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scRNAseq_scores.R
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scRNAseq_scores.R
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source("data.R")
## Single cell scores ----
### Reyfman ----
reyfman <- readRDS("./files/markers_reyfman.RDS") %>%
filter(
cluster == "Ciliated Cells", pct.1 - pct.2 > 0,
p_val_adj < 0.001
) %>%
hgncConverter("gene") %>%
mutate(
#mean_score = normalization0(pct.1 - pct.2),
mean_score = normalization0(log(pct.1 - pct.2)),
source = "reyfman"
) %>%
rename("Gene_name" = "gene") %>%
dplyr::select(Gene_name, mean_score, source) %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
distinct()
reyfman$mean_score[reyfman$mean_score == 0] <- 0.00001
### Carraro et al. ----
carraro <- readRDS("./files/markers.RDS")
carraro <- carraro[[2]] %>%
filter(p_val_adj < 0.001, pct.1 - pct.2 > 0) %>%
mutate(
mean_score = normalization0(pct.1 - pct.2),
source = "carraro"
) %>%
tibble::rownames_to_column(var = "Gene_name") %>%
dplyr::select(Gene_name, mean_score, source) %>%
hgncConverter("Gene_name") %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
distinct()
### Habermann et al. ----
habermann <- fread("./files/banovich_markers.txt") %>%
filter(
cluster == "Ciliated" | cluster == "Differentiating Ciliated",
p_val_adj < 0.001, pct.1 - pct.2 > 0
) %>%
mutate(
mean_score = normalization0(pct.1 - pct.2),
Gene_name = gene
) %>%
dplyr::select(Gene_name, mean_score) %>%
group_by(Gene_name) %>%
mutate(mean_score = exp(mean(log(mean_score)))) %>%
hgncConverter("Gene_name") %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
mutate(source = "habermann") %>%
distinct()
# Murthy et al.
murthy <- readRDS("./files/murthy_diff.RDS")
#murthy$Gene_name<-rownames(murthy)
murthy <- murthy %>%
filter(p_val_adj < 0.001, pct.1 - pct.2 > 0) %>%
mutate(
mean_score = normalization0(pct.1 - pct.2),
#mean_score = normalization0(log(pct.1 - pct.2)),
#mean_score = pct.1 - pct.2,
source = "murthy"
) %>%
tibble::rownames_to_column(var = "Gene_name") %>%
dplyr::select(Gene_name, mean_score, source) %>%
#filter(mean_score > 0.2) %>%
hgncConverter("Gene_name") %>%
distinct()
### C. elegans single cell ----
celegans <- fread("./files/celegans_sc_ciliated_oxygen_2022.txt") %>%
filter(higher.expr == "Set 1" & q.val < 0.05 & (pct.1 - pct.2) > 0) %>%
mutate(
gene = as.character(gene),
Gene_name = cele_orthology$Gene2Symbol[match(gene, cele_orthology$Gene1Symbol)],
#mean_score = normalization0(pct.1 - pct.2)
mean_score = normalization0(log(pct.1 - pct.2))
) %>%
drop_na(Gene_name) %>%
dplyr::select(15, 16) %>%
group_by(Gene_name) %>%
mutate(
mean_score = exp(mean(log(mean_score))),
source = "celegans"
) %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
distinct()
### Trachea single cell ----
trachea <- fread("./files/trachea_all_markers.txt") %>%
filter(
cluster == "Ciliated Cells", pct.1 - pct.2 > 0,
p_val_adj < 0.001
) %>%
hgncConverter("gene_name") %>%
mutate(
mean_score = normalization0(pct.1 - pct.2),
source = "trachea"
) %>%
rename("Gene_name" = "gene_name") %>%
dplyr::select(Gene_name, mean_score, source) %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
distinct()
trachea$mean_score[trachea$mean_score == 0] <- 0.00001
trachea<-trachea[-c(1,2),]
### CenGen single cell ----
cengen <- fread("./files/difex_cengen.csv") %>%
filter(human_symbol != "") %>%
filter(
cluster == "Sensory", pct.1 - pct.2 > 0,
p_val_adj < 0.001
) %>%
hgncConverter("human_symbol") %>%
mutate(
mean_score = normalization0(log(pct.1 - pct.2)),
source = "cengen"
) %>%
rename("Gene_name" = "human_symbol") %>%
dplyr::select(Gene_name, mean_score, source) %>%
group_by(Gene_name) %>%
mutate(mean_score = max(mean_score)) %>%
distinct()
cengen$mean_score[cengen$mean_score == 0] <- 0.00001
### All Single cell ----
sc2 <- rbind(carraro, reyfman, habermann, celegans, murthy, trachea, cengen) %>%
group_by(Gene_name) %>%
mutate(ln = log(length(Gene_name) + 1)) %>%
mutate(weighted_mean_score = ln * (exp(mean(log(mean_score + 1))) - 1)) %>%
ungroup() %>%
dplyr::select(Gene_name, weighted_mean_score) %>%
distinct() %>%
mutate(weighted_mean_score2 = normalization0(weighted_mean_score))
sc2 <- sc2 %>%
mutate(score = weighted_mean_score2) %>%
dplyr::select(Gene_name, score) %>%
distinct()
single_cell_scores <- left_join(hgnc_names[, 2], sc2, by = c("Approved symbol" = "Gene_name"))
single_cell_scores$score[is.na(single_cell_scores$score)] <- 0
colnames(single_cell_scores)[1:2] <- c("Gene_name", "single_cell_score")
write.table(single_cell_scores, "single_cell_scores.txt", sep = "\t", row.names = FALSE, quote = FALSE)