... | ... |
@@ -1,7 +1,7 @@ |
1 | 1 |
Package: NanoMethViz |
2 | 2 |
Type: Package |
3 | 3 |
Title: Visualise methlation data from Oxford Nanopore sequencing |
4 |
-Version: 2.5.7 |
|
4 |
+Version: 2.5.8 |
|
5 | 5 |
Authors@R: c( |
6 | 6 |
person("Shian", "Su", email = "[email protected]", role = c("cre", "aut"))) |
7 | 7 |
Description: NanoMethViz is a toolkit for visualising methylation data from |
... | ... |
@@ -1,4 +1,4 @@ |
1 |
-cluster_reads <- function(x, chr, start, end) { |
|
1 |
+cluster_reads <- function(x, chr, start, end, min_pts = 10) { |
|
2 | 2 |
methy_data <- query_methy(x, chr, start, end ) %>% |
3 | 3 |
filter(pos >= start & pos < end) |
4 | 4 |
|
... | ... |
@@ -36,7 +36,7 @@ cluster_reads <- function(x, chr, start, end) { |
36 | 36 |
mod_mat_filled[i, is.na(mod_mat_filled[i, ])] <- mean(mod_mat_filled[i, ], na.rm = TRUE) |
37 | 37 |
} |
38 | 38 |
|
39 |
- dbsc <- dbscan::hdbscan(mod_mat_filled, minPts = 15) |
|
39 |
+ dbsc <- dbscan::hdbscan(mod_mat_filled, minPts = min_pts) |
|
40 | 40 |
clust_df <- data.frame(read_name = rownames(mod_mat_filled), cluster_id = dbsc$cluster) |
41 | 41 |
|
42 | 42 |
out_df <- as.data.frame(mod_mat) %>% |
... | ... |
@@ -52,5 +52,11 @@ cluster_reads <- function(x, chr, start, end) { |
52 | 52 |
dplyr::summarise(mean = mean(methy_prob, na.rm = TRUE)) %>% |
53 | 53 |
dplyr::left_join(clust_df, by = "read_name") %>% |
54 | 54 |
dplyr::left_join(read_stats, by = "read_name") %>% |
55 |
- dplyr::arrange(cluster_id) |
|
55 |
+ dplyr::arrange(cluster_id) %>% |
|
56 |
+ dplyr::mutate( |
|
57 |
+ cluster_id = as.factor(cluster_id), |
|
58 |
+ start = as.integer(start), |
|
59 |
+ end = as.integer(end), |
|
60 |
+ span = as.integer(span) |
|
61 |
+ ) |
|
56 | 62 |
} |