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library(tibble)
library(dplyr)
library(ggupset)
library(ggplot2)
library(tidyr)
library(SingleCellExperiment)
library(logisticPCA)
library(pals)
library(topconfects)
library(patchwork)
library(ggrepel)
library(purrr)
library(scater)
source(here::here("code", "top-genes.R"))
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))
concordance_data <- retrieve_real_data_parameters() %>% 
  filter(data_id == "pbmc3k") %>% 
  rowwise() %>% 
  mutate(mgs = list(readRDS(full_filename)$result)) %>%
  mutate(mgs = list(split(mgs, mgs$cluster))) %>% 
  ungroup() %>% 
  unnest(cols = mgs) %>% 
  rowwise() %>% 
  mutate(cluster = mgs$cluster[[1]]) %>% 
  ungroup()
 
poor_methods <- c("random", "binom", "difference", "lm")

Upset plot

concordance_data %>% 
  filter(!(method %in% poor_methods)) %>% 
  filter(data_id == "pbmc3k" & cluster == "Platelet") %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  rowwise() %>% 
  mutate(mgs = list(dplyr::slice(mgs, 1:10)$gene)) %>% 
  unnest(col = mgs) %>% 
  nest_by(mgs) %>% 
  mutate(data = list(data[["pars"]])) %>% 
  ungroup()  %>%
  ggplot(aes(x = data)) +
  geom_bar() +
  scale_x_upset(n_intersections = 15) + 
  labs(
    x = "",
    y = "Number of genes"
  )
Warning: Removed 26 rows containing non-finite values (stat_count).

Tried on all genes, but convergence issues in logisticPCA.

long_data <- concordance_data %>% 
  filter(!(method %in% poor_methods)) %>% 
  rowwise() %>% 
  filter(data_id == "pbmc3k", cluster == "B") %>% 
  mutate(mgs = list(get_top_sel_mgs(mgs, n = 100)$gene)) %>% 
  ungroup() %>% 
  unnest_longer(col = mgs) %>% 
  # Not sure why this occurs.
  filter(!is.na(mgs))

binary_data <- model.matrix(~ 0 + . , data = long_data["mgs"])

cluster_data <- cbind(
  pars = long_data$pars, 
  as.data.frame(binary_data)
) %>% 
  group_by(pars) %>% 
  summarise(across(everything(), sum))

cluster_mat <- as.matrix(cluster_data[, -1])

# Sanity checking.
#rowSums(cluster_mat)
#colSums(cluster_mat)
#max(cluster_mat)

Clustering: PCA

pca <- prcomp(cluster_mat)
pca_data <- tibble(
  pc1 = pca$x[, 1], 
  pc2 = pca$x[, 2], 
  pars = cluster_data$pars,
  plot_pars = pars_lookup[cluster_data$pars]
) %>% 
  rowwise() %>% 
  mutate(method = strsplit(pars, "[_]")[[1]][1])

ggplot(pca_data, aes(x = pc1, y = pc2, colour = method)) + 
  geom_point() + 
  labs(
    x = "PC1", 
    y = "PC2",
    colour = "Method"
  ) + 
  theme_bw()

ggplot(pca_data, aes(x = pc1, y = pc2, label = plot_pars, colour = method)) + 
  geom_point(size = 2) + 
  geom_label_repel(colour = "black", max.overlaps = 20) + 
  labs(
    x = "PC 1", 
    y = "PC 2",
    colour = "Method"
  ) + 
  theme_bw()

ggplot(pca_data, aes(x = pc1, y = pc2, label = plot_pars, colour = method)) + 
  geom_point(size = 3) + 
  geom_text_repel(
    aes(label = plot_pars), 
    colour = "black", 
    max.overlaps = 20) + 
  #coord_cartesian(xlim = c(-5, 7), ylim = c(-6, 4)) + 
  labs(
    x = "PC 1", 
    y = "PC 2",
    colour = "Method"
  ) + 
  theme_bw()

Clustering: tSNE

# tsne <- Rtsne::Rtsne(cluster_mat, perplexity = 10)
# pca_data <- data.frame(
#   pc1 = pca$x[, 1], 
#   pc2 = pca$x[, 2], 
#   pars = cluster_data$pars
# )
# 
# ggplot(pca_data, aes(x = pc1, y = pc2, colour = pars)) + 
#   geom_point() + 
#   scale_colour_manual(values = unname(polychrome(33))) + 
#   theme_bw()

Clustering: Logistic PCA

log_pca <- logisticPCA(cluster_mat)
data.frame(pc1 = log_pca$PCs[, 1], pc2 = log_pca$PCs[, 2], 
           pars = cluster_data$pars) %>% 
  ggplot(aes(pc1, pc2, colour = pars)) + 
  geom_point() + 
  #scale_colour_manual(values = unname(polychrome(39))) + 
  theme_bw()

scanpy_t <- readRDS(
  here::here("results", "real_data", "pbmc3k-scanpy_t_rankby_raw.rds")
)

seurat_t <- readRDS(
  here::here("results", "real_data", "pbmc3k-seurat_t.rds")
)

scanpy_wilcox <- readRDS(
  here::here("results", "real_data", "pbmc3k-scanpy_wilcoxon_rankby_raw.rds")
)

scanpy_wilcox_tc <- readRDS(
  here::here("results", "real_data", "pbmc3k-scanpy_wilcoxontiecorrect_rankby_raw.rds")
)

seurat_wilcox <- readRDS(
  here::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
)

scran_t_any <- readRDS(
  here::here("results", "real_data", "pbmc3k-scran_t_any.rds")
)
scran_t_any <- readRDS(
  here::here("results", "real_data", "pbmc3k-scran_t_any.rds")
)

seurat_wilcox <- readRDS(
  here::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
)

n <- 20
top_scran_t_any <- scran_t_any %>%
  pluck("result") %>% 
  filter(cluster == "NK") %>% 
  get_top_sel_mgs(n = n)

top_seurat_wilcox <- seurat_wilcox %>%
  pluck("result") %>% 
  filter(cluster == "NK") %>% 
  get_top_sel_mgs(n = n)

scran_vs_seurat_plot <- rank_rank_plot(
  top_scran_t_any$gene,
  top_seurat_wilcox$gene, 
  label1 = "Scran default", 
  label2 = "Seurat default"
) +
  ggtitle("Scran vs Seurat")

scran_vs_seurat_plot

scanpy_t_raw <- readRDS(
  here::here("results", "real_data", "pbmc3k-scanpy_t_rankby_raw.rds")
)

seurat_wilcox <- readRDS(
  here::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
)

n <- 20
top_scanpy_t_raw <- scanpy_t_raw %>%
  pluck("raw_result") %>% 
  filter(cluster == "NK") %>% 
  get_top_sel_mgs(n = n)

top_seurat_wilcox <- seurat_wilcox %>%
  pluck("result") %>% 
  filter(cluster == "NK") %>% 
  get_top_sel_mgs(n = n)

seurat_vs_scanpy_plot <- rank_rank_plot(
  top_seurat_wilcox$gene, 
  top_scanpy_t_raw$gene,
  label1 = "Seurat default", 
  label2 = "Scanpy default"
) + 
  ggtitle("Seurat vs Scanpy")

seurat_vs_scanpy_plot 

wrap_plots(scran_vs_seurat_plot, seurat_vs_scanpy_plot) + 
  plot_annotation(tag_levels = "A")

n <- 40
top_scanpy_wilcox_tc <- scanpy_wilcox_tc %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n)

top_seurat_wilcox <- seurat_wilcox %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n)

top_seurat_wilcox %>% 
  mutate(rank = 1:n()) %>% 
  filter(gene == "NKG7") %>% 
  select(-raw_statistic)
# A tibble: 0 × 7
# … with 7 variables: p_value <dbl>, p_value_adj <dbl>, cluster <fct>,
#   log_fc <dbl>, gene <chr>, scaled_statistic <dbl>, rank <int>
top_scanpy_wilcox <- scanpy_wilcox %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n)

top_scanpy_t_processed <- scanpy_t %>%
  pluck("result") %>% 
  filter(cluster == "NK") %>% 
  get_top_sel_mgs(n = n)

top_scanpy_t_raw <- scanpy_t %>%
  pluck("raw_result") %>% 
  filter(cluster == "B")

top_seurat_t <- seurat_t %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n, direction = "both")

top_scran_t_any <- scran_t_any %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n, direction = "both")

top_scran_t_any <- scran_t_any %>%
  pluck("result") %>% 
  filter(cluster == "B") %>% 
  get_top_sel_mgs(n = n, direction = "both")
rank_rank_plot(
  top_scanpy_wilcox_tc$gene,
  top_seurat_wilcox$gene, 
  label1 = "Scanpy Wilcoxon (tie corrected)", 
  label2 = "Seurat Wilcoxon"
)

rank_rank_plot(
  top_scanpy_wilcox$gene,
  top_seurat_wilcox$gene, 
  label1 = "Scanpy Wilcoxon", 
  label2 = "Seurat Wilcoxon"
) + 
  ggtitle("Scanpy Wilcoxon vs Seurat Wilcoxon") + 
  theme(axis.ticks.y = element_blank())

rank_rank_plot(
  top_scanpy_t_raw$gene,
  top_seurat_t$gene, 
  label1 = "Scanpy t", 
  label2 = "Seurat t"
)

zeisel_seurat_wilcox <- readRDS(
  here::here("results", "real_data", "zeisel-seurat_wilcox.rds")
)

zeisel_scanpy_wilcox_tc <- readRDS(
  here::here("results", "real_data", "zeisel-scanpy_wilcoxontiecorrect_rankby_raw.rds")
)

n <- 40
zeisel_top_scanpy_wilcox_tc <- zeisel_scanpy_wilcox_tc %>%
  pluck("result") %>% 
  filter(cluster == "oligodendrocytes") %>% 
  get_top_sel_mgs(n = n)

zeisel_top_seurat_wilcox <- zeisel_seurat_wilcox %>%
  pluck("result") %>% 
  filter(cluster == "oligodendrocytes") %>% 
  get_top_sel_mgs(n = n)

rank_rank_plot(
  zeisel_top_seurat_wilcox$gene,
  zeisel_top_scanpy_wilcox_tc$gene, 
  label1 = "Seurat Wilcoxon", 
  label2 = "Scanpy Wilcoxon (tie corrected)"
)

pbmc3k <- readRDS(here::here("data", "real_data", "pbmc3k.rds"))
plotExpression(pbmc3k, x = "label", "HLA-DRA")

plotExpression(pbmc3k, x = "label", "CD74")

top_scanpy_t_raw
# A tibble: 2,000 × 7
   cluster gene     scaled_statistic   p_value p_value_adj log_fc raw_statistic
   <chr>   <chr>               <dbl>     <dbl>       <dbl>  <dbl>         <dbl>
 1 B       CD74                 79.0 0           0           4.07             0
 2 B       HLA-DRA              74.7 0           0           4.89             0
 3 B       CD79A                52.3 4.61e-169   1.32e-166   7.78             0
 4 B       HLA-DPB1             52.1 1.59e-257   1.06e-254   4.04             0
 5 B       HLA-DRB1             47.9 1.45e-233   7.26e-231   3.88             0
 6 B       CD79B                44.0 1.57e-152   3.15e-150   5.54             0
 7 B       HLA-DPA1             43.5 2.36e-201   7.86e-199   3.68             0
 8 B       HLA-DQA1             38.8 1.31e-135   2.01e-133   5.34             0
 9 B       MS4A1                36.4 3.65e-122   4.30e-120   6.42             0
10 B       HLA-DQB1             33.9 4.98e-117   5.25e-115   4.97             0
# … with 1,990 more rows
top_scanpy_wilcox_tc
# A tibble: 40 × 7
   cluster gene      scaled_statistic   p_value p_value_adj log_fc raw_statistic
   <chr>   <chr>                <dbl>     <dbl>       <dbl>  <dbl>         <dbl>
 1 B       CD79A                 43.9 0           0           7.78             0
 2 B       MS4A1                 39.7 0           0           6.42             0
 3 B       CD79B                 35.4 2.65e-274   1.77e-271   5.54             0
 4 B       LINC00926             35.3 2.39e-272   1.20e-269   7.50             0
 5 B       TCL1A                 35.2 9.46e-271   3.78e-268   6.94             0
 6 B       HLA-DQA1              34.9 2.94e-266   9.79e-264   5.34             0
 7 B       VPREB3                32.9 2.74e-237   7.82e-235   7.48             0
 8 B       HLA-DQB1              32.4 1.17e-229   2.93e-227   4.97             0
 9 B       CD74                  29.0 5.25e-185   1.17e-182   4.07             0
10 B       HLA-DRA               28.9 1.95e-183   3.90e-181   4.89             0
# … with 30 more rows
top_scanpy_wilcox
# A tibble: 40 × 7
   cluster gene     scaled_statistic   p_value p_value_adj log_fc raw_statistic
   <chr>   <chr>               <dbl>     <dbl>       <dbl>  <dbl>         <dbl>
 1 B       CD74                 29.0 2.58e-184   5.16e-181   4.07             0
 2 B       CD79A                27.9 4.51e-171   4.51e-168   7.78             0
 3 B       HLA-DRA              27.6 5.15e-168   3.43e-165   4.89             0
 4 B       CD79B                26.6 4.93e-156   2.46e-153   5.54             0
 5 B       HLA-DPB1             25.8 4.01e-147   1.60e-144   4.04             0
 6 B       HLA-DQA1             25.2 4.33e-140   1.44e-137   5.34             0
 7 B       MS4A1                25.2 1.25e-139   3.56e-137   6.42             0
 8 B       HLA-DRB1             24.3 4.73e-130   1.12e-127   3.88             0
 9 B       HLA-DQB1             24.3 5.03e-130   1.12e-127   4.97             0
10 B       CD37                 24.1 3.28e-128   6.56e-126   2.46             0
# … with 30 more rows
plotExpression(pbmc3k, x = "label", "CD74")

plotExpression(pbmc3k, x = "label", "CD79A")

plotExpression(pbmc3k, x = "label", "PF4")


devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.1.0 (2021-05-18)
 os       Red Hat Enterprise Linux    
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_AU.UTF-8                 
 ctype    en_AU.UTF-8                 
 tz       Australia/Melbourne         
 date     2022-02-08                  

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version  date       lib source        
 assertthat             0.2.1    2019-03-21 [2] CRAN (R 4.1.0)
 beachmat               2.10.0   2021-10-26 [1] Bioconductor  
 beeswarm               0.4.0    2021-06-01 [2] CRAN (R 4.1.0)
 Biobase              * 2.54.0   2021-10-26 [1] Bioconductor  
 BiocGenerics         * 0.40.0   2021-10-26 [1] Bioconductor  
 BiocNeighbors          1.12.0   2021-10-26 [1] Bioconductor  
 BiocParallel           1.28.3   2021-12-09 [1] Bioconductor  
 BiocSingular           1.10.0   2021-10-26 [1] Bioconductor  
 bitops                 1.0-7    2021-04-24 [2] CRAN (R 4.1.0)
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 cli                    3.1.0    2021-10-27 [1] CRAN (R 4.1.0)
 colorspace             2.0-2    2021-06-24 [1] CRAN (R 4.1.0)
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 evaluate               0.14     2019-05-28 [2] CRAN (R 4.1.0)
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 generics               0.1.1    2021-10-25 [1] CRAN (R 4.1.0)
 GenomeInfoDb         * 1.30.0   2021-10-26 [1] Bioconductor  
 GenomeInfoDbData       1.2.7    2021-12-03 [1] Bioconductor  
 GenomicRanges        * 1.46.1   2021-11-18 [1] Bioconductor  
 ggbeeswarm             0.6.0    2017-08-07 [2] CRAN (R 4.1.0)
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 here                   1.0.1    2020-12-13 [1] CRAN (R 4.1.0)
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 pals                 * 1.7      2021-04-17 [1] CRAN (R 4.1.0)
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 pillar                 1.6.4    2021-10-18 [1] CRAN (R 4.1.0)
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 pkgconfig              2.0.3    2019-09-22 [2] CRAN (R 4.1.0)
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 processx               3.5.2    2021-04-30 [2] CRAN (R 4.1.0)
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 purrr                * 0.3.4    2020-04-17 [2] CRAN (R 4.1.0)
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 Rcpp                   1.0.7    2021-07-07 [1] CRAN (R 4.1.0)
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[1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
[2] /usr/local/easybuild-2019/easybuild/software/mpi/gcc/10.2.0/openmpi/4.0.5/r/4.1.0/lib64/R/library