Commit 1aae2498 authored by Leon-Charles Tranchevent's avatar Leon-Charles Tranchevent
Browse files

Correction of small typographic errors in the comments.

parent 7e1bb0ce
...@@ -487,7 +487,8 @@ for (i in seq_len(length(config$integrations))) { ...@@ -487,7 +487,8 @@ for (i in seq_len(length(config$integrations))) {
# We then filter the data. # We then filter the data.
# Note: It is currently done after the integration. # Note: It is currently done after the integration.
# It was previously done prior to the integration but we have no guarantee that # It was previously done prior to the integration but we have no guarantee that
# our filters and the integrated P values are fully independent. # our filters and the integrated P values are fully independent (nor the
# other way round actually).
nb_db_min <- max(config$nb_min_pval, config$perc_min_pval * length(datasets)) nb_db_min <- max(config$nb_min_pval, config$perc_min_pval * length(datasets))
conf_threshold <- config$confidence_threshold / length(dataset_weights) conf_threshold <- config$confidence_threshold / length(dataset_weights)
int_res <- int_res [foldchanges$nb_csss_datasets >= nb_db_min, ] #nolint int_res <- int_res [foldchanges$nb_csss_datasets >= nb_db_min, ] #nolint
......
...@@ -46,7 +46,7 @@ select_best <- function(row, array_indexes) { ...@@ -46,7 +46,7 @@ select_best <- function(row, array_indexes) {
# For one dataset, if there are multiple probes, we also take the median. # For one dataset, if there are multiple probes, we also take the median.
# This is then used to keep only the probes that have a log FC that corresponds # This is then used to keep only the probes that have a log FC that corresponds
# to the majority (otherwise we might select a probe that won't be used). # to the majority (otherwise we might select a probe that won't be used).
# For microarray data, we use row$genes, now we use row$SYMBOL. # For microarray data, we used row$genes, now we use row$SYMBOL.
datasets_df_gene <- datasets_data_df %>% datasets_df_gene <- datasets_data_df %>%
filter(SYMBOL == row$SYMBOL) %>% filter(SYMBOL == row$SYMBOL) %>%
select(dataset, logFC) %>% select(dataset, logFC) %>%
...@@ -108,14 +108,14 @@ select_best <- function(row, array_indexes) { ...@@ -108,14 +108,14 @@ select_best <- function(row, array_indexes) {
# Then we do have multiple probes with a large average expression. # Then we do have multiple probes with a large average expression.
# We will therefore select the one with the best P value (for Best-PVal). # We will therefore select the one with the best P value (for Best-PVal).
to_return[((i * 3) - 2):(i * 3)] <- c(paste(na.omit(all_avgs), collapse = "|"), to_return[((i * 3) - 2):(i * 3)] <- c(paste(na.omit(all_avgs), collapse = "|"),
probe_list[which.max(all_avgs)], probe_list[which.max(all_avgs)],
all_probes_sup_min[which.max(all_pvals_sup_min)]) all_probes_sup_min[which.max(all_pvals_sup_min)])
} else { } else {
# At most a single probe is highly expressed, we select that probe for Best-Pval since it # At most a single probe is highly expressed, we select that probe for Best-Pval since it
# is too risky to based the decision on the P values. # is too risky to base the decision on the P values.
to_return[((i * 3) - 2):(i * 3)] <- c(paste(na.omit(all_avgs), collapse = "|"), to_return[((i * 3) - 2):(i * 3)] <- c(paste(na.omit(all_avgs), collapse = "|"),
probe_list[which.max(all_avgs)], probe_list[which.max(all_avgs)],
probe_list[which.max(all_avgs)]) probe_list[which.max(all_avgs)])
} }
rm(all_pvals_sup_min, all_probes_sup_min) rm(all_pvals_sup_min, all_probes_sup_min)
rm(probes, probe_list, all_avgs, all_pvals) rm(probes, probe_list, all_avgs, all_pvals)
......
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