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[[2016-Mouse-MHB]] | [[2016-Mouse-MHB]] | ||
* here, i update the idea. since usually our mhb calling input data is quite large and usually we merge large number WGBS data and therefore the coverage and depth is quite high. therefore, I tried fix the genomic intervals (10K as the window) and then call the haplotype for each sample. then I reassemble the code so that we can add the new samples to our current haplotype database, then we can update the MHB easily each time. | * here, i update the idea. since usually our mhb calling input data is quite large and usually we merge large number WGBS data and therefore the coverage and depth is quite high. therefore, I tried fix the genomic intervals (10K as the window) and then call the haplotype for each sample. then I reassemble the code so that we can add the new samples to our current haplotype database, then we can update the MHB easily each time. | ||
* I also update the bam2hapinfo.pl and now hapinfo2bedgraph.pl is also available so that our methylation haplotype pipeline cannnot connected with all the existed methyaltion analysis pipelne. | |||
* bam2hapinfo | * bam2hapinfo |
Revision as of 19:22, 14 February 2017
Genome-wide DNA methylation profile of reprogramming
Method
- Download human and mice reprogramming dataset
- Merge genome-wide DNA methylation data of human and mice
- Dendrogram based on genome-wide DNA methylation data(Figure 1a and 1b)
- PCA analysis based on genome-wide DNA methylation data(Figure 2a and 2b)
- heatmap based on DMR and DMRs in CpG Shore region (Figuare 1c,1d,1e and 1f)
- 1, cluster all scRNA by subset genes plu-diff
- 2, select good cells and separate ips and scnt
- 3, find differential express genes between ips and scnt
- 4, show previous knowledge base on this data
- 5, check Methylation to these DEG
- 6, find methylation biomarker/matric to show the difference between ips and scnt
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1027nnn/GSM1027571/suppl/GSM1027571_DNA_CpG_coverage_E14_serum_LIF.bedGraph.gz & wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1027nnn/GSM1027571/suppl/GSM1027571_DNA_CpG_methcounts_E14_serum_LIF.bedGraph.gz & wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1027nnn/GSM1027572/suppl/GSM1027572_DNA_CpG_coverage_E14_2i_adapted.bedGraph.gz & wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1027nnn/GSM1027572/suppl/GSM1027572_DNA_CpG_methcounts_E14_2i_adapted.bedGraph.gz & wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1127nnn/GSM1127953/suppl/GSM1127953_DNA_CpG_E14_serum_rep.bedGraph.gz & wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1127nnn/GSM1127954/suppl/GSM1127954_DNA_CpG_E14_2i_adapted_rep.bedGraph.gz &
Result
- MHB and entropy in scRNA-seq
Dendrogram and PCA analysis
- GSE57179: human, 4 hiPS, 4 SCNT, 2 ES and 1 fibroblasts
- Dendrogram based on genome-wide DNA methylation for human can be found here File:Figure1.dendrogram.euclidean.ward.hclust.pdf
- Dendrogram based on genome-wide DNA methylation for mice can be found here
- PCA analysis based on genome-wide DNA methylation for human can be found here File:Figure2.PC13.pdf
- PCA analysis based on genome-wide DNA methylation for mice can be found here
Differential Methylation Site Analysis
Distance between ES, IPS and HF
The distance between HF and ES as well as IPS is very large while actually IPS and ES are very similar, enclose figure showed ES, HF and two kinds of IPS (with two different inducing factor/method) in PC1-PC2 space.File:Ips ES HF.pdf.pdf, please check the code: File:PCAanalysis4GSE54115.R.txt
Relationship between Methylation linkage and Variation of Gene expression between ips and SCNT in mouse (mm9)
- We don't have human data, so such analysis cannot be conducted in human
- build analysis function so that Alice can use it. input: R2 and expression matrix.
- input:
cd /media/Home_Raid1/zhl002/WGBS_mouse/ /media/Home_Raid1/zhl002/WGBS_mouse/iPSC_SCNT.R2Pvalue.high.txt
Mouse MHB
2016-9-7: Download 4 differentiation SRR files(SRR2011294/SRR2011297/SRR2011296/SRR2011295)
- remove all single-cell methylome
- remove SRX1126148,SRX1091395,SRX1091396 since mapping ratio<5%
- 36 + 4 samples= 40 sample(SRX080191 to SRX701302)
cd /home/shg047/oasis/mouse/fastq perl ~/bin/trimGolrePBS.pl FastqMatchConfig.txt nonsubmit qsub SRR2011294_1.fastq.gz.job qsub SRR2011295_1.fastq.gz.job qsub SRR2011296_1.fastq.gz.job qsub SRR2011297_1.fastq.gz.job perl ~/bin/bismark2bamPBS.pl FastqMatchConfig.txt nosubmit qsub SRR2011294_1.fastq.gz.job qsub SRR2011295_1.fastq.gz.job qsub SRR2011296_1.fastq.gz.job qsub SRR2011297_1.fastq.gz.job
- RD90UP80
2016-Mouse-MHB
- here, i update the idea. since usually our mhb calling input data is quite large and usually we merge large number WGBS data and therefore the coverage and depth is quite high. therefore, I tried fix the genomic intervals (10K as the window) and then call the haplotype for each sample. then I reassemble the code so that we can add the new samples to our current haplotype database, then we can update the MHB easily each time.
- I also update the bam2hapinfo.pl and now hapinfo2bedgraph.pl is also available so that our methylation haplotype pipeline cannnot connected with all the existed methyaltion analysis pipelne.
- bam2hapinfo
perl /home/shg047/bin/bam2hapInfo2PBS.pl bam2hapinfo.config submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt cd /home/shg047/oasis/mouse/sortBam perl ~/bin/bam2hapInfo2PBS.pl SamConfig.txt submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt
cd /home/shg047/oasis/mouse/alice/bam perl ~/bin/bam2hapInfo2PBS.pl SamConfig.txt submit bisreadMapper /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt
cd ../mergeBam/ perl ../saminfoPre4bam2hapinfo.pl > SamConfig.txt perl /home/shg047/bin/bam2hapInfo2PBS.pl bam2hapinfo.config submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt qsub SRX1019864.job qsub SRX1019865.job qsub SRX1019866.job qsub SRX1019867.job
- merge haploinfo (cat) and Unique (hapinfoMerge.pl)
cat ************ | sort > Adult.sort.hapinfo.txt cat ************ | sort > mESC.sort.hapinfo.txt perl /home/shg047/oasis/mouse/mergeHapinfo/hapinfoMerge.pl Adult.sort.hapinfo.txt perl /home/shg047/oasis/mouse/mergeHapinfo/hapinfoMerge.pl mESC.sort.hapinfo.txt
- hapinfo to MHB
perl ~/bin/hapinfo2mhb.pl SRX202086.hapInfo.txt 0.1
- Hapinfo2LD-R2(here: MHB regions,hapinfo2LDR2ByBed.pl)
cd /home/shg047/oasis/mouse/mergeHapinfo perl ~/bin/hapinfo2BlocAvgR2.pl SRX1019864.hapInfo.txt > SRX1019864.R2.txt
cd /home/shg047/oasis/mouse/mergeHapinfo/mf perl /home/shg047/bin/hapinfo2mf.pl /home/shg047/oasis/mouse/mergeHapinfo/mf > output.mf File:4FD2.tm.png
- hapinfo to R2 by chr10:126500749-126500761
perl ~/bin/hapinfo2LDR2.pl rlt chr10:126500749-126500761 < SRX202086.hapInfo.txt
- hapinfo2mf (5me)
cd /home/shg047/oasis/mouse/mergeHapinfo/mf perl ~/bin/hapinfo2mf.pl grep chr10:100004267-100004288 output.mf File:DD51.tm.png
- Alice's own data
cd /home/shg047/oasis/mouse/alice/raw ls *.txt.gz | awk -F. '{print $1}' | awk -F_ '{print $4}' | sort -u
- Identify differential MHBs among ESC, Somatic tissue and IPS-SCNT groups
for i in {1..100} do perl ~/bin/randomSampleFromHaploInfo.pl tissue.hapinfo.txt > tissue.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl tissue.hapinfo.txt.$i > R2.tissue.hapinfo.txt.$i perl ~/bin/randomSampleFromHaploInfo.pl ipsNT.hapinfo.txt > ipsNT.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl ipsNT.hapinfo.txt.$i > R2.ipsNT.hapinfo.txt.$i perl ~/bin/randomSampleFromHaploInfo.pl ESC.hapinfo.txt > ESC.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl ESC.hapinfo.txt.$i > R2.ESC.hapinfo.txt.$i done
- Differential Linkage MHBs Among IPS, STEM, TISSUE
data<-read.table("MatrixR2.txt",head=T) x1<-grep("ESC",colnames(data)) x2<-grep("ips",colnames(data)) x3<-grep("tissue",colnames(data)) newdata<-data.frame(Y=c(rep("ESC",length(x1)),rep("IPS",length(x2)),rep("TISSUE",length(x3))),t(data),check.names=F) Pvalue<-c() for(i in 2:ncol(newdata)){ print(i) Newdata<-na.omit(newdata[,c(1,i)]) if(length(unique(Newdata$Y))>1){ fit <- try(aov(Newdata[,2]~ Y, data=Newdata)) p<-summary(fit)1[1,5] }else{ p<-NA } Pvalue<-c(Pvalue,p) } rlt<-data.frame(MHB=colnames(newdata)[2:ncol(newdata)],Pvalue) write.table(rlt,file="MHB.Pvalue.Bootstrap.txt",sep="\t",col.names=F,row.names=F,quote=F) data1<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.Pvalue.Bootstrap.txt",head=F) data2<-read.table("/home/shg047/oasis/mouse/hapinfo/tmp/MHB.R2.txt",head=T) newdata<-data.frame(data2,Pvalue=data1[match(rownames(data2),data1[,1]),2]) write.table(newdata,file="MHB.R2.Pvalue.Bootstrap.txt",sep="\t",col.names=NA,row.names=T,quote=F) data3<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.R2.Pvalue.Bootstrap.txt",head=F) Subset1<-subset(data3,R2.ESC.hapinfo.txt<R2.ipsNT.hapinfo.txt & R2.tissue.hapinfo.txt<R2.ipsNT.hapinfo.txt) dim(Subset1) Subset2<-subset(data3,R2.ESC.hapinfo.txt<0.1 & R2.tissue.hapinfo.txt<0.1 & R2.ipsNT.hapinfo.txt>0.2) dim(Subset2)
- Two group wilcox test
data<-read.table("MatrixR2.txt",head=T) x1<-grep("ESC",colnames(data)) x2<-grep("ips",colnames(data)) data<-data[,c(x1,x2)] newdata<-data.frame(Y=c(rep("ESC",length(x1)),rep("IPS",length(x2))),t(data),check.names=F) Pvalue<-c() for(i in 2:ncol(newdata)){ print(i) Newdata<-na.omit(newdata[,c(1,i)]) if(length(unique(Newdata$Y))>1){ fit <- try(wilcox.test(Newdata[,2]~ Y, data=Newdata)) p<-fit$p.value }else{ p<-NA } Pvalue<-c(Pvalue,p) } rlt<-data.frame(MHB=colnames(newdata)[2:ncol(newdata)],Pvalue) write.table(rlt,file="MHB.Pvalue.Bootstrap.txt",sep="\t",col.names=F,row.names=F,quote=F) data1<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.Pvalue.Bootstrap.txt",head=F) data2<-read.table("/home/shg047/oasis/mouse/hapinfo/tmp/MHB.R2.txt",head=T) newdata<-data.frame(data2,Pvalue=data1[match(rownames(data2),data1[,1]),2]) write.table(newdata,file="MHB.R2.Pvalue.Bootstrap.txt",sep="\t",col.names=NA,row.names=T,quote=F) data3<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.R2.Pvalue.Bootstrap.txt",head=F) Subset1<-subset(data3,R2.ESC.hapinfo.txt<R2.ipsNT.hapinfo.txt & R2.tissue.hapinfo.txt<R2.ipsNT.hapinfo.txt) dim(Subset1) Subset2<-subset(data3,R2.ESC.hapinfo.txt<0.1 & R2.tissue.hapinfo.txt<0.1 & R2.ipsNT.hapinfo.txt>0.2) dim(Subset2)
Mouse scRNA
- heatmap plot
- quite interesting, with ESC specific gene, 22% ips and scnt are in bad quality(2633/(9220+2633)) including: 5519 ips and 3701 scnt, indicating SCNT is better than ips or ips still have space to improve.
- We can download some more study data and validate it again. Very good!
Human IPS/ESC MHB
- Human hapinfo files, total 70. 1st part(59=49+10), 2nd part= 11 (ips and scnt)
cd /home/shg047/work/monod/hapinfo ls *hapInfo.txt | grep -v CTT | grep -v PC | grep -v NC | grep -v LC | grep -v "\-7-" | grep -v 6- | grep -v "\-T-" | grep -v "\-P-" | grep -v merge | xargs -I {} cat {} >> merge.txt cd /home/shg047/work/Alice/human/mergeHapinfo ls *hapinfo.txt | xargs -I {} cat {} >> /home/shg047/work/Alice/human/mergeHapinfo/merge.txt # Go to GM since the memory of TSCC is not enough. cd /home/shg047/work/monod/hapinfo # already have the key, no need input the passwd scp merge.txt shg047@genome-miner.ucsd.edu:/media/Home_Raid1/shg047/work/Alice/human/hapinfo cd /media/Home_Raid1/shg047/work/Alice/human/hapinfo perl ~/bin/hapinfoMerge.pl merge.txt perl ~/bin/hapinfo2mhb.pl merge.txt.SumUniq 0.3 > mm9.MHB.0.3.txt perl ~/bin/hapinfo2mhb.pl merge.txt.SumUniq 0.5 > mm9.MHB.0.5.txt mkdir ../mhb mv mm9.MHB.0.5.txt ../mhb mv mm9.MHB.0.3.txt ../mhb
/home/shg047/work/monod/hapinfo/MHB.0.5.txt /home/shg047/work/monod/hapinfo/MHB.0.3.txt
- 417 SRR download(complete, remember fastq-dump should be updated regular or else the download will be with lots of confusing mistake)
perl srrdownload.pl
- 417 Alignment by bismark and BAM (complete)
cd /home/shg047/work/Alice/human perl bismarkAutoSingle.pl submit
- 417 sortBam(complete)
cd /home/shg047/work/Alice/bam perl ~/bin/bam2sortBam.pl sortc submit
- Remove cancer samples
- 417 hapinfo (complete)
cd /home/shg047/work/Alice/sortBam perl ~/bin/SaminfoPre4hapinfo.pl > sampleconfig.txt perl ~/bin/bam2hapInfo2PBS.pl sampleconfig.txt non bismark /home/shg047/oasis/db/hg19/hg19.chrom.sizes /home/shg047/oasis/db/hg19/HsGenome19.CpG.positions.txt
- coverage, R2, methylation level
WNT5A chr14:29318659-29338701 PPP3CA chr3:136333030-136600691
sort the new bam files
Conclusion
- For human ips and SCNT data, the methylation status of 4377908 CpG were shared by 11 samples(2 ES, 1 Fibroblast, 4 ips and 4 SCNT). PCA analysis based on the genome-wide DNA methylation data showed the first and second principle component can explain almost 50% variances. In the dimension of first component fibroblast were obviously separated with ES,ips and SCNT. In the dimension of the second component, ES was separated with ips and SCNT with clear boundary.
- For mice ips and SCNT data, the methylation status of 4909250 CpG were shared by 9 samples(1 ES, 4 ips and 4 SCNT). PCA analysis based on the genome-wide DNA methylation data showed the first and second principle component can explain almost 50% variances. In the dimension of first component fibroblast were obviously separated with ES,ips and SCNT. In the dimension of the second component, ES was separated with ips and SCNT with clear boundary.
Highlight
Human ES/iPS Dataset
GSM1385973 | SCNT-1 | ||||||
GSM1385974 | HDF | ||||||
GSM1385975 | HESO-7 | ||||||
GSM1385976 | HESO-8 | ||||||
GSM1385977 | SCNT-2 | ||||||
GSM1385978 | SCNT-3 | ||||||
GSM1385979 | SCNT-4 | ||||||
GSM1385980 | iPS-S1 | ||||||
GSM1385981 | iPS-S2 | ||||||
GSM1385982 | iPS-R2 | ||||||
GSM1385983 | iPS-R1 | ||||||
SCNT1 | SCNT2 | SCNT3 | SCNT4 | iPSC_S1 | iPSC_S2 | iPSC_R1 | iPSC_R2 |
SRX542415 | SRX542419 | SRX542420 | SRX542421 | SRX542422 | SRX542423 | SRX542425 | SRX542424 |
SRR1286295 | SRR1286459 | SRR1286502 | SRR1286538 | SRR1286579 | SRR1286620 | SRR1286703 | SRR1286661 |
SRR1286296 | SRR1286460 | SRR1286503 | SRR1286539 | SRR1286580 | SRR1286621 | SRR1286704 | SRR1286662 |
SRR1286297 | SRR1286461 | SRR1286504 | SRR1286540 | SRR1286581 | SRR1286622 | SRR1286705 | SRR1286663 |
SRR1286298 | SRR1286462 | SRR1286505 | SRR1286541 | SRR1286582 | SRR1286623 | SRR1286706 | SRR1286664 |
SRR1286299 | SRR1286463 | SRR1286506 | SRR1286542 | SRR1286583 | SRR1286624 | SRR1286707 | SRR1286665 |
SRR1286300 | SRR1286464 | SRR1286507 | SRR1286543 | SRR1286584 | SRR1286625 | SRR1286708 | SRR1286666 |
SRR1286301 | SRR1286465 | SRR1286508 | SRR1286544 | SRR1286585 | SRR1286626 | SRR1286709 | SRR1286667 |
SRR1286302 | SRR1286466 | SRR1286509 | SRR1286545 | SRR1286586 | SRR1286627 | SRR1286710 | SRR1286668 |
SRR1286303 | SRR1286467 | SRR1286510 | SRR1286546 | SRR1286587 | SRR1286628 | SRR1286711 | SRR1286669 |
SRR1286304 | SRR1286468 | SRR1286511 | SRR1286547 | SRR1286588 | SRR1286629 | SRR1286712 | SRR1286670 |
SRR1286305 | SRR1286469 | SRR1286512 | SRR1286548 | SRR1286589 | SRR1286630 | SRR1286713 | SRR1286671 |
SRR1286306 | SRR1286470 | SRR1286513 | SRR1286549 | SRR1286590 | SRR1286631 | SRR1286714 | SRR1286672 |
SRR1286307 | SRR1286471 | SRR1286514 | SRR1286550 | SRR1286591 | SRR1286632 | SRR1286715 | SRR1286673 |
SRR1286308 | SRR1286472 | SRR1286515 | SRR1286551 | SRR1286592 | SRR1286633 | SRR1286716 | SRR1286674 |
SRR1286309 | SRR1286473 | SRR1286516 | SRR1286552 | SRR1286593 | SRR1286634 | SRR1286717 | SRR1286675 |
SRR1286310 | SRR1286474 | SRR1286517 | SRR1286553 | SRR1286594 | SRR1286635 | SRR1286718 | SRR1286676 |
SRR1286311 | SRR1286475 | SRR1286518 | SRR1286554 | SRR1286595 | SRR1286636 | SRR1286719 | SRR1286677 |
SRR1286312 | SRR1286476 | SRR1286519 | SRR1286555 | SRR1286596 | SRR1286637 | SRR1286720 | SRR1286678 |
SRR1286313 | SRR1286477 | SRR1286520 | SRR1286556 | SRR1286597 | SRR1286638 | SRR1286721 | SRR1286679 |
SRR1286314 | SRR1286478 | SRR1286521 | SRR1286557 | SRR1286598 | SRR1286639 | SRR1286722 | SRR1286680 |
SRR1286315 | SRR1286479 | SRR1286522 | SRR1286558 | SRR1286599 | SRR1286640 | SRR1286723 | SRR1286681 |
SRR1286316 | SRR1286480 | SRR1286523 | SRR1286559 | SRR1286600 | SRR1286641 | SRR1286724 | SRR1286682 |
SRR1286317 | SRR1286481 | SRR1286524 | SRR1286560 | SRR1286601 | SRR1286642 | SRR1286725 | SRR1286683 |
SRR1286318 | SRR1286482 | SRR1286525 | SRR1286561 | SRR1286602 | SRR1286643 | SRR1286726 | SRR1286684 |
SRR1286319 | SRR1286483 | SRR1286526 | SRR1286562 | SRR1286603 | SRR1286644 | SRR1286727 | SRR1286685 |
SRR1286320 | SRR1286484 | SRR1286527 | SRR1286563 | SRR1286604 | SRR1286645 | SRR1286728 | SRR1286686 |
SRR1286321 | SRR1286485 | SRR1286528 | SRR1286564 | SRR1286605 | SRR1286646 | SRR1286729 | SRR1286687 |
SRR1286322 | SRR1286486 | SRR1286529 | SRR1286565 | SRR1286606 | SRR1286647 | SRR1286730 | SRR1286688 |
SRR1286323 | SRR1286487 | SRR1286530 | SRR1286566 | SRR1286607 | SRR1286648 | SRR1286731 | SRR1286689 |
SRR1286324 | SRR1286488 | SRR1286531 | SRR1286567 | SRR1286608 | SRR1286649 | SRR1286732 | SRR1286690 |
SRR1286325 | SRR1286489 | SRR1286532 | SRR1286568 | SRR1286609 | SRR1286650 | SRR1286733 | SRR1286691 |
SRR1286326 | SRR1286490 | SRR1286533 | SRR1286569 | SRR1286610 | SRR1286651 | SRR1286734 | SRR1286692 |
SRR1286327 | SRR1286491 | SRR1286534 | SRR1286570 | SRR1286611 | SRR1286652 | SRR1286735 | SRR1286693 |
SRR1286328 | SRR1286492 | SRR1286535 | SRR1286571 | SRR1286612 | SRR1286653 | SRR1286736 | SRR1286694 |
SRR1286329 | SRR1286493 | SRR1286536 | SRR1286572 | SRR1286613 | SRR1286654 | SRR1286737 | SRR1286695 |
SRR1286330 | SRR1286494 | SRR1286537 | SRR1286573 | SRR1286614 | SRR1286655 | SRR1286738 | SRR1286696 |
SRR1286331 | SRR1286495 | SRR1286574 | SRR1286615 | SRR1286656 | SRR1286739 | SRR1286697 | |
SRR1286332 | SRR1286496 | SRR1286575 | SRR1286616 | SRR1286657 | SRR1286740 | SRR1286698 | |
SRR1286333 | SRR1286497 | SRR1286576 | SRR1286617 | SRR1286658 | SRR1286741 | SRR1286699 | |
SRR1286334 | SRR1286498 | SRR1286577 | SRR1286618 | SRR1286659 | SRR1286742 | SRR1286700 | |
SRR1286335 | SRR1286499 | SRR1286578 | SRR1286619 | SRR1286660 | SRR1286743 | SRR1286701 | |
SRR1286336 | SRR1286500 | SRR1286702 | |||||
SRR1286337 | SRR1286501 |
Supplemenatry
- For human
- PCA analysis PC2 and PC3 can be found here: File:Figure2.PC23.pdf
- PCA analysis PC1 and PC3 can be found here:File:Figure2.PC13.pdf
- PCA analysis loading can be found here: File:Figure2.PCA.loading.pdf
- PCA analysis variance can be found here: File:Figure2.PCA.loading.total.increasing.pdf
- correlation matrix between different samples: File:Table1.correlation.matrix.txt
- For mice
Reference
- 1.Ma H, Morey R, O'Neil RC, He Y, Daughtry B, Schultz MD, et al. Abnormalities in human pluripotent cells due to reprogramming mechanisms. Nature. 2014;511(7508):177-83. Epub 2014/07/11.
- 2.
- 3.