Killifish Hypoxia RRBS Part 30
Fixing DMR figures
When working on green crab metabolomics analysis, I realized that I used summarize_all incorrectly! Instead of averaging methylation for CpGs in a DMR, I accidentally added all methylation. That could explain the weird heatmaps. I went back into this code to correct my mistake.
Heatmaps
I revised my code to actually average methylation then made heatmaps.
NBH20vOCavgMeth <- NBH20vOCpositions %>%
group_by(., DMR) %>%
dplyr::select(., -c(chr, pos)) %>%
summarize_all(funs(mean(., na.rm = TRUE))) %>%
slice(., 1, 3:10, 2) %>%
column_to_rownames(var = "DMR") #Group by DMR. Remove chromosome and position columns. Average methylation level across all CpG positions in each DMR for each sample. Reorganize manually with slice. Change DMR column to rownames
In addition to using the correct data, I installed ComplexHeatmap as a wrapper to pheatmap. This gave me some flexibility in plotting. I also added rownames (DMR IDs), plotted percent methylation, and updated the legend name:
pdf("figures/20_OC_N-DMR-heatmap.pdf", width = 11, height = 8.5)
pheatmap(as.matrix(NBH20vOCavgMeth*100), cluster_row = TRUE, show_rownames = TRUE, cluster_cols = FALSE, show_colnames = FALSE, color = heatmapColors, annotation_col = NBH20vOCoxygenTreatment, annotation_colors = NBH20vOCoxygenTreatmentColors, annotation_names_col = FALSE, name = "Methylation (%)")
dev.off()
Figures 1-2. Heatmaps of average methylation across samples for normoxia vs. outside control DMR and hypoxia vs. outside control DMR.
Looking at these heatmaps, I still see inter-individual variation. However, there are more DMR with clearer differences between treatments when comparing the control and severe hypoxia treatments.
BAT_correlating plots
I went through the rest of my code to see if there was any downstream analysis that used the dataframe of averaged methylation. Turns out, my correlation plots did! I reran the chunk of code that performed several left_join commands to get the dataframe for the correlation plot, then remade figures.
NBH20vOCcorrOnly <- NBH20vOCBATcorrelating %>%
filter(., is.na(rho.p.val) == FALSE) %>%
left_join(., (NBH20vOCavgMeth %>%
rename_with(., function(x) paste0(x, sep = "_meth")) %>%
rownames_to_column(., var = "DMR")), by = "DMR") %>%
left_join(., NBH20geneExp, by = "gene.name") %>%
left_join(., NBHOCgeneExp, by = "gene.name") %>%
dplyr::select(-c(chr, DMR.start, DMR.end, direction, gene.start, gene.end, strands, "OC-N3_exp")) %>%
pivot_longer(cols = "NO_20.N4_meth":"OC_OC.N4_meth", names_to = "sample_meth", values_to = "meth") %>%
pivot_longer(cols = "20-N1_exp":"OC-N5_exp", names_to = "sample_exp", values_to = "exp") %>%
mutate(sample_meth = gsub(x = sample_meth, pattern = "_meth", replacement = "")) %>%
mutate(sample_meth = gsub(x = sample_meth, pattern = "NO_20.", replacement = "20-")) %>%
mutate(sample_meth = gsub(x = sample_meth, pattern = "OC_OC.", replacement = "OC-")) %>%
mutate(sample_exp = gsub(x = sample_exp, pattern = "_exp", replacement = "")) %>%
filter(., sample_meth == sample_exp) %>%
mutate(sample = sample_meth) %>%
separate(., col = sample_meth, into = c("treatment", "extra"), sep = "-") %>%
dplyr::select(-c(sample_exp, extra))
#Take BAT_correlating output and retain rows that have a correlation p-value. Join with average methylation data where the columns are renamed to indicate methylation values. Join with gene expression data. Remove unecessary columns. Pivot longer for methylation and expression data separately. Modify column contents and retain rows where the sample ID for methylation and expression match. Create a new sample ID column. Create a new column for treatment specifications by separating defunct sample_meth column. Remove extra columns
Figure 3. Correlation plot for normoxia vs. outside control DMR
Figures 4-5. Hypoxia vs. outside control DMR plots for all correlations and only significant correlations
Going forward
- Update methods and results
- Identify known SNP/DMR overlaps
- Update OSF repository for all intermediate files