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Read datasets

datasets <- readr::read_csv(here::here("data", "datasets.csv"), col_types = readr::cols())

sct_thetas <- lapply(datasets$key, FUN = function(x) {
  theta_file <- here::here("output", "snakemake_output", "seurat_output", clean_keys(x), "vst2", "gene_attr.csv")
  if (file.exists(theta_file)) {
    theta_df <- read.csv(theta_file)
    theta_df$key <- x
    return(theta_df)
  } else {
    return(NULL)
  }
})
sct_thetas_df <- bind_rows(sct_thetas)
sct_thetas_df <- left_join(sct_thetas_df, datasets)
sct_thetas_df <- sct_thetas_df %>% arrange(datatype)
colors <- brewer.pal(3, "Set2")[2:3]
names(colors) <- c("cell line", "heterogeneous")


sct_thetas_df_modified <- sct_thetas_df[is.finite(sct_thetas_df$step1_theta), ]
sct_thetas_df_modified <- sct_thetas_df_modified[sct_thetas_df_modified$datatype == "cell line", ]
p3 <- DoThetaPlot(sct_thetas_df_modified) + theme(legend.position = "none")


sct_thetas_df_modified <- sct_thetas_df[is.finite(sct_thetas_df$step1_theta), ]
sct_thetas_df_modified <- sct_thetas_df_modified[sct_thetas_df_modified$datatype == "heterogeneous", ]
p4 <- DoThetaPlot(sct_thetas_df_modified) + theme(legend.position = "none")

p0c <- p3 | p4
p0c

Version Author Date
400797a Saket Choudhary 2021-07-06

Plot residual variance for \(\theta=\inf,\theta=100,\theta=10\)

dataset_keys2 <- c(
  "Ding-PBMC2__ChromiumV2", "Ding-MouseMixture1__Drop-seq", "HEK__ChromiumV3", "PBMC__Smart-seq3"
)

dataset_keys <- c(
  "TechnicalControl1__ChromiumV1", "TechnicalControl2__ChromiumV1", "Ding-HumanMixture1__inDrops", "Ding-HumanMixture1__sci-RNA-seq",
  "Ding-Cortex2__sci-RNA-seq", "Ding-PBMC2__CEL-seq2", "Fibroblasts__Smart-seq3", "PBMC__ChromiumV3"
)


methods <- c(
  "offset-Inf", "offset-100",
  "offset-10"
)
names(methods) <- c(
  paste("theta==infinity"),
  paste("theta==100"),
  paste("theta==10")
)

Read residual variances

gene_attrs <- list()

for (key in dataset_keys) {
  for (method in methods) {
    output_dir <- here::here("output", "snakemake_output", "seurat_output", key, method)
    gene_attr_tmp <- read.csv(file.path(output_dir, "gene_attr.csv"), row.names = 1)
    gene_attr_tmp$method <- method
    if (method == methods[1]) {
      gene_attr <- gene_attr_tmp
    } else {
      common_cols <- intersect(colnames(gene_attr), colnames(gene_attr_tmp))
      gene_attr <- rbind(gene_attr[, common_cols], gene_attr_tmp[, common_cols])
    }
  }
  gene_attrs[[key]] <- gene_attr
}
gene_attrs_df <- bind_rows(gene_attrs, `.id` = "key")
gene_attrs_df$method <- factor(gene_attrs_df$method, levels = methods, labels = names(methods))

max_resvar <- 25
gene_attrs_df[gene_attrs_df$residual_variance > max_resvar, "residual_variance"] <- max_resvar
gene_attrs <- list()

for (key in dataset_keys2) {
  for (method in c("offset-10", "offset-100")) {
    output_dir <- here::here("output", "snakemake_output", "seurat_output", key, method)
    gene_attr <- read.csv(file.path(output_dir, "gene_attr.csv"), row.names = 1)
    gene_attr$method <- method
    gene_attr$key <- key
    gene_attrs[[paste0(key, method)]] <- gene_attr
  }
}
gene_attrs_df_sampled <- bind_rows(gene_attrs)
gene_attrs_df_sampled$method <- factor(gene_attrs_df_sampled$method, levels = methods, labels = names(methods))

max_resvar <- 25
gene_attrs_df_sampled[gene_attrs_df_sampled$residual_variance > max_resvar, "residual_variance"] <- max_resvar

gene_attrs_df_sampled <- left_join(gene_attrs_df_sampled, datasets)

gene_attrs_df_subset <- gene_attrs_df_sampled
gene_attrs_df_subset$datatype <- factor(gene_attrs_df_subset$datatype, levels = c("cell line", "heterogeneous"))
gene_attrs_df_subset <- gene_attrs_df_subset[gene_attrs_df_subset$method %in% c("theta==10", "theta==100"), ]


colors <- brewer.pal(3, "Set2")[2:3]
names(colors) <- c("cell line", "heterogeneous")

mylabeller <- labeller(
  method = label_parsed, sample_name = label_wrap_gen(width = 10)
)


p1 <- ggplot(gene_attrs_df_subset, aes(gmean, residual_variance, color = datatype)) +
  geom_scattermore(pointsize = 1.1, shape = 16, alpha = 0.5) +
  geom_smooth(color = "red", method = "loess", span = 0.1, size = 0.9, formula = "y ~ x") + # , se = FALSE) +
  scale_color_manual(values = colors, name = "") +
  geom_hline(yintercept = 1, color = "#4daf4a", size = 0.9, linetype = "dashed") +
  scale_y_continuous(trans = "sqrt", breaks = c(0, 1, 10, 25, 50, 100, 150), limits = c(0, max_resvar + 1)) +
  scale_x_continuous(trans = "log10", breaks = c(0.001, 1, 10, 100), labels = MASS::rational) +
  facet_grid(method ~ sample_name, labeller = mylabeller) +
  xlab("Gene mean") +
  ylab("Residual variance") +
  theme_pubr() +
  theme(
    legend.position = "none",
  )
p1

Version Author Date
400797a Saket Choudhary 2021-07-06
gene_attrs_df <- left_join(gene_attrs_df, datasets)
p2 <- ggplot(gene_attrs_df, aes(gmean, residual_variance, color = sample_name)) +
  geom_hline(yintercept = 1, color = "#4daf4a", size = 0.9, linetype = "dashed") +
  geom_smooth(aes(color = sample_name), method = "loess", span = 0.1, size = 0.9, formula = "y ~ x") +
  scale_y_continuous(trans = "sqrt", breaks = c(0, 1, 10, 25, 50, 100, 150), limits = c(0, max_resvar + 1)) +
  scale_x_continuous(trans = "log10", breaks = c(0.001, 0.01, 0.1, 1, 10, 100), labels = MASS::rational) +
  facet_wrap(~method, ncol = 5, labeller = label_parsed) +
  xlab("Gene mean") +
  ylab("Residual variance") +
  theme_pubr() +
  theme(
    legend.position = "bottom",
    legend.key = element_rect(fill = NA),
    legend.background = element_blank()
  ) +
  scale_color_manual(values = brewer.pal(8, "Set1"), name = "") +
  plot_layout(guides = "collect") &
  theme(
    legend.position = "bottom",
    legend.key = element_rect(fill = NA),
    legend.background = element_blank()
  )
p2

Version Author Date
400797a Saket Choudhary 2021-07-06
p3x <- p3 + theme(text = element_text(size=6))
p4x <- p4 + theme(text = element_text(size=6))
p3x

Version Author Date
400797a Saket Choudhary 2021-07-06
layout <- "
AB
CC
DD
"

p3x + p4x + p1 + p2 + plot_layout(design = layout, tag_level = "new") + plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(face = "bold"))

Version Author Date
400797a Saket Choudhary 2021-07-06
ggsave(here::here("output", "figures", "02_Figure2.pdf"), width = 11, height = 12)

Supplementary figures

max_resvar <- 25
gene_attrs_df[gene_attrs_df$residual_variance > max_resvar, "residual_variance"] <- max_resvar
global_labeller <- labeller(
  sample_name = label_wrap_gen(8),
  method = label_parsed
)
p1 <- ggplot(gene_attrs_df, aes(gmean, residual_variance)) +
  geom_scattermore(pointsize = 1.1, shape = 16, alpha = 0.5) +
  geom_hline(yintercept = 1, color = "#4daf4a", size = 0.9, linetype = "dashed") +
  geom_smooth(aes(color = "red"), method = "loess", span = 0.1, size = 0.9, formula = "y ~ x") +
  scale_y_continuous(trans = "sqrt", breaks = c(0, 1, 10, 25, 50, 100, 150), limits = c(0, max_resvar + 1)) +
  scale_x_continuous(trans = "log10", breaks = c(0.001, 0.1, 10), labels = MASS::rational) +
  facet_grid(method ~ sample_name, labeller = global_labeller) +
  xlab("Gene mean") +
  ylab("Residual variance") +
  theme_pubr() +
  plot_layout(guides = "collect") & theme(
  legend.position = "bottom",
  legend.key = element_rect(fill = NA),
  legend.background = element_blank()
) + theme(legend.position = "none")

p1

Version Author Date
400797a Saket Choudhary 2021-07-06
ggsave(here::here("output", "figures", "02_residual_variance_vs_genemean_expanded.pdf"), width = 10, height = 5, dpi = "print")
methods <- c(
  "offset-Inf", "offset-100",
  "offset-10", "vst2"
)
names(methods) <- c(
  paste("theta==infinity"),
  paste("theta==100"),
  paste("theta==10"), paste("SCT")
)

gene_attrs <- list()


for (key in dataset_keys) {
  for (method in methods) {
    output_dir <- here::here("output", "snakemake_output", "seurat_output", key, method)
    gene_attr <- read.csv(file.path(output_dir, "gene_attr.csv"), row.names = 1)
    gene_attr$method <- method
    gene_attr$key <- key
    gene_attrs[[paste0(key, method)]] <- gene_attr
  }
}
gene_attrs_df <- bind_rows(gene_attrs)

gene_attrs_df$method <- factor(gene_attrs_df$method, levels = methods, labels = names(methods))

gene_attrs_df <- left_join(gene_attrs_df, datasets)
max_resvar <- 25
gene_attrs_df[gene_attrs_df$residual_variance > max_resvar, "residual_variance"] <- max_resvar

global_labeller <- labeller(
  sample_name = label_wrap_gen(8),
  method = label_parsed
)
p.sup <- ggplot(gene_attrs_df, aes(gmean, residual_variance)) +
  geom_scattermore(pointsize = 1.1, shape = 16, alpha = 0.5) +
  geom_hline(yintercept = 1, color = "#4daf4a", size = 0.9, linetype = "dashed") +
  geom_smooth(aes(color = "red"), method = "loess", span = 0.1, size = 0.9, formula = "y ~ x") +
  scale_y_continuous(trans = "sqrt", breaks = c(0, 1, 10, 25, 50, 100, 150), limits = c(0, max_resvar + 1)) +
  scale_x_continuous(trans = "log10", breaks = c(0.001, 0.1, 10), labels = MASS::rational) +
  facet_grid(method ~ sample_name, labeller = global_labeller) +
  xlab("Gene mean") +
  ylab("Residual variance") +
  theme_pubr() +
  plot_layout(guides = "collect") & theme(
  legend.position = "bottom",
  legend.key = element_rect(fill = NA),
  legend.background = element_blank()
) + theme(legend.position = "none")

p.sup

Version Author Date
400797a Saket Choudhary 2021-07-06
ggsave(here::here("output", "figures", "Fig2S.pdf"), width = 10, height = 5, dpi = "print")
p2.expanded <- ggplot(gene_attrs_df, aes(gmean, residual_variance, color = sample_name)) +
  geom_hline(yintercept = 1, color = "#4daf4a", size = 0.9, linetype = "dashed") +
  geom_smooth(aes(color = sample_name), method = "loess", span = 0.1, size = 0.9, formula = "y ~ x") +
  scale_y_continuous(trans = "sqrt", breaks = c(0, 1, 10, 25, 50, 100, 150), limits = c(0, max_resvar + 1)) +
  scale_x_continuous(trans = "log10", breaks = c(0.001,  0.1, 1, 10, 100), labels = MASS::rational) +
  facet_wrap(~method, ncol = 5, labeller = label_parsed) +
  xlab("Gene mean") +
  ylab("Residual variance") +
  theme_pubr() +
  theme(
    legend.position = "bottom",
    legend.key = element_rect(fill = NA),
    legend.background = element_blank()
  ) +
  scale_color_manual(values = brewer.pal(8, "Set1"), name = "") +
  plot_layout(guides = "collect") &
  theme(
    legend.position = "bottom",
    legend.key = element_rect(fill = NA),
    legend.background = element_blank()
  )
p2.expanded

Version Author Date
400797a Saket Choudhary 2021-07-06
ggsave(here::here("output", "figures", "02_Fig2C_expanded.pdf"), width = 12, height = 4, dpi = "print")
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:   /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

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] SeuratObject_4.0.4      Seurat_4.0.5            sparseMatrixStats_1.4.2
 [4] MatrixGenerics_1.4.3    matrixStats_0.61.0      scattermore_0.7        
 [7] sctransform_0.3.2.9008  RColorBrewer_1.1-2      patchwork_1.1.1        
[10] here_1.0.1              ggridges_0.5.3          ggpubr_0.4.0           
[13] ggplot2_3.3.5           dplyr_1.0.7             workflowr_1.6.2        

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_1.0-6         
  [4] ggsignif_0.6.3        ellipsis_0.3.2        rprojroot_2.0.2      
  [7] fs_1.5.2              spatstat.data_2.1-0   farver_2.1.0         
 [10] leiden_0.3.9          listenv_0.8.0         bit64_4.0.5          
 [13] ggrepel_0.9.1         fansi_0.5.0           codetools_0.2-18     
 [16] splines_4.1.2         knitr_1.36            polyclip_1.10-0      
 [19] jsonlite_1.7.2        broom_0.7.10          ica_1.0-2            
 [22] cluster_2.1.2         png_0.1-7             uwot_0.1.11          
 [25] shiny_1.7.1           spatstat.sparse_2.0-0 readr_2.1.1          
 [28] compiler_4.1.2        httr_1.4.2            backports_1.4.1      
 [31] assertthat_0.2.1      Matrix_1.4-0          fastmap_1.1.0        
 [34] lazyeval_0.2.2        later_1.3.0           htmltools_0.5.2      
 [37] tools_4.1.2           igraph_1.2.9          gtable_0.3.0         
 [40] glue_1.5.1            RANN_2.6.1            reshape2_1.4.4       
 [43] Rcpp_1.0.7            carData_3.0-4         jquerylib_0.1.4      
 [46] vctrs_0.3.8           nlme_3.1-152          lmtest_0.9-39        
 [49] xfun_0.28             stringr_1.4.0         globals_0.14.0       
 [52] mime_0.12             miniUI_0.1.1.1        lifecycle_1.0.1      
 [55] irlba_2.3.5           goftest_1.2-3         rstatix_0.7.0        
 [58] future_1.23.0         MASS_7.3-54           zoo_1.8-9            
 [61] scales_1.1.1          vroom_1.5.7           spatstat.core_2.3-2  
 [64] hms_1.1.1             promises_1.2.0.1      spatstat.utils_2.3-0 
 [67] parallel_4.1.2        yaml_2.2.1            reticulate_1.22      
 [70] pbapply_1.5-0         gridExtra_2.3         sass_0.4.0           
 [73] rpart_4.1-15          stringi_1.7.6         highr_0.9            
 [76] rlang_0.4.12          pkgconfig_2.0.3       evaluate_0.14        
 [79] lattice_0.20-45       ROCR_1.0-11           purrr_0.3.4          
 [82] tensor_1.5            htmlwidgets_1.5.4     bit_4.0.4            
 [85] cowplot_1.1.1         tidyselect_1.1.1      parallelly_1.29.0    
 [88] RcppAnnoy_0.0.19      plyr_1.8.6            magrittr_2.0.1       
 [91] R6_2.5.1              generics_0.1.1        DBI_1.1.1            
 [94] mgcv_1.8-38           pillar_1.6.4          whisker_0.4          
 [97] withr_2.4.3           fitdistrplus_1.1-6    survival_3.2-13      
[100] abind_1.4-5           tibble_3.1.6          future.apply_1.8.1   
[103] crayon_1.4.2          car_3.0-12            KernSmooth_2.23-20   
[106] utf8_1.2.2            spatstat.geom_2.3-1   plotly_4.10.0        
[109] tzdb_0.2.0            rmarkdown_2.11        grid_4.1.2           
[112] data.table_1.14.2     git2r_0.29.0          digest_0.6.29        
[115] xtable_1.8-4          tidyr_1.1.4           httpuv_1.6.3         
[118] munsell_0.5.0         viridisLite_0.4.0     bslib_0.3.1          

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:   /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

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] SeuratObject_4.0.4      Seurat_4.0.5            sparseMatrixStats_1.4.2
 [4] MatrixGenerics_1.4.3    matrixStats_0.61.0      scattermore_0.7        
 [7] sctransform_0.3.2.9008  RColorBrewer_1.1-2      patchwork_1.1.1        
[10] here_1.0.1              ggridges_0.5.3          ggpubr_0.4.0           
[13] ggplot2_3.3.5           dplyr_1.0.7             workflowr_1.6.2        

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_1.0-6         
  [4] ggsignif_0.6.3        ellipsis_0.3.2        rprojroot_2.0.2      
  [7] fs_1.5.2              spatstat.data_2.1-0   farver_2.1.0         
 [10] leiden_0.3.9          listenv_0.8.0         bit64_4.0.5          
 [13] ggrepel_0.9.1         fansi_0.5.0           codetools_0.2-18     
 [16] splines_4.1.2         knitr_1.36            polyclip_1.10-0      
 [19] jsonlite_1.7.2        broom_0.7.10          ica_1.0-2            
 [22] cluster_2.1.2         png_0.1-7             uwot_0.1.11          
 [25] shiny_1.7.1           spatstat.sparse_2.0-0 readr_2.1.1          
 [28] compiler_4.1.2        httr_1.4.2            backports_1.4.1      
 [31] assertthat_0.2.1      Matrix_1.4-0          fastmap_1.1.0        
 [34] lazyeval_0.2.2        later_1.3.0           htmltools_0.5.2      
 [37] tools_4.1.2           igraph_1.2.9          gtable_0.3.0         
 [40] glue_1.5.1            RANN_2.6.1            reshape2_1.4.4       
 [43] Rcpp_1.0.7            carData_3.0-4         jquerylib_0.1.4      
 [46] vctrs_0.3.8           nlme_3.1-152          lmtest_0.9-39        
 [49] xfun_0.28             stringr_1.4.0         globals_0.14.0       
 [52] mime_0.12             miniUI_0.1.1.1        lifecycle_1.0.1      
 [55] irlba_2.3.5           goftest_1.2-3         rstatix_0.7.0        
 [58] future_1.23.0         MASS_7.3-54           zoo_1.8-9            
 [61] scales_1.1.1          vroom_1.5.7           spatstat.core_2.3-2  
 [64] hms_1.1.1             promises_1.2.0.1      spatstat.utils_2.3-0 
 [67] parallel_4.1.2        yaml_2.2.1            reticulate_1.22      
 [70] pbapply_1.5-0         gridExtra_2.3         sass_0.4.0           
 [73] rpart_4.1-15          stringi_1.7.6         highr_0.9            
 [76] rlang_0.4.12          pkgconfig_2.0.3       evaluate_0.14        
 [79] lattice_0.20-45       ROCR_1.0-11           purrr_0.3.4          
 [82] tensor_1.5            htmlwidgets_1.5.4     bit_4.0.4            
 [85] cowplot_1.1.1         tidyselect_1.1.1      parallelly_1.29.0    
 [88] RcppAnnoy_0.0.19      plyr_1.8.6            magrittr_2.0.1       
 [91] R6_2.5.1              generics_0.1.1        DBI_1.1.1            
 [94] mgcv_1.8-38           pillar_1.6.4          whisker_0.4          
 [97] withr_2.4.3           fitdistrplus_1.1-6    survival_3.2-13      
[100] abind_1.4-5           tibble_3.1.6          future.apply_1.8.1   
[103] crayon_1.4.2          car_3.0-12            KernSmooth_2.23-20   
[106] utf8_1.2.2            spatstat.geom_2.3-1   plotly_4.10.0        
[109] tzdb_0.2.0            rmarkdown_2.11        grid_4.1.2           
[112] data.table_1.14.2     git2r_0.29.0          digest_0.6.29        
[115] xtable_1.8-4          tidyr_1.1.4           httpuv_1.6.3         
[118] munsell_0.5.0         viridisLite_0.4.0     bslib_0.3.1