18 KiB
18 KiB
08.compare_mce_maier
Roberto Olayo Alarcon 2024-10-02
In this file we prepare the final figures from the data analysed in
08.compare_mce_maier.ipynb.
Prepare directories.
OUTPUT_DIR <- "../data/08.compare_mce_maier/"
Read data.
Here we read the UMAP coordinates.
# UMAP based on MolE representation
mole.umap <- read_tsv(file.path(OUTPUT_DIR, "mole_joint_umap.tsv.gz"),
show_col_types = FALSE)
# UMAP based on ECFP4 representation
ecfp4.umap <- read_tsv(file.path(OUTPUT_DIR, "ecfp4_joint_umap.tsv.gz"),
show_col_types = FALSE)
# Complete prediction set
completepreds <- read_excel("../data/04.new_predictions/mole_mce_predictions_litsearch.xlsx", sheet = "mole_prediction_overview")
# Literature search data.
litsearch.mole <- read_excel("../data/04.new_predictions/mole_mce_predictions_litsearch.xlsx", sheet = "mole_over10")
mce_litexamples <- litsearch.mole %>%
filter(!is.na(`Reported Activity`),
antibiotic == "not_abx") %>%
select(`Catalog Number`, `Reported Activity`) %>%
rename("chem_id" = "Catalog Number")
mce_litexamples %>% head()
## # A tibble: 6 × 2
## chem_id `Reported Activity`
## <chr> <chr>
## 1 HY-N0797 Antibacterial
## 2 HY-N0835 Antibacterial
## 3 HY-16974 Antiparasitic
## 4 HY-13948A Antiplasmodium
## 5 HY-13553 Antifungal
## 6 HY-P0017 Antibacterial
Plot UMAP.
Plot the UMAP, highlighting some chemicals that are predicted to be broad-spectrum and confirmed in the literature.
# Chemicals of interest
coi_df <- data.frame("chem_id" = c("HY-B0183", "HY-B0021", "HY-N0829", "HY-B0723", "HY-B2136" ),
"ProductName" = c("Ellagic acid", "Doxifluridine", "Shionone", "Ospemifene", "Tannic acid"))
coi_df
## chem_id ProductName
## 1 HY-B0183 Ellagic acid
## 2 HY-B0021 Doxifluridine
## 3 HY-N0829 Shionone
## 4 HY-B0723 Ospemifene
## 5 HY-B2136 Tannic acid
mce_litexamples.umap <- mce_litexamples %>%
left_join(mole.umap, by="chem_id") %>%
mutate(`Chemical Library` = "MCE (Predictions with reported activity)")
coi.mole <- coi_df %>%
left_join(mole.umap, by="chem_id")
mole.umap <- mole.umap %>%
mutate(keep = case_when(`Chemical Library` == "Maier et.al." ~ TRUE,
`Chemical Library` == "MCE" & chem_id %in% completepreds$`Catalog Number` ~ TRUE,
TRUE ~ FALSE)) %>%
filter(keep)
u.mole <- ggplot(mole.umap, aes(x=umap1, y=umap2, fill=`Chemical Library`)) +
geom_point(size=2, color="white", shape=21, alpha=0.75) +
geom_point(data = mce_litexamples.umap, color="white", size=2.5, shape=21, alpha=0.75) +
geom_text_repel(data = coi.mole, aes(label=ProductName),
max.overlaps = Inf, size=3, min.segment.length = 0,
color="black", fontface="plain",
box.padding = 0.5,
nudge_x = if_else(coi.mole$ProductName %in% c("Ellagic acid", "Tannic acid", "Ospemifene"), 1, 0),
nudge_y = case_when(coi.mole$ProductName %in% c("Ospemifene", "Shionone") ~ 3,
coi.mole$ProductName %in% c("Doxifluridine") ~ -2,
coi.mole$ProductName %in% c("Tannic acid") ~ 2,
TRUE ~ 0)) +
scale_fill_manual(values=c("#DE1F84", "#C5C5C5", "#1F9DBB")) +
theme_bw() +
theme(legend.position = "bottom",
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
panel.background = element_rect(fill = "transparent", color=NA),
plot.background = element_rect(fill = "transparent", colour = NA)) +
labs(x="UMAP 1",
y="UMAP 2")
u.mole
ggsave(filename = file.path(OUTPUT_DIR, "mole_joint_umap.pdf"), plot=u.mole, dpi = 300,
height = 15, width = 21, units="cm")
ggsave(filename = file.path(OUTPUT_DIR, "mole_joint_umap.png"), plot=u.mole, dpi = 300,
height = 15, width = 21, units="cm")
mce_metadata <- completepreds %>%
select(`Catalog Number`, ProductName, nk_total, antibiotic) %>%
rename("chem_id" = "Catalog Number") %>%
left_join(mce_litexamples, by="chem_id")
mole.umap %>%
left_join(mce_metadata, by="chem_id") %>%
select(-keep) %>%
rename("n_predicted_inhibited_strains" = "nk_total") %>%
write_tsv("../data/08.compare_mce_maier/umap_library_comparison.tsv")
The same but for ECFP4.
mce_litexamples.umap <- mce_litexamples %>%
left_join(ecfp4.umap, by="chem_id") %>%
mutate(`Chemical Library` = "MCE (Predictions with reported activity)")
coi.ecfp4 <- coi_df %>%
left_join(ecfp4.umap, by="chem_id")
u.ecfp4 <- ggplot(ecfp4.umap, aes(x=umap1, y=umap2, fill=`Chemical Library`)) +
geom_point(size=1.5, color="white", shape=21, alpha=0.75) +
geom_point(data = mce_litexamples.umap, color="white", size=2.5, shape=21, alpha=0.75) +
geom_text_repel(data = coi.ecfp4, aes(label=ProductName),
max.overlaps = Inf, size=3, min.segment.length = 0,
color="black", fontface="plain",
box.padding = 0.5,
nudge_y = if_else(coi.mole$ProductName %in% c("Ospemifene", "Tannic acid"), -5, 0),
nudge_x = if_else(coi.mole$ProductName %in% c("Doxifluridine", "Ellagic acid"), -3, 0)) +
scale_fill_manual(values=c("#DE1F84", "#C5C5C5","#1F9DBB")) +
theme_bw() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
panel.background = element_rect(fill = "transparent", color=NA),
plot.background = element_rect(fill = "transparent", colour = NA)) +
labs(x="UMAP 1",
y="UMAP 2")
u.ecfp4
ggsave(filename = file.path(OUTPUT_DIR, "ecfp4_joint_umap.pdf"), plot=u.ecfp4, dpi = 300,
height = 13, width = 19, units="cm")
Comparing MolE and ECFP4
library(ggpubr)
umap.arranged <- ggarrange(u.mole + ggtitle("MolE"),
u.ecfp4 + ggtitle("ECFP4"),
common.legend = TRUE,
legend="bottom")
umap.arranged
ggsave(filename = file.path(OUTPUT_DIR, "joint_umap_panel.pdf"), plot=umap.arranged, dpi = 300,
height = 11, width = 20, units="cm")
Comparing molecular similarity.
# Read molecular similarity
mol.similarity <- read_tsv("../data/08.compare_mce_maier/ecfp4_distance_to_maier.tsv.gz", show_col_types = FALSE) %>%
# Make tidy form to ease handling
pivot_longer(cols = -selected_chem, names_to = "prestwick_ID", values_to = "jaccard_distance") %>%
# Get tanimoto similarity
mutate(tanimoto_similarity = 1-jaccard_distance) %>%
# Gather the ranking per molecule. Make sure to rank ties the same
group_by(selected_chem) %>%
mutate(similarity_ranking = rank(-tanimoto_similarity, ties.method = "min"))
# Read prestwick metadata
maier_chems_metadata = read_excel("../raw_data/maier_microbiome/chem_library_info_SF1.xlsx") %>%
select(prestwick_ID, `chemical name`, `target species`) %>%
rename("prestwick_chemname" = "chemical name",
"target_species" = "target species")
# Read screening results
maier_screening_results <- read_tsv("../data/01.prepare_training_data/maier_screening_results.tsv.gz", show_col_types = FALSE) %>%
# Gather chemicals used in training
filter(prestwick_ID %in% unique(mol.similarity$prestwick_ID)) %>%
# Count number of strains inhibited
pivot_longer(cols = -prestwick_ID, names_to = "strain", values_to = "growth_inhibition") %>%
group_by(prestwick_ID) %>%
summarise(n_inhibited_strains = sum(growth_inhibition)) %>%
ungroup()
# Join information
mol.similarity <- mol.similarity %>%
left_join(maier_chems_metadata, by="prestwick_ID") %>%
left_join(maier_screening_results, by="prestwick_ID")
Determine the most similar molecule percategory
# Overall
overall.most_similar <- mol.similarity %>%
filter(similarity_ranking == 1) %>%
# Thymidine has two molecules with equal ranking
head(6) %>%
mutate(sim_category = "Overall")
# Most similar antibiotic
abx.most_similar <- mol.similarity %>%
filter(target_species == "bacteria") %>%
group_by(selected_chem) %>%
filter(similarity_ranking == min(similarity_ranking)) %>%
mutate(sim_category = "Antibiotic")
# Most similar non-antibiotic with broad spectrum activity
nonabx.bsa.most_similar <- mol.similarity %>%
filter(target_species != "bacteria",
n_inhibited_strains >= 10) %>%
group_by(selected_chem) %>%
filter(similarity_ranking == min(similarity_ranking)) %>%
mutate(sim_category = "Non-antibiotic with BSA")
most_similar.df <- bind_rows(overall.most_similar, abx.most_similar, nonabx.bsa.most_similar) %>%
select(selected_chem, prestwick_ID, tanimoto_similarity, similarity_ranking, target_species, n_inhibited_strains, sim_category) %>%
# For some compounds, the same molecule is the most similar in more than one category. Let's choose the more specific annotation.
group_by(selected_chem, prestwick_ID) %>%
slice_tail() %>%
# Prepare labelling
mutate(tansim_label = round(tanimoto_similarity, 2),
sim_category = factor(sim_category, levels=c("Overall", "Antibiotic", "Non-antibiotic with BSA")))
most_similar.df
## # A tibble: 15 × 8
## # Groups: selected_chem, prestwick_ID [15]
## selected_chem prestwick_ID tanimoto_similarity similarity_ranking
## <chr> <chr> <dbl> <int>
## 1 Cetrorelix Prestw-301 0.276 1
## 2 Cetrorelix Prestw-408 0.182 13
## 3 Cetrorelix Prestw-919 0.273 2
## 4 Ebastine Prestw-1009 0.235 59
## 5 Ebastine Prestw-138 0.444 2
## 6 Ebastine Prestw-707 0.481 1
## 7 Elvitegravir Prestw-1303 0.3 1
## 8 Elvitegravir Prestw-205 0.230 14
## 9 Opicapone Prestw-1403 0.257 1
## 10 Opicapone Prestw-81 0.194 3
## 11 Thymidine Prestw-1415 0.744 1
## 12 Thymidine Prestw-544 0.222 17
## 13 Visomitin Prestw-1078 0.179 46
## 14 Visomitin Prestw-1288 0.255 1
## 15 Visomitin Prestw-270 0.224 4
## # ℹ 4 more variables: target_species <chr>, n_inhibited_strains <dbl>,
## # sim_category <fct>, tansim_label <dbl>
similarity.chosen <- ggplot(mol.similarity, aes(x=similarity_ranking, y=tanimoto_similarity)) +
geom_hline(yintercept = 0.3, linetype="longdash") +
geom_point(size=1.8, color="white", shape=21, alpha=1, fill="#C5C5C5") +
geom_point(data=most_similar.df, aes(fill=sim_category, shape=sim_category), size=2,
color="black", shape=21, alpha=0.75) +
scale_fill_manual(values=c("#377eb8", "#e41a1c", "#1b9e77")) +
geom_text_repel(data = most_similar.df, aes(label=tansim_label),
max.overlaps = Inf, size=3.5,
min.segment.length = 0,
color="black",
fontface="plain",
point.padding = 0.2,
box.padding = 0.2,
segment.size=0.3,
nudge_y = case_when(most_similar.df$selected_chem == "Cetrorelix" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ -0.125,
most_similar.df$selected_chem == "Cetrorelix" & most_similar.df$sim_category == "Overall" ~ 0.125,
most_similar.df$selected_chem == "Cetrorelix" & most_similar.df$sim_category == "Antibiotic" ~ -0.05,
most_similar.df$selected_chem == "Elvitegravir" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ -0.15,
most_similar.df$selected_chem == "Elvitegravir" & most_similar.df$sim_category == "Antibiotic" ~ 0.1255,
most_similar.df$selected_chem == "Opicapone" & most_similar.df$sim_category == "Antibiotic" ~ 0.03,
most_similar.df$selected_chem == "Opicapone" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ 0.15,
most_similar.df$selected_chem == "Visomitin" & most_similar.df$sim_category == "Overall" ~ 0.15,
most_similar.df$selected_chem == "Visomitin" & most_similar.df$sim_category == "Antibiotic" ~ -0.125,
most_similar.df$selected_chem == "Ebastine" & most_similar.df$sim_category == "Antibiotic" ~ -0.15,
most_similar.df$selected_chem == "Ebastine" & most_similar.df$sim_category == "Overall" ~ 0.125,
TRUE ~ 0),
nudge_x = case_when(most_similar.df$selected_chem == "Ebastine" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ 125,
most_similar.df$selected_chem == "Ebastine" & most_similar.df$sim_category == "Overall" ~ 125,
most_similar.df$selected_chem == "Thymidine" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ 150,
most_similar.df$selected_chem == "Thymidine" & most_similar.df$sim_category == "Antibiotic" ~ 150,
most_similar.df$selected_chem == "Opicapone" & most_similar.df$sim_category == "Antibiotic" ~ 150,
most_similar.df$selected_chem == "Elvitegravir" & most_similar.df$sim_category == "Antibiotic" ~ 100,
most_similar.df$selected_chem == "Visomitin" & most_similar.df$sim_category == "Non-antibiotic with BSA" ~ 180,
most_similar.df$selected_chem == "Cetrorelix" & most_similar.df$sim_category == "Antibiotic" ~ 125,
TRUE ~ 0)) +
facet_wrap(~selected_chem) +
theme_bw() +
theme(legend.position = "bottom",
axis.text.x = element_text(size=6)) +
labs(x="Ranking of training set compounds",
y="Tanimoto similarity",
fill = "Similarity category")
similarity.chosen
ggsave(plot=similarity.chosen, filename = "../data/08.compare_mce_maier/similarity_to_maier.png",
dpi = 300, width = 21, height = 12.5, units="cm")
ggsave(plot=similarity.chosen, filename = "../data/08.compare_mce_maier/similarity_to_maier.pdf",
dpi = 300, width = 21, height = 12.5, units="cm")
most_similar_metadata <- most_similar.df %>%
unite("selected_prest", c(selected_chem, prestwick_ID), sep=":") %>%
select(selected_prest, sim_category)
mol.similarity %>%
unite("selected_prest", c(selected_chem, prestwick_ID), sep=":", remove=FALSE) %>%
left_join(most_similar_metadata, by="selected_prest") %>%
select(-selected_prest) %>%
arrange(selected_chem, similarity_ranking) %>%
rename("measured_n_inhibited_strains" = "n_inhibited_strains",
"most_similar_category" = "sim_category") %>%
write_tsv("../data/08.compare_mce_maier/selected_prestwick_tanimoto.tsv")
Session Info:
sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.6.0 readxl_1.4.2 ggrepel_0.9.3 lubridate_1.9.2
## [5] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.2
## [9] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
## [13] tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.4 generics_0.1.3 rstatix_0.7.2 stringi_1.7.12
## [5] hms_1.1.3 digest_0.6.33 magrittr_2.0.3 evaluate_0.21
## [9] grid_4.3.1 timechange_0.2.0 fastmap_1.1.1 cellranger_1.1.0
## [13] backports_1.4.1 gridExtra_2.3 fansi_1.0.6 scales_1.3.0
## [17] textshaping_0.3.6 abind_1.4-5 cli_3.6.3 rlang_1.1.4
## [21] crayon_1.5.2 cowplot_1.1.1 bit64_4.0.5 munsell_0.5.0
## [25] withr_2.5.2 yaml_2.3.7 tools_4.3.1 parallel_4.3.1
## [29] tzdb_0.4.0 ggsignif_0.6.4 colorspace_2.1-0 broom_1.0.5
## [33] vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4 car_3.1-2
## [37] bit_4.0.5 vroom_1.6.3 ragg_1.2.5 pkgconfig_2.0.3
## [41] pillar_1.9.0 gtable_0.3.4 glue_1.7.0 Rcpp_1.0.13
## [45] systemfonts_1.0.4 highr_0.10 xfun_0.40 tidyselect_1.2.0
## [49] rstudioapi_0.15.0 knitr_1.43 farver_2.1.1 htmltools_0.5.6
## [53] carData_3.0-5 rmarkdown_2.24 labeling_0.4.3 compiler_4.3.1