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comparative_condition.R
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comparative_condition.R
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#'Read the lrobject and generate the comparative tables
#'
#'@param data LRObj with single condition
#'@param out_path output path
#'@return LRObject
#'@import dplyr
#'@importFrom tidyr %>%
create_diff_table1 <- function(data, out_path, comparison = NULL) {
if (!is.null(comparison)) {
for (pair in comparison) {
ctr_name <- pair[2]
ctr_table <- data@tables[[ctr_name]]
exp_name <- pair[1]
cmp_name <- paste0(exp_name, "_x_", ctr_name)
exp_table <- data@tables[[exp_name]]
tmp_data <- merge(exp_table, ctr_table, by = 'allpair', all = TRUE)
tmp_data <- tmp_data %>%
tidyr::separate(.data$allpair,
c('ligpair', 'recpair'),
sep = '@', remove = F)
tmp_data$LRScore.x[is.na(tmp_data$LRScore.x)] <- 0
tmp_data$LRScore.y[is.na(tmp_data$LRScore.y)] <- 0
final_data <- tmp_data %>%
dplyr::mutate(LRScore = .data$LRScore.x - .data$LRScore.y) %>%
dplyr::select(c(.data$source.x,
.data$source.y,
.data$target.x,
.data$target.y,
.data$LRScore,
.data$ligpair,
.data$recpair,
.data$allpair,
.data$type_gene_A.x,
.data$type_gene_B.x,
.data$type_gene_A.y,
.data$type_gene_B.y,
.data$gene_A.x,
.data$gene_B.x,
.data$gene_A.y,
.data$gene_B.y)) %>%
dplyr::arrange(LRScore)
final_data <- final_data[final_data$LRScore != 0,]
final_data <- final_data %>%
dplyr::mutate(type_gene_A = dplyr::coalesce(.data$type_gene_A.x,
.data$type_gene_A.y)) %>%
dplyr::mutate(type_gene_B = dplyr::coalesce(.data$type_gene_B.x,
.data$type_gene_B.y)) %>%
dplyr::mutate(gene_A = dplyr::coalesce(.data$gene_A.x,
.data$gene_A.y)) %>%
dplyr::mutate(gene_B = dplyr::coalesce(.data$gene_B.x,
.data$gene_B.y)) %>%
dplyr::mutate(source = dplyr::coalesce(.data$source.x,
.data$source.y)) %>%
dplyr::mutate(target = dplyr::coalesce(.data$target.x,
.data$target.y)) %>%
dplyr::select(-.data$type_gene_A.x,
-.data$type_gene_A.y,
-.data$type_gene_B.x,
-.data$type_gene_B.y,
-.data$gene_A.x,
-.data$gene_A.y,
-.data$gene_B.x,
-.data$gene_B.y,
-.data$source.x,
-.data$source.y,
-.data$target.x,
-.data$target.y) %>%
dplyr::mutate(cellpair = paste0(.data$source, "@",
.data$target))
final_data$interaction_type <- paste(final_data$type_gene_A, final_data$type_gene_B, sep = "")
final_data = final_data %>%
mutate(interaction_type = ifelse(interaction_type == "LigandReceptor", "LR", interaction_type))
final_data = final_data %>%
mutate(interaction_type = ifelse(interaction_type == "ReceptorTranscription Factor", "RTF", interaction_type))
final_data = final_data %>%
mutate(interaction_type = ifelse(interaction_type == "Transcription FactorLigand", "TFL", interaction_type))
data@tables[[cmp_name]] <- final_data
final <- final_data %>%
dplyr::mutate(ccitype = paste(.data$type_gene_A, .data$type_gene_B)) %>%
dplyr::filter(!(str_detect(.data$ccitype, "Transcription Factor"))) %>%
dplyr::group_by(.data$cellpair) %>%
dplyr::summarise(LRScore = sum(.data$LRScore))
final <- final %>%
tidyr::separate(.data$cellpair, c("u", "v"), sep = "@", remove = F)
final <- na.omit(final, cols = c(u, v))
filtervar <- grepl('Transcription', final_data[['type_gene_A']]) | grepl('Transcription', final_data[['type_gene_B']])
raw_inter <- table(final_data$cellpair[!filtervar])
freq <- as.array(raw_inter)[final$cellpair] - min(as.array(raw_inter)[final$cellpair])
freq <- freq / (max(as.array(raw_inter)[final$cellpair]) - min(as.array(raw_inter)[final$cellpair])) + 0.1
final$freq <- as.array(freq)[final$cellpair]
final$pair <- final$cellpair
final <- dplyr::arrange(final, final$LRScore) #abs(final$LRScore))
graph1 <- igraph::graph_from_data_frame(final[, c("u", "v", "LRScore")])
igraph::E(graph1)$inter <- final$freq #setting thickness and weight
igraph::E(graph1)$inter.raw <- as.array(raw_inter)[final$cellpair] #setting thickness and weight
igraph::E(graph1)$weight <- igraph::E(graph1)$LRScore
igraph::E(graph1)$mean <- igraph::E(graph1)$LRScore
data@graphs[[cmp_name]] <- graph1
graph1 <- igraph::graph_from_data_frame(final_data[, c("ligpair",
"recpair",
"LRScore")])
igraph::E(graph1)$weight <- igraph::E(graph1)$LRScore
igraph::E(graph1)$mean <- igraph::E(graph1)$LRScore
data@graphs_ggi[[cmp_name]] <- graph1
}
} else {
ctr_name <- names(data@tables)[1]
ctr_table <- data@tables[[ctr_name]]
for (i in 2:length(data@tables)) {
exp_name <- names(data@tables)[i]
cmp_name <- paste0(exp_name, "_x_", ctr_name)
exp_table <- data@tables[[exp_name]]
tmp_data <- merge(exp_table, ctr_table, by = 'allpair', all = TRUE)
tmp_data <- tmp_data %>%
tidyr::separate(.data$allpair,
c('ligpair', 'recpair'),
sep = '@', remove = F)
tmp_data$LRScore.x[is.na(tmp_data$LRScore.x)] <- 0
tmp_data$LRScore.y[is.na(tmp_data$LRScore.y)] <- 0
final_data <- tmp_data %>%
dplyr::mutate(LRScore = .data$LRScore.x - .data$LRScore.y) %>%
dplyr::select(c(.data$source.x,
.data$source.y,
.data$target.x,
.data$target.y,
.data$LRScore,
.data$ligpair,
.data$recpair,
.data$allpair,
.data$type_gene_A.x,
.data$type_gene_B.x,
.data$type_gene_A.y,
.data$type_gene_B.y,
.data$gene_A.x,
.data$gene_B.x,
.data$gene_A.y,
.data$gene_B.y))
final_data <- final_data[final_data$LRScore != 0,]
final_data <- final_data %>%
dplyr::mutate(type_gene_A = dplyr::coalesce(.data$type_gene_A.x,
.data$type_gene_A.y)) %>%
dplyr::mutate(type_gene_B = dplyr::coalesce(.data$type_gene_B.x,
.data$type_gene_B.y)) %>%
dplyr::mutate(gene_A = dplyr::coalesce(.data$gene_A.x,
.data$gene_A.y)) %>%
dplyr::mutate(gene_B = dplyr::coalesce(.data$gene_B.x,
.data$gene_B.y)) %>%
dplyr::mutate(source = dplyr::coalesce(.data$source.x,
.data$source.y)) %>%
dplyr::mutate(target = dplyr::coalesce(.data$target.x,
.data$target.y)) %>%
dplyr::select(-.data$type_gene_A.x,
-.data$type_gene_A.y,
-.data$type_gene_B.x,
-.data$type_gene_B.y,
-.data$gene_A.x,
-.data$gene_A.y,
-.data$gene_B.x,
-.data$gene_B.y,
-.data$source.x,
-.data$source.y,
-.data$target.x,
-.data$target.y) %>%
dplyr::mutate(cellpair = paste0(.data$source, "@",
.data$target)) %>%
dplyr::arrange(LRScore)
data@tables[[cmp_name]] <- final_data
final <- final_data %>%
dplyr::mutate(ccitype = paste(.data$type_gene_A, .data$type_gene_B)) %>%
dplyr::filter(!(str_detect(.data$ccitype, "Transcription Factor"))) %>%
dplyr::group_by(.data$cellpair) %>%
dplyr::summarise(LRScore = sum(.data$LRScore))
final <- final %>%
tidyr::separate(.data$cellpair, c("u", "v"), sep = "@", remove = F)
final <- na.omit(final, cols = c(u, v))
filtervar <- grepl('Transcription', final_data[['type_gene_A']]) | grepl('Transcription', final_data[['type_gene_B']])
raw_inter <- table(final_data$cellpair[!filtervar])
freq <- as.array(raw_inter)[final$cellpair] - min(as.array(raw_inter)[final$cellpair])
freq <- freq / (max(as.array(raw_inter)[final$cellpair]) - min(as.array(raw_inter)[final$cellpair])) + 0.1
final$freq <- as.array(freq)[final$cellpair]
final$pair <- final$cellpair
final <- dplyr::arrange(final, final$LRScore) #abs(final$LRScore))
graph1 <- igraph::graph_from_data_frame(final[, c("u", "v", "LRScore")])
igraph::E(graph1)$inter <- final$freq #setting thickness and weight
igraph::E(graph1)$inter.raw <- as.array(raw_inter)[final$cellpair] #setting thickness and weight
igraph::E(graph1)$weight <- igraph::E(graph1)$LRScore
igraph::E(graph1)$mean <- igraph::E(graph1)$LRScore
data@graphs[[cmp_name]] <- graph1
graph1 <- igraph::graph_from_data_frame(final_data[, c("ligpair",
"recpair",
"LRScore")])
igraph::E(graph1)$weight <- igraph::E(graph1)$LRScore
igraph::E(graph1)$mean <- igraph::E(graph1)$LRScore
data@graphs_ggi[[cmp_name]] <- graph1
}
}
saveRDS(data, file.path(out_path, "LR_data_step2.Rds"))
return(data)
}