Last updated: 2023-07-28

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Introduction

In this analysis, we explore the potential of implementing a cell type label transfer workflow from a CITEseq dataset onto a collection of supercells derived from a cytometry data.

The reference CITEseq data used in this study is obtained from a bone marrow sample of a healthy adult, quantified using AbSeq (Triana et al. 2021)

For cytometry data, we utilised a healthy bone marrow sample (Levine et al. 2015) from a benchmarking study on clustering (Weber and Robinson 2016).

The label transfer workflow is as the following. For the cytometry data, we began with transforming markers using an arcsinh transformation with a co-factor of 5, while for the CITEseq data, we used Centered Log Ratio (CLR) transformation. Subsequently, SuperCellCyto was applied with a gamma value of 20. Following this, for both the supercells and CITEseq data, we retain only the common proteins/markers. Lastly, we utilised either the Seurat rPCA (Hao et al. 2021) or Harmony (Korsunsky et al. 2019) and k-Nearest Neighbor (kNN) methods to perform the label tranfer. For the latter, Harmony was employed for integration the supercell with the CITEseq data, while kNN was employed assigning supercell with the cell type annotation in the CITEseq data.

The scripts necessary to replicate the workflow are available in code/label_transfer directory.

The ensuing results are derived from the aforementioned workflow application.

Load libraries

library(pheatmap)
library(data.table)
library(viridis)
library(scales)
library(ggplot2)
library(RColorBrewer)
library(here)

Load data

harmony_res <- fread(here("output", "label_transfer", "harmony_knn.csv"))
rpca_res <- fread(here("output", "label_transfer", "seurat_rPCA.csv"))

We shall remove the unassigned cells as we don’t know their identity.

harmony_res <- harmony_res[Gated_Population != "unassigned"]
rpca_res <- rpca_res[Gated_Population != "unassigned"]

Harmony with kNN

Here, we first employ Harmony do align supercells with the CITEseq data, and thereafter use kNN classifier to transfer the label of the aligned CITEseq data to supercell.

conf_mat <- with(harmony_res, table(predicted_population, Gated_Population))
conf_mat_proportion <- sweep(conf_mat, 2, colSums(conf_mat), "/")
conf_mat_dt <- data.table(conf_mat_proportion)
conf_mat_dt <- conf_mat_dt[order(Gated_Population, predicted_population)]
conf_mat_dt <- conf_mat_dt[N > 0]
ggplot(conf_mat_dt, aes(x=Gated_Population, y=predicted_population)) +
  geom_point(aes(size = N, fill = N), pch=21, color="grey") +
  scale_fill_distiller(palette = "RdBu", direction = -1) +
  theme_minimal() +
  theme(
    panel.border = element_rect(colour = "black", fill=NA, linewidth=0.5),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
    panel.grid.major = element_blank()
  ) +
  labs(x = "Actual Label", y = "Predicted Label", size = "Proportion", fill = "Proportion",
       title = "Harmony Combined with kNN")

rPCA

conf_mat <- with(rpca_res, table(predicted_population, Gated_Population))
conf_mat_proportion <- sweep(conf_mat, 2, colSums(conf_mat), "/")
conf_mat_dt <- data.table(conf_mat_proportion)
conf_mat_dt <- conf_mat_dt[order(Gated_Population, predicted_population)]
conf_mat_dt <- conf_mat_dt[N > 0]
ggplot(conf_mat_dt, aes(x=Gated_Population, y=predicted_population)) +
  geom_point(aes(size = N, fill = N), pch=21, color="grey") +
  scale_fill_distiller(palette = "RdBu", direction = -1) +
  theme_minimal() +
  theme(
    panel.border = element_rect(colour = "black", fill=NA, linewidth=0.5),
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
    panel.grid.major = element_blank()
  ) +
  labs(x = "Actual Label", y = "Predicted Label", size = "Proportion", fill = "Proportion",
       title = "Seurat rPCA")

References

Hao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck III, Shiwei Zheng, Andrew Butler, Maddie J. Lee, et al. 2021. “Integrated Analysis of Multimodal Single-Cell Data.” Cell. https://doi.org/10.1016/j.cell.2021.04.048.
Korsunsky, Ilya, Nghia Millard, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-ru Loh, and Soumya Raychaudhuri. 2019. “Fast, Sensitive and Accurate Integration of Single-Cell Data with Harmony.” Nature Methods 16 (12): 1289–96.
Levine, Jacob H, Erin F Simonds, Sean C Bendall, Kara L Davis, D Amir El-ad, Michelle D Tadmor, Oren Litvin, et al. 2015. “Data-Driven Phenotypic Dissection of AML Reveals Progenitor-Like Cells That Correlate with Prognosis.” Cell 162 (1): 184–97.
Triana, Sergio, Dominik Vonficht, Lea Jopp-Saile, Simon Raffel, Raphael Lutz, Daniel Leonce, Magdalena Antes, et al. 2021. “Single-Cell Proteo-Genomic Reference Maps of the Hematopoietic System Enable the Purification and Massive Profiling of Precisely Defined Cell States.” Nature Immunology 22 (12): 1577–89.
Weber, Lukas M, and Mark D Robinson. 2016. “Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data.” Cytometry Part A 89 (12): 1084–96. 

sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/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] here_1.0.1         RColorBrewer_1.1-3 ggplot2_3.4.1      scales_1.2.1      
[5] viridis_0.6.2      viridisLite_0.4.1  data.table_1.14.8  pheatmap_1.0.12   
[9] workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0 xfun_0.39        bslib_0.4.2      colorspace_2.1-0
 [5] vctrs_0.5.2      generics_0.1.3   htmltools_0.5.4  yaml_2.3.7      
 [9] utf8_1.2.3       rlang_1.0.6      jquerylib_0.1.4  later_1.3.0     
[13] pillar_1.8.1     glue_1.6.2       withr_2.5.0      lifecycle_1.0.3 
[17] stringr_1.5.0    munsell_0.5.0    gtable_0.3.1     evaluate_0.20   
[21] labeling_0.4.2   knitr_1.42       callr_3.7.3      fastmap_1.1.0   
[25] httpuv_1.6.9     ps_1.7.2         fansi_1.0.4      highr_0.10      
[29] Rcpp_1.0.10      promises_1.2.0.1 cachem_1.0.6     jsonlite_1.8.4  
[33] farver_2.1.1     fs_1.6.1         gridExtra_2.3    digest_0.6.31   
[37] stringi_1.7.12   processx_3.8.0   dplyr_1.1.0      getPass_0.2-2   
[41] rprojroot_2.0.3  grid_4.2.3       cli_3.6.0        tools_4.2.3     
[45] magrittr_2.0.3   sass_0.4.5       tibble_3.1.8     whisker_0.4.1   
[49] pkgconfig_2.0.3  rmarkdown_2.20   httr_1.4.4       rstudioapi_0.14 
[53] R6_2.5.1         git2r_0.31.0     compiler_4.2.3