Last updated: 2022-08-02

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Knit directory: scATACseq-topics/

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Add text here summarizing the analysis.

Load the packages used in the analysis below.

library(Matrix)
library(fastTopics)
library(ggplot2)

Load the sample meta-data:

tissues <-
  c("BoneMarrow_62016","BoneMarrow_62216",
  "HeartA_62816","Kidney_62016","Testes_62016","SmallIntestine_62816",
  "LargeIntestineA_62816",
  "LargeIntestineB_62816","Liver_62016",
  "Lung1_62216","Lung2_62216",
  "Spleen_62016",
  "Thymus_62016","PreFrontalCortex_62216",
  "Cerebellum_62216","WholeBrainA_62216","WholeBrainA_62816")
load("data/Cusanovich_2018/processed_data/Cusanovich_2018_metadata_only.RData")
samples <- transform(samples,
                     tissue.replicate = factor(tissue.replicate,tissues))

Next load the \(k = 13\) multinomial topic model fit:

fit <- readRDS("output/Cusanovich_2018/fit-Cusanovich2018-scd-ex-k=13.rds")$fit

This Structure plot shows the cells arranged by tissue. Note that replicates were collected for four of the tissues (bone marrow, lung, small intestine, whole brain) in a second mouse.

set.seed(1)
topic_colors <- c("gold","royalblue","red","sienna","limegreen",
                  "plum","tomato","purple","cyan","forestgreen",
                  "darkblue","darkorange","lightgray")
topics <- c(1,2,5,6,9,11,4,10,8,3,7,12,13)
p1 <- structure_plot(fit,grouping = samples$tissue.replicate,gap = 30,n = 4000,
                     perplexity = 30,colors = topic_colors,topics = topics,
                     verbose = FALSE)
print(p1)

Version Author Date
05361f0 Peter Carbonetto 2022-08-01
44d4987 Peter Carbonetto 2022-08-01
643af50 Peter Carbonetto 2022-08-01
52d4663 Peter Carbonetto 2022-08-01

Reassuringly, the replicates show a very similar distribution of topics. Also reassuringly, some tissues are clearly distinguished by the topics (e.g., thymus), and similar tissues (e.g., small and large intenstine) share topics.

This next Structure plot arranges the cells by the cluster labels assigned by Cusanovich et al (see the “cell_type” column in Table S1).

cell_types <-
  c("Cardiomyocytes",
    "Astrocytes",
    "Oligodendrocytes",
    "Hepatocytes",
    "Podocytes",
    "Endothelial cells",
    "Neurons",
    "Purkinje cells",
    "Cerebellar granule cells",
    "T cells",
    "NK cells",
    "B cells",
    "Other immune cells",
    "Proximal tubule",
    "cluster 18",
    "Pneumocytes",
    "Enterocytes",
    "Erythroblasts",
    "Sperm",
    "Hematopoietic progenitors",
    "Collisions",
    "Unknown")
x <- samples$cell_label
x[x == "Activated B cells"] <- "B cells"
x[x == "Immature B cells"] <- "B cells"
x[x == "Ex. neurons CPN"] <- "Neurons"
x[x == "Ex. neurons SCPN"] <- "Neurons"
x[x == "Ex. neurons CThPN"] <- "Neurons"
x[x == "SOM+ Interneurons"] <- "Neurons"
x[x == "Inhibitory neurons"] <- "Neurons"
x[x == "Regulatory T cells"] <- "T cells"
x[x == "Endothelial I cells"] <- "Endothelial cells"
x[x == "Endothelial II cells"] <- "Endothelial cells"
x[x == "Endothelial I (glomerular)"] <- "Endothelial cells"
x[x == "Proximal tubule S3"] <- "Proximal tubule"
x[x == "Type I pneumocytes"] <- "Pneumocytes"
x[x == "Type II pneumocytes"] <- "Pneumocytes"
x[x == "Alveolar macrophages"] <- "Macrophages"
x[x == "Loop of henle"] <- "cluster 18"
x[x == "Distal convoluted tubule"] <- "cluster 18"
x[x == "Collecting duct"] <- "cluster 18"
x[x == "DCT/CD"] <- "cluster 18"
x[x == "Monocytes"] <- "Other immune cells"
x[x == "Dendritic cells"] <- "Other immune cells"
x[x == "Macrophages"] <- "Other immune cells"
x[x == "Microglia"] <- "Other immune cells"
samples <- transform(samples,
                     cell_label = factor(x,cell_types))
set.seed(1)
p2 <- structure_plot(fit,grouping = samples$cell_label,gap = 30,n = 4000,
                    perplexity = 30,colors = topic_colors,verbose = FALSE)
print(p2)

Version Author Date
cc5335a Peter Carbonetto 2022-08-02
9eff66b Peter Carbonetto 2022-08-01

Clearly, the topics capture much of the high-level structure identified by community detection (Cusanovich et al used the Louvain algorithm implemented in Seurat).


sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] ggplot2_3.3.6      fastTopics_0.6-131 Matrix_1.2-18      workflowr_1.7.0   
# 
# loaded via a namespace (and not attached):
#  [1] mcmc_0.9-6         fs_1.5.2           progress_1.2.2     httr_1.4.2        
#  [5] rprojroot_1.3-2    tools_3.6.2        backports_1.1.5    bslib_0.3.1       
#  [9] utf8_1.1.4         R6_2.4.1           irlba_2.3.3        uwot_0.1.10       
# [13] DBI_1.1.0          lazyeval_0.2.2     colorspace_1.4-1   withr_2.5.0       
# [17] tidyselect_1.1.1   prettyunits_1.1.1  processx_3.5.2     compiler_3.6.2    
# [21] git2r_0.29.0       quantreg_5.54      SparseM_1.78       plotly_4.9.2      
# [25] labeling_0.3       sass_0.4.0         scales_1.1.0       SQUAREM_2017.10-1 
# [29] quadprog_1.5-8     callr_3.7.0        pbapply_1.5-1      mixsqp_0.3-46     
# [33] systemfonts_1.0.2  stringr_1.4.0      digest_0.6.23      rmarkdown_2.11    
# [37] MCMCpack_1.4-5     pkgconfig_2.0.3    htmltools_0.5.2    fastmap_1.1.0     
# [41] invgamma_1.1       highr_0.8          htmlwidgets_1.5.1  rlang_0.4.11      
# [45] rstudioapi_0.13    jquerylib_0.1.4    generics_0.0.2     farver_2.0.1      
# [49] jsonlite_1.7.2     dplyr_1.0.7        magrittr_2.0.1     Rcpp_1.0.8        
# [53] munsell_0.5.0      fansi_0.4.0        lifecycle_1.0.0    stringi_1.4.3     
# [57] whisker_0.4        yaml_2.2.0         MASS_7.3-51.4      Rtsne_0.15        
# [61] grid_3.6.2         parallel_3.6.2     promises_1.1.0     ggrepel_0.9.1     
# [65] crayon_1.4.1       lattice_0.20-38    cowplot_1.1.1      hms_1.1.0         
# [69] knitr_1.37         ps_1.6.0           pillar_1.6.2       glue_1.4.2        
# [73] evaluate_0.14      getPass_0.2-2      data.table_1.12.8  RcppParallel_5.1.5
# [77] vctrs_0.3.8        httpuv_1.5.2       MatrixModels_0.4-1 gtable_0.3.0      
# [81] purrr_0.3.4        tidyr_1.1.3        assertthat_0.2.1   ashr_2.2-54       
# [85] xfun_0.29          coda_0.19-3        later_1.0.0        ragg_0.3.1        
# [89] viridisLite_0.3.0  truncnorm_1.0-8    tibble_3.1.3       ellipsis_0.3.2