Visualizing ASCAT profiles¶
This example visualizes an ASCAT profile and an allele-specific copy number segmentation along with the raw LogR and BAF data.
We use the simulated example dataset from ASCAT's website in this example. Although the dataset uses only 10000 probes, the visualization has been used successfully with over 1.5M SNPs extracted from WGS data.
TODO: Some explanation and feature highlights:
- View composition
- minLength for rules
- yOffset
- geometric zooming
- plotBackground
Data wrangling¶
Even though ASCAT computes start and end positions for the segments in the copy number profiles, it does not provide them for the raw allele-specific copy number segmentations. The following R script runs ASCAT, computes segmented LogR and BAF means, and writes the results for a single sample into two files, one for the segments and another one for the raw LogR and BAF SNPs.
library(ASCAT) library(dplyr) library(magrittr) library(readr) library(tibble) # Choose an arbitrary sample id sampleId <- 96 # Run ASCAT analysis ascat.bc = ascat.loadData("Tumor_LogR.txt", "Tumor_BAF.txt", "Germline_LogR.txt", "Germline_BAF.txt") ascat.bc = ascat.aspcf(ascat.bc) ascat.output = ascat.runAscat(ascat.bc) # Join SNP positions to LogR and BAF values segmentedSNPs <- as_tibble(ascat.bc$SNPpos, rownames = "SNP") %>% rename(chr = chrs) %>% left_join(tibble(SNP = rownames(ascat.bc$Tumor_LogR_segmented), logR = ascat.bc$Tumor_LogR_segmented[, sampleId])) %>% left_join(tibble(SNP = rownames(ascat.bc$Tumor_BAF_segmented[[sampleId]]), baf = ascat.bc$Tumor_BAF_segmented[[sampleId]])) %>% mutate(segmentId = 0) # Pick the segments of a specific sample and enumerate them segments <- ascat.output$segments %>% filter(sample == paste0("S", sampleId)) %>% mutate(segmentId = row_number()) # Assign each SNP a segment for (i in seq_len(nrow(segmentedSNPs))) { segmentedSNPs$segmentId[i] = min(which( segmentedSNPs$pos[i] >= segments$startpos & segmentedSNPs$pos[i] <= segments$endpos & segmentedSNPs$chr[i] == segments$chr )) } # Join the segments with the LogR and BAF values and write them to a file segments %>% left_join(segmentedSNPs %>% group_by(segmentId) %>% summarise(logRMean = mean(logR, na.rm = TRUE), bafMean = mean(baf, na.rm = TRUE), nProbes = n())) %>% select(-segmentId) %>% mutate_if(is.numeric, round, digits = 3) %>% write_tsv(paste0("ascat_segments_S", sampleId, ".tsv"), na = "") # Write the raw data. Only include BAF for SNPs that are germline homozygous segmentedSNPs %>% mutate(segmentedBaf = baf) %>% select(-logR, -segmentId, -baf) %>% left_join(tibble(SNP = rownames(ascat.bc$Tumor_LogR), logR = ascat.bc$Tumor_LogR[, sampleId])) %>% left_join(tibble(SNP = rownames(ascat.bc$Tumor_BAF), baf = ascat.bc$Tumor_BAF[, sampleId])) %>% mutate(baf = ifelse(is.na(segmentedBaf), NA, baf)) %>% select(-segmentedBaf) %>% write_tsv(paste0("ascat_raw_S", sampleId, ".tsv"), na = "")
The first five rows from the produced files:
ascat_segments_S96.tsv¶
| chr | startpos | endpos | nMajor | nMinor | logRMean | bafMean |
|---|---|---|---|---|---|---|
| 1 | 1695590 | 116624361 | 2 | 0 | -0.133 | 0.218 |
| 1 | 116976886 | 120138178 | 2 | 2 | 0.18 | 0.5 |
| 1 | 143133910 | 147896005 | 4 | 1 | 0.373 | 0.301 |
| 1 | 147970991 | 244820741 | 3 | 1 | 0.219 | 0.325 |
| 2 | 385195 | 3254139 | 2 | 0 | -0.109 | 0.244 |
ascat_raw_S96.tsv¶
| SNP | chr | pos | logR | baf |
|---|---|---|---|---|
| SNP1 | 1 | 1695590 | -0.0589 | 0.2464 |
| SNP2 | 1 | 2189662 | 0.0293 | 0.2013 |
| SNP3 | 1 | 2393282 | -0.2291 | |
| SNP4 | 1 | 2414781 | -0.2221 | 0.7504 |
| SNP5 | 1 | 2516275 | -0.0379 |