-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathbenchmarking_summary_alldata.R
More file actions
128 lines (102 loc) · 8.08 KB
/
benchmarking_summary_alldata.R
File metadata and controls
128 lines (102 loc) · 8.08 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
# Read in all summary results
library(deMULTIplex2)
savePath <- "./data-raw/benchmark_res/summary_res/"; dir.create(savePath)
rdsPath <- paste0(savePath, "rds/");dir.create(rdsPath)
plotPath <- paste0(savePath, "plots/"); dir.create(plotPath)
test_text<- "benchmark_summary_realandsim_"
show_ratios <- c(1, 0.5, 0.1, 0.05, 0.01)
bc_path <- list(
"sim1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim1_n.bc_5_bc_all.rds",
"sim1_0.5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim1_n.bc_5__read_ds_0.5bc_all.rds",
"sim1_0.1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim1_n.bc_5__read_ds_0.1bc_all.rds",
"sim1_0.05" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim1_n.bc_5__read_ds_0.05bc_all.rds",
"sim1_0.01" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim1_n.bc_5__read_ds_0.01bc_all.rds",
"sim2" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim2_n.bc_10_bc_all.rds",
"sim2_0.5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim2_n.bc_10__read_ds_0.5bc_all.rds",
"sim2_0.1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim2_n.bc_10__read_ds_0.1bc_all.rds",
"sim2_0.05" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim2_n.bc_10__read_ds_0.05bc_all.rds",
"sim2_0.01" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim2_n.bc_10__read_ds_0.01bc_all.rds",
"sim3" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim3_n.bc_10_bc_all.rds",
"sim3_0.5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim3_n.bc_10__read_ds_0.5bc_all.rds",
"sim3_0.1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim3_n.bc_10__read_ds_0.1bc_all.rds",
"sim3_0.05" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim3_n.bc_10__read_ds_0.05bc_all.rds",
"sim3_0.01" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim3_n.bc_10__read_ds_0.01bc_all.rds",
"sim4" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim4_n.bc_30_bc_all.rds",
"sim4_0.5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim4_n.bc_30__read_ds_0.5bc_all.rds",
"sim4_0.1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim4_n.bc_30__read_ds_0.1bc_all.rds",
"sim4_0.05" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim4_n.bc_30__read_ds_0.05bc_all.rds",
"sim4_0.01" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim4_n.bc_30__read_ds_0.01bc_all.rds",
"sim5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim5_n.bc_30_bc_all.rds",
"sim5_0.5" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim5_n.bc_30__read_ds_0.5bc_all.rds",
"sim5_0.1" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim5_n.bc_30__read_ds_0.1bc_all.rds",
"sim5_0.05" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim5_n.bc_30__read_ds_0.05bc_all.rds",
"sim5_0.01" ="./data-raw/benchmark_res/simulation_model/rds/simulation_model_update_sim5_n.bc_30__read_ds_0.01bc_all.rds",
"McGinnis_8donor_MULTI" = "./data-raw/benchmark_res/McGinnis_8donor_MULTI/rds/McGinnis_8donor_MULTI_benchmarking_bc_all.rds",
"McGinnis_8donor_MULTI_0.5" = "./data-raw/benchmark_res/ds_McGinnis_8donor_MULTI/rds/ds_McGinnis_8donor_MULTI_0.5bc_all.rds",
"McGinnis_8donor_MULTI_0.1" = "./data-raw/benchmark_res/ds_McGinnis_8donor_MULTI/rds/ds_McGinnis_8donor_MULTI_0.1bc_all.rds",
"McGinnis_8donor_MULTI_0.05" = "./data-raw/benchmark_res/ds_McGinnis_8donor_MULTI/rds/ds_McGinnis_8donor_MULTI_0.05bc_all.rds",
"McGinnis_8donor_MULTI_0.01" = "./data-raw/benchmark_res/ds_McGinnis_8donor_MULTI/rds/ds_McGinnis_8donor_MULTI_0.01bc_all.rds",
"McGinnis_8donor_SCMK" = "./data-raw/benchmark_res/McGinnis_8donor_SCMK/rds/McGinnis_8donor_SCMK_benchmarking_bc_all.rds",
"McGinnis_8donor_SCMK_0.5" = "./data-raw/benchmark_res/ds_McGinnis_8donor_SCMK/rds/ds_McGinnis_8donor_SCMK_0.5bc_all.rds",
"McGinnis_8donor_SCMK_0.1" = "./data-raw/benchmark_res/ds_McGinnis_8donor_SCMK/rds/ds_McGinnis_8donor_SCMK_0.1bc_all.rds",
"McGinnis_8donor_SCMK_0.05" = "./data-raw/benchmark_res/ds_McGinnis_8donor_SCMK/rds/ds_McGinnis_8donor_SCMK_0.05bc_all.rds",
"McGinnis_8donor_SCMK_0.01" = "./data-raw/benchmark_res/ds_McGinnis_8donor_SCMK/rds/ds_McGinnis_8donor_SCMK_0.01bc_all.rds",
"Stoeckius_4cellline" = "./data-raw/benchmark_res/Stoeckius_4cellline/rds/benchmark_Stoeckius_4cellline_bc_all.rds",
"Stoeckius_8donor" = "./data-raw/benchmark_res/Stoeckius_8donor/rds/benchmark_Stoeckius_8donor_bc_all.rds",
"Gaublomme_8donor" = "./data-raw/benchmark_res/Gaublomme/human_st/rds/benchmark_Gaublomme_bc_all.rds",
"lung_cellline" = "./data-raw/benchmark_res/lung_cellline/rds/benchmark_lungcellline_bc_cbn.rds",
"bal_batch1" = "./data-raw/benchmark_res/BAL/rds/benchmark_bal_b1_bc_cbn.rds",
"bal_batch2" = "./data-raw/benchmark_res/BAL/rds/benchmark_bal_b2_bc_cbn.rds",
"bal_batch3" = "./data-raw/benchmark_res/BAL/rds/benchmark_bal_b3_bc_cbn.rds",
"Winkler_PDX" = "./data-raw/benchmark_res/Winkler_PDX/rds/Winkler_PDX_benchmarking_bc_cbn.rds"
)
bc_res <- lapply(bc_path, function(x) {
readRDS(x)
})
show_methods <- names(bc_res$McGinnis_8donor_SCMK)
singlet_stats <- lapply(bc_res, function(x) {
sapply(show_methods, function(cur_method) {
y = x[[cur_method]]
if(is.null(y)) return(c(NA,NA,NA)) else y$singlet_avg_stats
})
})
singlet_dfs <- lapply(1:length(singlet_stats), function(i){
x <- singlet_stats[[i]]
x <- reshape2::melt(x)
colnames(x) <- c("stats", "method", "value")
x$dataset <- names(singlet_stats)[i]
return(x)
})
singlet_df_cbn <- do.call(rbind, singlet_dfs)
singlet_df_cbn$dataset <- factor(singlet_df_cbn$dataset, levels= names(bc_res))
plot_df <- singlet_df_cbn[singlet_df_cbn$stats == "f_score", ]
g1 <- ggplot(plot_df, aes(x = dataset, y = value, fill = method)) +
geom_bar(stat="identity",position ="dodge")
ggsave(paste0(plotPath, test_text, "_barv1.pdf"), g1, width = 10, height =2)
# Heatmap
library(pheatmap)
singlet_mtx <- sapply(singlet_stats, function(x) {
x["f_score",][show_methods]
})
breaksList <- seq(0,1,by=.1)
graphics.off() # Shut down open plotting devices if any left over
pdf(paste0(plotPath, test_text, "singlet_summary_mtx", ".pdf"), width = 15, height = 5)
pheatmap(singlet_mtx, cluster_rows = F, cluster_cols = F, display_numbers =T, border_color=NA, number_format ="%.2f", number_color ='black', fontsize = 8, fontsize_number=8, color = get_gradient_color('RdBu', length(breaksList)), breaks = breaksList)
dev.off()
# Rank based heatmap
library(matrixStats)
#singlet_ranks <- t(colRanks(-round(singlet_mtx, 3), ties.method = "dense"))
#singlet_ranks <- t(colRanks(-round(singlet_mtx, 3), ties.method = "max"))
colnames(singlet_ranks) <- colnames(singlet_mtx)
rownames(singlet_ranks) <- rownames(singlet_mtx)
breaksList <- seq(0,10,by=1)
pdf(paste0(plotPath, test_text, "singlet_rank_mtx", ".pdf"), width = 15, height = 3.7)
pheatmap(singlet_ranks, cluster_rows = F, cluster_cols = F, display_numbers =T, border_color=NA, number_format ="%.0f", number_color ='black', fontsize = 8, fontsize_number=8, color = rev(get_gradient_color('RdBu', length(breaksList))), breaks = breaksList)
dev.off()
# Only show simulation with increasing noise
show_sims <- c("sim1", "sim2", "sim3", "sim4", "sim5")
singlet_ranks <- singlet_ranks[,colnames(singlet_ranks) %in% show_sims | !grepl("sim", colnames(singlet_ranks))]
breaksList <- seq(0,10,by=1)
pdf(paste0(plotPath, test_text, "singlet_rank_mtx_selected", ".pdf"), width = 7, height = 3.7)
pheatmap(singlet_ranks, cluster_rows = F, cluster_cols = F, display_numbers =T, border_color=NA, number_format ="%.0f", number_color ='black', fontsize = 8, fontsize_number=8, color = rev(get_gradient_color('RdBu', length(breaksList))), breaks = breaksList)
dev.off()