clusters == '24',]$cell_type) ## plasma B cells ##
## ery naiB plasma
## 11 1 166rm(wb_seurat)Harmony, 3’ 10k PBMC cells and whole blood STRT-Seq
Similarly to the previous approaches, let’s make a merged Seurat dataset, normalize and process it:
wb_harmony <- merge(srat_3p,srat_wb)
wb_harmony <- NormalizeData(wb_harmony, verbose = F)
wb_harmony <- FindVariableFeatures(wb_harmony, selection.method = "vst", nfeatures = 2000, verbose = F)
wb_harmony <- ScaleData(wb_harmony, verbose = F)
wb_harmony <- RunPCA(wb_harmony, npcs = 30, verbose = F)
wb_harmony <- RunUMAP(wb_harmony, reduction = "pca", dims = 1:30, verbose = F)We can take a look at the PCA plot for a change, as well as distributions along the first principal component:
p1 <- DimPlot(object = wb_harmony, reduction = "pca", pt.size = .1, group.by = "orig.ident") + NoLegend()
p2 <- VlnPlot(object = wb_harmony, features = "PC_1", group.by = "orig.ident", pt.size = .1) + NoLegend()
plot_grid(p1,p2)
UMAP also shows clear differences between the datasets (we saw this plot above already):
DimPlot(wb_harmony,reduction = "umap") + plot_annotation(title = "10k 3' PBMC and whole blood, before integration")
Let’s run harmony using a simple wrapper named RunHarmony from SeuratWrappers library:
wb_harmony <- wb_harmony %>% RunHarmony("orig.ident", plot_convergence = T)## Harmony 1/10## Harmony 2/10## Harmony 3/10## Harmony converged after 3 iterations## Warning: Invalid name supplied, making object name syntactically valid. New
## object name is Seurat..ProjectDim.RNA.harmony; see ?make.names for more details
## on syntax validity
This generates the embeddings that we shall later use for all downstream analysis.
harmony_embeddings <- Embeddings(wb_harmony, 'harmony')
harmony_embeddings[1:5, 1:5]## harmony_1 harmony_2 harmony_3 harmony_4 harmony_5
## AAACCCACATAACTCG-1 3.2402720 0.5517086 -4.014592 -0.2490492 -0.4555420
## AAACCCACATGTAACC-1 4.3992278 -0.2653914 8.905441 5.7085193 -2.6650973
## AAACCCAGTGAGTCAG-1 3.6924188 1.4199533 -2.323807 -1.7352863 1.2648235
## AAACGAACAGTCAGTT-1 -0.8957237 -3.2318868 -2.398822 -0.1342351 -1.9238917
## AAACGAACATTCGGGC-1 4.6218704 1.6066620 1.914114 -1.7354947 0.8168312Corrected PCA and distribution:
p1 <- DimPlot(object = wb_harmony, reduction = "harmony", pt.size = .1, group.by = "orig.ident") + NoLegend()
p2 <- VlnPlot(object = wb_harmony, features = "harmony_1", group.by = "orig.ident", pt.size = .1) + NoLegend()
plot_grid(p1,p2)
Run UMAP and perform Louvain clustering:
wb_harmony <- wb_harmony %>%
RunUMAP(reduction = "harmony", dims = 1:30, verbose = F) %>%
FindNeighbors(reduction = "harmony", k.param = 10, dims = 1:30) %>%
FindClusters() %>%
identity()## Computing nearest neighbor graph## Computing SNN## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 16883
## Number of edges: 275342
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9231
## Number of communities: 29
## Elapsed time: 1 secondswb_harmony <- SetIdent(wb_harmony,value = "orig.ident")
DimPlot(wb_harmony,reduction = "umap") + plot_annotation(title = "10k 3' PBMC and whole blood, after integration (Harmony)")
DimPlot(wb_harmony, reduction = "umap", group.by = "orig.ident", pt.size = .1, split.by = 'orig.ident') + NoLegend()
Corrected results for this dataset appear to be very similar to Seurat v3:
wb_harmony <- SetIdent(wb_harmony,value = "seurat_clusters")
DimPlot(wb_harmony,label = T) + NoLegend()
More detailed cluster examination also seems to confirm this:
plot_integrated_clusters(wb_harmony) ## Using cluster as id variables
rm(wb_harmony)LIGER, 3’ 10k PBMC cells and whole blood STRT-Seq
Finally, let’s do data integration with LIGER. This step takes several minutes to run:
wb_liger <- merge(srat_3p,srat_wb)
wb_liger <- NormalizeData(wb_liger)
wb_liger <- FindVariableFeatures(wb_liger)
wb_liger <- ScaleData(wb_liger, split.by = "orig.ident", do.center = F)## Scaling data matrix## Scaling data from split pbmc10k_3p## Scaling data from split whole_bloodwb_liger <- RunOptimizeALS(wb_liger, k = 30, lambda = 5, split.by = "orig.ident")##
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## Finished in 10.07576 mins, 30 iterations.
## Max iterations set: 30.
## Final objective delta: 4.18028e-05.
## Best results with seed 1.## Warning: No columnames present in cell embeddings, setting to 'riNMF_1:30'wb_liger <- RunQuantileNorm(wb_liger, split.by = "orig.ident")We will then perform Louvain clustering (FindNeighbors and FindClusters) with the settings similar to what we have been using before:
wb_liger <- FindNeighbors(wb_liger,reduction = "iNMF",k.param = 10,dims = 1:30)## Computing nearest neighbor graph## Computing SNNwb_liger <- FindClusters(wb_liger)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 16883
## Number of edges: 276592
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9233
## Number of communities: 38
## Elapsed time: 0 secondsLet’s look at the corrected UMAP plot in a couple of different ways:
wb_liger <- RunUMAP(wb_liger, dims = 1:ncol(wb_liger[["iNMF"]]), reduction = "iNMF",verbose = F)
wb_liger <- SetIdent(wb_liger,value = "orig.ident")
DimPlot(wb_liger,reduction = "umap") + plot_annotation(title = "10k 3' PBMC and 10k 5' PBMC cells, after integration (LIGER)")
DimPlot(wb_liger, reduction = "umap", group.by = "orig.ident", pt.size = .1, split.by = 'orig.ident') + NoLegend()
Finally, a look at distribution of datasets per cluster:
plot_integrated_clusters(wb_liger)## Using cluster as id variables
rm(wb_liger)
rm(srat_wb)
rm(srat_3p)sessionInfo()
View session info
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] reshape2_1.4.4 rliger_1.0.0
## [3] patchwork_1.1.1 Matrix_1.4-0
## [5] ggplot2_3.3.5 DropletUtils_1.10.3
## [7] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
## [9] Biobase_2.50.0 GenomicRanges_1.42.0
## [11] GenomeInfoDb_1.26.4 IRanges_2.24.1
## [13] S4Vectors_0.28.1 BiocGenerics_0.36.1
## [15] MatrixGenerics_1.2.1 matrixStats_0.61.0
## [17] SeuratWrappers_0.3.0 SeuratObject_4.0.4
## [19] Seurat_4.0.5 glmGamPoi_1.2.0
## [21] remotes_2.4.2 harmony_1.0
## [23] Rcpp_1.0.7 cowplot_1.1.1
## [25] dplyr_1.0.7 RColorBrewer_1.1-2
## [27] knitr_1.36
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 igraph_1.2.6
## [3] lazyeval_0.2.2 splines_4.0.4
## [5] BiocParallel_1.24.1 listenv_0.8.0
## [7] scattermore_0.7 digest_0.6.29
## [9] foreach_1.5.1 htmltools_0.5.2
## [11] fansi_0.5.0 magrittr_2.0.1
## [13] doParallel_1.0.16 tensor_1.5
## [15] cluster_2.1.1 ROCR_1.0-11
## [17] limma_3.46.0 globals_0.14.0
## [19] R.utils_2.10.1 spatstat.sparse_2.0-0
## [21] colorspace_2.0-2 ggrepel_0.9.1
## [23] xfun_0.28 crayon_1.4.2
## [25] RCurl_1.98-1.3 jsonlite_1.7.2
## [27] spatstat.data_2.1-0 iterators_1.0.13
## [29] survival_3.2-10 zoo_1.8-9
## [31] glue_1.5.1 polyclip_1.10-0
## [33] gtable_0.3.0 zlibbioc_1.36.0
## [35] XVector_0.30.0 leiden_0.3.9
## [37] DelayedArray_0.16.3 Rhdf5lib_1.12.1
## [39] future.apply_1.8.1 HDF5Array_1.18.1
## [41] abind_1.4-5 scales_1.1.1
## [43] edgeR_3.32.1 DBI_1.1.1
## [45] miniUI_0.1.1.1 riverplot_0.10
## [47] viridisLite_0.4.0 xtable_1.8-4
## [49] dqrng_0.3.0 reticulate_1.22
## [51] spatstat.core_2.3-2 mclust_5.4.8
## [53] bit_4.0.4 rsvd_1.0.5
## [55] htmlwidgets_1.5.4 httr_1.4.2
## [57] FNN_1.1.3 ellipsis_0.3.2
## [59] ica_1.0-2 farver_2.1.0
## [61] scuttle_1.0.4 pkgconfig_2.0.3
## [63] R.methodsS3_1.8.1 sass_0.4.0
## [65] uwot_0.1.11 deldir_1.0-6
## [67] locfit_1.5-9.4 utf8_1.2.2
## [69] labeling_0.4.2 tidyselect_1.1.1
## [71] rlang_0.4.12 later_1.3.0
## [73] munsell_0.5.0 tools_4.0.4
## [75] generics_0.1.1 ggridges_0.5.3
## [77] evaluate_0.14 stringr_1.4.0
## [79] fastmap_1.1.0 yaml_2.2.1
## [81] goftest_1.2-3 bit64_4.0.5
## [83] fitdistrplus_1.1-6 purrr_0.3.4
## [85] RANN_2.6.1 sparseMatrixStats_1.2.1
## [87] pbapply_1.5-0 future_1.23.0
## [89] nlme_3.1-152 mime_0.12
## [91] R.oo_1.24.0 hdf5r_1.3.3
## [93] compiler_4.0.4 plotly_4.10.0
## [95] png_0.1-7 spatstat.utils_2.2-0
## [97] tibble_3.1.6 bslib_0.3.1
## [99] stringi_1.7.6 highr_0.9
## [101] RSpectra_0.16-0 lattice_0.20-41
## [103] vctrs_0.3.8 rhdf5filters_1.2.0
## [105] pillar_1.6.4 lifecycle_1.0.1
## [107] BiocManager_1.30.16 spatstat.geom_2.3-0
## [109] lmtest_0.9-39 jquerylib_0.1.4
## [111] RcppAnnoy_0.0.19 data.table_1.14.2
## [113] bitops_1.0-7 irlba_2.3.5
## [115] httpuv_1.6.3 R6_2.5.1
## [117] promises_1.2.0.1 KernSmooth_2.23-18
## [119] gridExtra_2.3 parallelly_1.29.0
## [121] codetools_0.2-18 MASS_7.3-53.1
## [123] assertthat_0.2.1 rhdf5_2.34.0
## [125] withr_2.4.3 sctransform_0.3.2
## [127] GenomeInfoDbData_1.2.4 mgcv_1.8-34
## [129] beachmat_2.6.4 grid_4.0.4
## [131] rpart_4.1-15 tidyr_1.1.4
## [133] DelayedMatrixStats_1.12.3 rmarkdown_2.7
## [135] Rtsne_0.15 shiny_1.7.1