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==DMR finding using BSmooth and T-stat== ===perform smoothing on the data using BSmooth=== [[Media:runSmoothing.txt|BSmooth R script]] bsseq package usage and description: [http://www.bioconductor.org/packages/devel/bioc/vignettes/bsseq/inst/doc/bsseq_analysis.pdf Link] [http://www.bioconductor.org/packages/devel/bioc/vignettes/bsseq/inst/doc/bsseq.pdf UserGuide] TO run: script_dir="/media/1TB_storeB/ZL_meangenemachine/Habibi_smoothing" bed="/media/1TB_storeB/ZL_meangenemachine/BEDfiles" for c in chr# do -> for f in <font color=red>list_of_file_names</font> do -> grep $c [space]$bed/$f.bed.txt > $f.$c for ns in <font color=red>number_CpG_sites</font> do -> for h in <font color=red>min_window_size</font> do -> sed "s/TESTNS/$ns/g" runSmoothing.R | sed "s/TESTH/$h/g" | sed "s/CHROM/$c/g" > run.R output files: file_name.chr# chr#.ns#_h#.rda ===perform T-statistics on smoothed data=== [[Media:runTstat.txt|Tstat R script]] TO run: for f in *rda do -> sed "s/RDA/$f/g" runTstat.R > Run.R output files: chr#.ns#_h#.rda_comparison_coveredsites chr#.ns#_h#.rda_comparison_DRMs.txt chr#.ns#_h#.rda_comparison_plotDMRs.pdf highlight: group1 and group2 contain the sample names of the two groups being compared estimate.var describes which sample are being used to estimate variability, and choices are: gr# (e.g normal sample when compared again cancer data) same(assume same variability in each group) paired(do a paired T-test) In my analysis, I used gr2 to estimate variability, which are the ES lines ===plotting the DMRs=== T-stat will generate txt file that identified potential DMRs between two group of samples Dinh modified the methyl matrix perl script which allows it to use chr_interval as input and generate matrix only within the interval specified on meangenemachine: /home/zhl002/ZL_1TB/allBED2Matrix_Jan222014.pl TO run: perl allBED2Matrix_Jan222014.pl [list_bed] [min_sample] [min_depth] [windowSize >= 1] chr#:#-# output: MethylMatrix_bed_list.min#.md#.win# Then use [[Media:plot_methyl_graph_black.txt | plotting DMR R script]] to generate the plot use vim to change the first line of the script to use the output matrix that we just generated from last step ===results=== used previously published WGBS on ES cell cultured under 2i and serum conditions [http://www.sciencedirect.com/science/article/pii/S1934590913002609 Link] do BSmooth on all the data: ns=20 and h=2000 (20 CpG sites in the window that range from minimum =2000 and maximum =10,000) then run Tstat to do the comparison: SCNT-serum (indx 9 and 10) against ES-serum (indx11 and Habibi_ES_serum) (ES serum is group2 and also used to estimate variability) iPSC-serum (indx 1, 2, 4, 5) against ES-serum (indx11 and Habibi_ES_serum) (ES serum is group2 and also used to estimate variability) SCNT-2i (indx 6 and 7) against ES-serum (Habibi_ES_2i_adapted and Habibi_ES_2i) (ES 2i is group2 and also used to estimate variability) Among the candidate DMRs generated from the above steps, we were able to detect true culture-specific DMRs, where there are differentially methylated regions between 2i and serum conditions. However, because this method relies on biological replicates to do smoothing and calculate T-statistics, so we have decided to use other published method to find DMRs.
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