Last updated: 2019-07-30

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Knit directory: scRNA-seq-workshop-Fall-2019/

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Annotating the cell types with large data set

We have gone through a basic Seurat workflow in the last section using 5k PBMC as an example. We have identified some marker genes for each cluster, and how do we assign each cluster a cell type? Usually, cell type assignment requires a lot of expert opinions based on the known biology of the cells being studied. Large single-cell consortiums such as The Human Cell Atalas (HCA) has produced a lot of data sets with a lot of cells for each tissue/organ and has annotated each cell type. A practical problem is that we have our own single-cell experiemnt done and want to know what are the cell types in our own data set when comparing to a reference data set e.g. in HCA?

Seurat V3 provide convinient functions to do that. For more details, read the paper: Comprehensive Integration of Single-Cell Data and tutorial

Their method aims to first identify ‘anchors’ between pairs of datasets. These represent pairwise correspondences between individual cells (one in each dataset), that we hypothesize originate from the same biological state. These ‘anchors’ are then used to harmonize the datasets, or transfer information from one dataset to another.

For this example, we have a 10k PBMC data set (reference data set) which was annotated by the Seurat developing group. Let’s annotate our 5k PMBC data with the reference.

Transfer of cell type labels from a reference dataset onto a new query dataset

go to the Terminal tab in your Rstudio:

download 10k pbmc reference data

cd data
mkdir pbmc10k
cd pbmc10k

curl -Lo pbmc_10k_v3.rds https://www.dropbox.com/s/3f3p5nxrn5b3y4y/pbmc_10k_v3.rds?dl=1 
# the size of the data
ls -sh

read in the 10k pbmc data

library(Seurat)
Warning: package 'Seurat' was built under R version 3.5.2
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.2.5  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.2       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'dplyr' was built under R version 3.5.2
── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
# this returns a seurat object
pbmc.10k<- readRDS("data/pbmc10k/pbmc_10k_v3.rds")
pbmc.10k
An object of class Seurat 
19089 features across 9432 samples within 1 assay 
Active assay: RNA (19089 features)
 3 dimensional reductions calculated: pca, tsne, umap
pbmc.10k@meta.data %>% head()
                       orig.ident nCount_RNA nFeature_RNA    observed
rna_AAACCCAAGCGCCCAT-1    10x_RNA       2204         1087 0.035812672
rna_AAACCCACAGAGTTGG-1    10x_RNA       5884         1836 0.019227034
rna_AAACCCACAGGTATGG-1    10x_RNA       5530         2216 0.005447865
rna_AAACCCACATAGTCAC-1    10x_RNA       5106         1615 0.014276003
rna_AAACCCACATCCAATG-1    10x_RNA       4572         1800 0.053857351
rna_AAACCCAGTGGCTACC-1    10x_RNA       6702         1965 0.056603774
                       simulated percent.mito RNA_snn_res.0.4
rna_AAACCCAAGCGCCCAT-1 0.4382022   0.02359347               1
rna_AAACCCACAGAGTTGG-1 0.1017964   0.10757988               0
rna_AAACCCACAGGTATGG-1 0.1392801   0.07848101               5
rna_AAACCCACATAGTCAC-1 0.4949495   0.10830396               3
rna_AAACCCACATCCAATG-1 0.1392801   0.08989501               5
rna_AAACCCAGTGGCTACC-1 0.3554328   0.06326470               1
                              celltype
rna_AAACCCAAGCGCCCAT-1      CD4 Memory
rna_AAACCCACAGAGTTGG-1 CD14+ Monocytes
rna_AAACCCACAGGTATGG-1          NK dim
rna_AAACCCACATAGTCAC-1      pre-B cell
rna_AAACCCACATCCAATG-1       NK bright
rna_AAACCCAGTGGCTACC-1      CD4 Memory
## how many cells for each cell type? 14 cell types
table(pbmc.10k@meta.data$celltype)

     B cell progenitor        CD14+ Monocytes        CD16+ Monocytes 
                   460                   2992                    328 
            CD4 Memory              CD4 Naive           CD8 effector 
                  1596                   1047                    383 
             CD8 Naive         Dendritic cell Double negative T cell 
                   337                     74                    592 
        Megakaryocytes              NK bright                 NK dim 
                    52                    109                    435 
                   pDC             pre-B cell 
                    68                    959 
# 10k cells
length(colnames(pbmc.10k))
[1] 9432
p1<- DimPlot(pbmc.10k, group.by = "celltype", label = TRUE, repel = TRUE) + NoLegend() + ggtitle("10k pbmc")

plot the UMAP for two data sets side by side

pbmc<- readRDS("data/pbmc5k/pbmc_5k_v3.rds")

p2<- DimPlot(pbmc, group.by = "seurat_clusters", label = TRUE, repel = TRUE) + NoLegend() + ggtitle("5k pbmc")

CombinePlots(plots = list(p1, p2))

Version Author Date
7bf52d9 Ming Tang 2019-07-23

Now, we can identify anchors between the two dataset and use these anchors to transfer the celltype labels we learned from the 10K scRNA-seq data to the 5k pmbc cells.

transfer.anchors <- FindTransferAnchors(reference = pbmc.10k, query = pbmc, features = VariableFeatures(object = pbmc.10k), 
    reference.assay = "RNA", query.assay = "RNA", reduction = "pcaproject")
Performing PCA on the provided reference using 3000 features as input.
Projecting PCA
Finding neighborhoods
Finding anchors
    Found 7790 anchors
Filtering anchors
    Retained 5062 anchors
Extracting within-dataset neighbors

Note if transferring scRNAseq label to scATACseq data, set reduction = “cca” is recommended

To transfer the cluster ids, we provide a vector of previously annotated cell type labels for the RNA to the refdata parameter. The output will contain a matrix with predictions and confidence scores for each 5k pbmc cell.

celltype.predictions <- TransferData(anchorset = transfer.anchors, refdata = pbmc.10k$celltype, 
    dims = 1:30)
Finding integration vectors
Finding integration vector weights
Predicting cell labels
head(celltype.predictions)
                      predicted.id prediction.score.CD4.Memory
AAACCCAAGCGTATGG   CD14+ Monocytes                   0.0000000
AAACCCAGTCCTACAA   CD14+ Monocytes                   0.0000000
AAACGCTAGGGCATGT B cell progenitor                   0.0000000
AAACGCTGTAGGTACG         CD4 Naive                   0.1296114
AAACGCTGTGTCCGGT   CD14+ Monocytes                   0.0000000
AAACGCTGTGTGATGG            NK dim                   0.0000000
                 prediction.score.CD14..Monocytes prediction.score.NK.dim
AAACCCAAGCGTATGG                                1                       0
AAACCCAGTCCTACAA                                1                       0
AAACGCTAGGGCATGT                                0                       0
AAACGCTGTAGGTACG                                0                       0
AAACGCTGTGTCCGGT                                1                       0
AAACGCTGTGTGATGG                                0                       1
                 prediction.score.pre.B.cell prediction.score.NK.bright
AAACCCAAGCGTATGG                           0                          0
AAACCCAGTCCTACAA                           0                          0
AAACGCTAGGGCATGT                           0                          0
AAACGCTGTAGGTACG                           0                          0
AAACGCTGTGTCCGGT                           0                          0
AAACGCTGTGTGATGG                           0                          0
                 prediction.score.CD4.Naive prediction.score.CD8.Naive
AAACCCAAGCGTATGG                  0.0000000                  0.0000000
AAACCCAGTCCTACAA                  0.0000000                  0.0000000
AAACGCTAGGGCATGT                  0.0000000                  0.0000000
AAACGCTGTAGGTACG                  0.7578112                  0.1125774
AAACGCTGTGTCCGGT                  0.0000000                  0.0000000
AAACGCTGTGTGATGG                  0.0000000                  0.0000000
                 prediction.score.pDC
AAACCCAAGCGTATGG                    0
AAACCCAGTCCTACAA                    0
AAACGCTAGGGCATGT                    0
AAACGCTGTAGGTACG                    0
AAACGCTGTGTCCGGT                    0
AAACGCTGTGTGATGG                    0
                 prediction.score.Double.negative.T.cell
AAACCCAAGCGTATGG                                       0
AAACCCAGTCCTACAA                                       0
AAACGCTAGGGCATGT                                       0
AAACGCTGTAGGTACG                                       0
AAACGCTGTGTCCGGT                                       0
AAACGCTGTGTGATGG                                       0
                 prediction.score.CD16..Monocytes
AAACCCAAGCGTATGG                                0
AAACCCAGTCCTACAA                                0
AAACGCTAGGGCATGT                                0
AAACGCTGTAGGTACG                                0
AAACGCTGTGTCCGGT                                0
AAACGCTGTGTGATGG                                0
                 prediction.score.Megakaryocytes
AAACCCAAGCGTATGG                               0
AAACCCAGTCCTACAA                               0
AAACGCTAGGGCATGT                               0
AAACGCTGTAGGTACG                               0
AAACGCTGTGTCCGGT                               0
AAACGCTGTGTGATGG                               0
                 prediction.score.CD8.effector
AAACCCAAGCGTATGG                             0
AAACCCAGTCCTACAA                             0
AAACGCTAGGGCATGT                             0
AAACGCTGTAGGTACG                             0
AAACGCTGTGTCCGGT                             0
AAACGCTGTGTGATGG                             0
                 prediction.score.B.cell.progenitor
AAACCCAAGCGTATGG                                  0
AAACCCAGTCCTACAA                                  0
AAACGCTAGGGCATGT                                  1
AAACGCTGTAGGTACG                                  0
AAACGCTGTGTCCGGT                                  0
AAACGCTGTGTGATGG                                  0
                 prediction.score.Dendritic.cell prediction.score.max
AAACCCAAGCGTATGG                               0            1.0000000
AAACCCAGTCCTACAA                               0            1.0000000
AAACGCTAGGGCATGT                               0            1.0000000
AAACGCTGTAGGTACG                               0            0.7578112
AAACGCTGTGTCCGGT                               0            1.0000000
AAACGCTGTGTGATGG                               0            1.0000000
pbmc<- AddMetaData(pbmc, metadata = celltype.predictions)

pbmc@meta.data %>% head()
                 orig.ident nCount_RNA nFeature_RNA percent.mt
AAACCCAAGCGTATGG     pbmc5k      13536         3502  10.675236
AAACCCAGTCCTACAA     pbmc5k      12667         3380   5.620905
AAACGCTAGGGCATGT     pbmc5k       5788         1799  10.608155
AAACGCTGTAGGTACG     pbmc5k      13185         2886   7.819492
AAACGCTGTGTCCGGT     pbmc5k      15495         3801   7.460471
AAACGCTGTGTGATGG     pbmc5k       6148         2347   9.954457
                 RNA_snn_res.0.5 seurat_clusters      predicted.id
AAACCCAAGCGTATGG               1               1   CD14+ Monocytes
AAACCCAGTCCTACAA               1               1   CD14+ Monocytes
AAACGCTAGGGCATGT               5               5 B cell progenitor
AAACGCTGTAGGTACG               0               0         CD4 Naive
AAACGCTGTGTCCGGT               1               1   CD14+ Monocytes
AAACGCTGTGTGATGG               4               4            NK dim
                 prediction.score.CD4.Memory
AAACCCAAGCGTATGG                   0.0000000
AAACCCAGTCCTACAA                   0.0000000
AAACGCTAGGGCATGT                   0.0000000
AAACGCTGTAGGTACG                   0.1296114
AAACGCTGTGTCCGGT                   0.0000000
AAACGCTGTGTGATGG                   0.0000000
                 prediction.score.CD14..Monocytes prediction.score.NK.dim
AAACCCAAGCGTATGG                                1                       0
AAACCCAGTCCTACAA                                1                       0
AAACGCTAGGGCATGT                                0                       0
AAACGCTGTAGGTACG                                0                       0
AAACGCTGTGTCCGGT                                1                       0
AAACGCTGTGTGATGG                                0                       1
                 prediction.score.pre.B.cell prediction.score.NK.bright
AAACCCAAGCGTATGG                           0                          0
AAACCCAGTCCTACAA                           0                          0
AAACGCTAGGGCATGT                           0                          0
AAACGCTGTAGGTACG                           0                          0
AAACGCTGTGTCCGGT                           0                          0
AAACGCTGTGTGATGG                           0                          0
                 prediction.score.CD4.Naive prediction.score.CD8.Naive
AAACCCAAGCGTATGG                  0.0000000                  0.0000000
AAACCCAGTCCTACAA                  0.0000000                  0.0000000
AAACGCTAGGGCATGT                  0.0000000                  0.0000000
AAACGCTGTAGGTACG                  0.7578112                  0.1125774
AAACGCTGTGTCCGGT                  0.0000000                  0.0000000
AAACGCTGTGTGATGG                  0.0000000                  0.0000000
                 prediction.score.pDC
AAACCCAAGCGTATGG                    0
AAACCCAGTCCTACAA                    0
AAACGCTAGGGCATGT                    0
AAACGCTGTAGGTACG                    0
AAACGCTGTGTCCGGT                    0
AAACGCTGTGTGATGG                    0
                 prediction.score.Double.negative.T.cell
AAACCCAAGCGTATGG                                       0
AAACCCAGTCCTACAA                                       0
AAACGCTAGGGCATGT                                       0
AAACGCTGTAGGTACG                                       0
AAACGCTGTGTCCGGT                                       0
AAACGCTGTGTGATGG                                       0
                 prediction.score.CD16..Monocytes
AAACCCAAGCGTATGG                                0
AAACCCAGTCCTACAA                                0
AAACGCTAGGGCATGT                                0
AAACGCTGTAGGTACG                                0
AAACGCTGTGTCCGGT                                0
AAACGCTGTGTGATGG                                0
                 prediction.score.Megakaryocytes
AAACCCAAGCGTATGG                               0
AAACCCAGTCCTACAA                               0
AAACGCTAGGGCATGT                               0
AAACGCTGTAGGTACG                               0
AAACGCTGTGTCCGGT                               0
AAACGCTGTGTGATGG                               0
                 prediction.score.CD8.effector
AAACCCAAGCGTATGG                             0
AAACCCAGTCCTACAA                             0
AAACGCTAGGGCATGT                             0
AAACGCTGTAGGTACG                             0
AAACGCTGTGTCCGGT                             0
AAACGCTGTGTGATGG                             0
                 prediction.score.B.cell.progenitor
AAACCCAAGCGTATGG                                  0
AAACCCAGTCCTACAA                                  0
AAACGCTAGGGCATGT                                  1
AAACGCTGTAGGTACG                                  0
AAACGCTGTGTCCGGT                                  0
AAACGCTGTGTGATGG                                  0
                 prediction.score.Dendritic.cell prediction.score.max
AAACCCAAGCGTATGG                               0            1.0000000
AAACCCAGTCCTACAA                               0            1.0000000
AAACGCTAGGGCATGT                               0            1.0000000
AAACGCTGTAGGTACG                               0            0.7578112
AAACGCTGTGTCCGGT                               0            1.0000000
AAACGCTGTGTGATGG                               0            1.0000000
DimPlot(pbmc, group.by = "predicted.id", label = TRUE, repel = TRUE) + NoLegend() + ggtitle("5k pbmc")

Version Author Date
7bf52d9 Ming Tang 2019-07-23

confidence of the label transfering

pbmc@meta.data$prediction.score.max %>%
  tibble::enframe() %>%
  ggplot(aes(x=value)) + 
  geom_histogram(color = "white", bins = 50) +
  geom_vline(xintercept = 0.9, linetype = 2, color = "red") + 
  theme_classic(base_size = 14) +
  ggtitle("distribution of max prediciton score")

Version Author Date
89422b2 Ming Tang 2019-07-25

Most of the cells receive a high prediction score (> 0.9).

Assembly of multiple distinct scRNA-seq datasets into an integrated reference

There are times we want to merge multiple data sets to produce a master reference data. To do this, we can identify anchors using the FindIntegrationAnchors function, which takes a list of Seurat objects as input. Here, We can combine the 5k and 10k data set into a 15k data set.

Use IntegrateData when the data sets may have batch effect. (e.g. sequenced in different platforms, on different days).

Let’s first check if those two data sets have batch effect or not.

pbmc.merge<- merge(pbmc, pbmc.10k)
table(pbmc.merge@meta.data$orig.ident)

10x_RNA  pbmc5k 
   9432    4595 

Investigate in a PCA plot

## using default for all steps
pbmc.merge<- pbmc.merge %>% 
  FindVariableFeatures() %>% 
  NormalizeData() %>% 
  ScaleData() %>% 
  RunPCA(verbose = FALSE)
Centering and scaling data matrix
DimPlot(pbmc.merge, group.by = "orig.ident")

Version Author Date
89422b2 Ming Tang 2019-07-25

The points are overlapping each other and make it difficult to read. use split.by argument

p1<- DimPlot(pbmc.merge, group.by = "orig.ident", split.by = "orig.ident")
p1

Version Author Date
89422b2 Ming Tang 2019-07-25

We see the cells in these two data sets mixed well in the PCA space and no batch effect was observed.

If we do see the cells are seprated by batch. we can do

pbmc.anchors <- FindIntegrationAnchors(object.list = list(pbmc.10k, pbmc), 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 13975 anchors
Filtering anchors
    Retained 8586 anchors
Extracting within-dataset neighbors
pbmc.integrated <- IntegrateData(anchorset = pbmc.anchors, dims = 1:30)
Merging dataset 2 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
DefaultAssay(pbmc.integrated) <- "integrated"

# Run the standard workflow for visualization and clustering
pbmc.integrated <- pbmc.integrated %>%
  ScaleData() %>% 
  RunPCA(verbose = FALSE) %>%
  RunUMAP(reduction = "pca", dims = 1:30)
Centering and scaling data matrix
DimPlot(pbmc.integrated, reduction = "umap", group.by = "orig.ident", split.by = "orig.ident")

Version Author Date
89422b2 Ming Tang 2019-07-25

Let’s compare the PCA with the merged but not “integrated” data

p2<- DimPlot(pbmc.integrated, reduction = "pca", group.by = "orig.ident", split.by = "orig.ident")

CombinePlots(plots = list(p1,p2), ncol = 1)

Version Author Date
89422b2 Ming Tang 2019-07-25

You may observe the subtle differences :)


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.2.5    
 [5] readr_1.3.1     tidyr_0.8.2     tibble_2.0.1    ggplot2_3.1.0  
 [9] tidyverse_1.2.1 Seurat_3.0.2   

loaded via a namespace (and not attached):
  [1] tsne_0.1-3          nlme_3.1-137        bitops_1.0-6       
  [4] fs_1.2.6            lubridate_1.7.4     RColorBrewer_1.1-2 
  [7] httr_1.4.0          rprojroot_1.3-2     sctransform_0.2.0  
 [10] tools_3.5.1         backports_1.1.3     R6_2.3.0           
 [13] irlba_2.3.2         KernSmooth_2.23-15  lazyeval_0.2.1     
 [16] colorspace_1.4-1    withr_2.1.2         npsurv_0.4-0       
 [19] gridExtra_2.3       tidyselect_0.2.5    compiler_3.5.1     
 [22] git2r_0.23.0        cli_1.0.1           rvest_0.3.2        
 [25] xml2_1.2.0          plotly_4.8.0        labeling_0.3       
 [28] caTools_1.17.1.1    scales_1.0.0        lmtest_0.9-36      
 [31] ggridges_0.5.1      pbapply_1.3-4       digest_0.6.18      
 [34] rmarkdown_1.11      R.utils_2.7.0       pkgconfig_2.0.2    
 [37] htmltools_0.3.6     bibtex_0.4.2        readxl_1.2.0       
 [40] htmlwidgets_1.3     rlang_0.3.1         rstudioapi_0.8     
 [43] generics_0.0.2      zoo_1.8-4           jsonlite_1.6       
 [46] ica_1.0-2           gtools_3.8.1        R.oo_1.22.0        
 [49] magrittr_1.5        Matrix_1.2-15       Rcpp_1.0.0         
 [52] munsell_0.5.0       ape_5.2             reticulate_1.10    
 [55] R.methodsS3_1.7.1   stringi_1.2.4       whisker_0.3-2      
 [58] yaml_2.2.0          gbRd_0.4-11         MASS_7.3-51.1      
 [61] gplots_3.0.1        Rtsne_0.15          plyr_1.8.4         
 [64] grid_3.5.1          parallel_3.5.1      gdata_2.18.0       
 [67] listenv_0.7.0       ggrepel_0.8.0       crayon_1.3.4       
 [70] lattice_0.20-38     haven_2.0.0         cowplot_0.9.3      
 [73] splines_3.5.1       hms_0.4.2           SDMTools_1.1-221   
 [76] knitr_1.21          pillar_1.3.1        igraph_1.2.2       
 [79] reshape2_1.4.3      future.apply_1.0.1  codetools_0.2-16   
 [82] glue_1.3.0          evaluate_0.12       lsei_1.2-0         
 [85] metap_1.0           modelr_0.1.2        data.table_1.11.8  
 [88] png_0.1-7           Rdpack_0.10-1       cellranger_1.1.0   
 [91] gtable_0.2.0        RANN_2.6            future_1.10.0      
 [94] assertthat_0.2.0    xfun_0.4            rsvd_1.0.0         
 [97] broom_0.5.1         survival_2.43-3     viridisLite_0.3.0  
[100] workflowr_1.4.0     cluster_2.0.7-1     globals_0.12.4     
[103] fitdistrplus_1.0-11 ROCR_1.0-7