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Kun:LabNotes/SingleCellExpr/2011-9-7
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==RNAseq data analysis== *The data came from limited number of mouse PGC cells provided by Yi Zhang's lab. Rui[[Rui_Liu#RNA_libraries_for_Dr._Yi_Zhang_lab | made the RNA sequencing libraries]]. ===Analysis of low-pass sequencing data.=== *The first round of low-pass sequencing was done in HL101 run (SE60bp+index), lane 7-8. *I ran tophat on each data set, using mouse gene annotation as the model. less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx3.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx3.txt.gz > RNAseq_Indx3.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx4.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx4.txt.gz > RNAseq_Indx4.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx5.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx5.txt.gz > RNAseq_Indx5.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx6.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx6.txt.gz > RNAseq_Indx6.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx7.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx7.txt.gz > RNAseq_Indx7.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx8.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx8.txt.gz > RNAseq_Indx8.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx9.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx9.txt.gz > RNAseq_Indx9.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx10.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx10.txt.gz > RNAseq_Indx10.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx11.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx11.txt.gz > RNAseq_Indx11.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx12.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx12.txt.gz > RNAseq_Indx12.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx13.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx13.txt.gz > RNAseq_Indx13.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx14.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx14.txt.gz > RNAseq_Indx14.fq less /home/kunzhang/SeqStore/110902_HL101/s_7_Indx15.txt.gz /home/kunzhang/SeqStore/110902_HL101/s_8_Indx15.txt.gz > RNAseq_Indx15.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx3 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx3.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx4 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx4.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx5 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx5.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx6 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx6.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx7 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx7.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx8 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx8.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx9 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx9.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx10 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx10.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx11 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx11.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx12 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx12.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx13 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx13.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx14 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx14.fq /home/kunzhang/softwares/tophat-latest/tophat -p 8 --solexa1.3-quals -o tophat_Mm_Indx15 -G /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf /GenomeDB/UCSC/Mus_musculus/mm9/Sequence/BowtieIndex/genome RNAseq_Indx15.fq *Then I checked the tophat_Mm_IndxN/logs/bowtie.left_kept_reads.fixmap.log files, and used the mapping rates in these files as the proxy of the true mapping rates. {| {{table}} | align="center" style="background:#f0f0f0;"|'''Sample Name''' | align="center" style="background:#f0f0f0;"|'''Index''' | align="center" style="background:#f0f0f0;"|'''Qualty-filtered reads''' | align="center" style="background:#f0f0f0;"|'''Uniquely mappable reads''' | align="center" style="background:#f0f0f0;"|'''Non-mappable reads''' | align="center" style="background:#f0f0f0;"|'''Mapping rate''' |- | E9.5||Indx3||3,775,311||2,715,872||1,014,808||71.9% |- | E11.5||Indx4||3,987,505||2,710,512||1,238,018||68.0% |- | wt E13.5m1||Indx5||4,212,162||2,921,411||1,250,730||69.4% |- | wt E13.5f1||Indx6||3,557,137||2,506,010||1,019,618||70.5% |- | E9.5||Indx7||5,628,397||3,964,069||1,571,087||70.4% |- | E11.5||Indx8||6,095,896||4,322,793||1,687,193||70.9% |- | wt E13.5m1||Indx9||6,284,443||4,290,999||1,925,123||68.3% |- | wt E13.5m2||Indx10||4,649,460||3,276,710||1,316,390||70.5% |- | wt E13.5f1||Indx11||5,953,433||4,199,249||1,693,646||70.5% |- | wt E13.5f2||Indx12||6,712,315||4,671,588||1,969,301||69.6% |- | KO E13.5m||Indx13||4,954,624||3,517,633||1,380,709||71.0% |- | KO E13.5f1||Indx14||7,223,059||5,137,419||1,999,302||71.1% |- | KO E13.5f2||Indx15||8,053,410||5,681,254||2,272,034||70.5% |- | |} *Conclusions: **The mapping rate is consistently at ~70% across all libraries. **To generate roughly 20 millions mappable reads in each library, we need 4-5 HiSeq sequencing lanes. ===Further examination of the data quality=== *I first create a master transcripts.gtf file that combined all transcripts found in the 13 data sets. /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffcompare -o cuffcompare_all -R tophat_Mm_Indx3/transcripts.gtf tophat_Mm_Indx4/transcripts.gtf ... tophat_Mm_Indx15/transcripts.gtf *Then I ran cuffdiff on technical/biological replicates to examine the noise in the data. /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_3_vs_7 tophat_Mm_Indx3/accepted_hits.bam tophat_Mm_Indx7/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_4_vs_8 tophat_Mm_Indx4/accepted_hits.bam tophat_Mm_Indx8/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_9_vs_10 tophat_Mm_Indx9/accepted_hits.bam tophat_Mm_Indx10/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_14_vs_15 tophat_Mm_Indx14/accepted_hits.bam tophat_Mm_Indx15/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_5_vs_9 tophat_Mm_Indx5/accepted_hits.bam tophat_Mm_Indx9/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_6_vs_11 tophat_Mm_Indx6/accepted_hits.bam tophat_Mm_Indx11/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff cuffcompare_all.combined.gtf -N -p 8 -o cuffDiff_11_vs_12 tophat_Mm_Indx11/accepted_hits.bam tophat_Mm_Indx12/accepted_hits.bam *Then I plotted the FPKM values between each pairs of replicates. Between two batches of experiments: R=0.939 R=0.928 [[Image:FPKM_Indx3_vs_Indx7.png|300px]][[Image:FPKM_Indx4_vs_Indx8.png|300px]] R=0.935 R=0.978 [[Image:FPKM_Indx5_vs_Indx9.png|300px]][[Image:FPKM_Indx6_vs_Indx11.png|300px]] Within one batch of experiment. R=0.911 R=0.984 [[Image:FPKM_Indx9_vs_Indx10.png|300px]][[Image:FPKM_Indx11_vs_Indx12.png|300px]] R=0.552 (a few transcripts seem to be missing in one sample) [[Image:FPKM_Indx14_vs_Indx15.png|300px]] ===Looking for differentially expressed genes=== *I just realized that when feeding cuffdiff with a gtf file generated by cuffcompare, all the gene names are XLOC_xxxxx, which are difficult to link to the known genes. So I changed the gtf file to the UCSC mouse genes.gtf. /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf -N -p 8 -o cuffDiff_E9.5_vs_E11.5 tophat_Mm_Indx3/accepted_hits.bam,tophat_Mm_Indx7/accepted_hits.bam tophat_Mm_Indx4/accepted_hits.bam,tophat_Mm_Indx8/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf -N -p 8 -o cuffDiff_E11.5_vs_wtE13.5f tophat_Mm_Indx4/accepted_hits.bam,tophat_Mm_Indx8/accepted_hits.bam tophat_Mm_Indx6/accepted_hits.bam,tophat_Mm_Indx11/accepted_hits.bam,tophat_Mm_Indx12/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf -N -p 8 -o cuffDiff_E11.5_vs_wtE13.5m tophat_Mm_Indx4/accepted_hits.bam,tophat_Mm_Indx8/accepted_hits.bam tophat_Mm_Indx5/accepted_hits.bam,tophat_Mm_Indx9/accepted_hits.bam,tophat_Mm_Indx10/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf -N -p 8 -o cuffDiff_wtE13.5m_vs_wtE13.5f tophat_Mm_Indx5/accepted_hits.bam,tophat_Mm_Indx9/accepted_hits.bam,tophat_Mm_Indx10/accepted_hits.bam tophat_Mm_Indx6/accepted_hits.bam,tophat_Mm_Indx11/accepted_hits.bam,tophat_Mm_Indx12/accepted_hits.bam /home/kunzhang/softwares/cufflinks-1.0.3.Linux_x86_64/cuffdiff /GenomeDB/UCSC/Mus_musculus/mm9/Annotation/Genes/genes.gtf -N -p 8 -o cuffDiff_wtE13.5f_vs_koE13.5f tophat_Mm_Indx6/accepted_hits.bam,tophat_Mm_Indx11/accepted_hits.bam,tophat_Mm_Indx12/accepted_hits.bam tophat_Mm_Indx14/accepted_hits.bam,tophat_Mm_Indx15/accepted_hits.bam Wild-type: E9.5 vs. E11.5 Wild-type: E11.5 vs. E13.5-female [[Image:FPKM_wtE9.5_vs_wtE11.5.png|300px]][[Image:FPKM_wtE11.5_vs_wtE13.5f.png|300px]] [[Media:FPKM_wtE9.5_vs_wtE11.5.xlsx|Data]] [[Media:FPKM_wtE11.5_vs_wtE13.5f.png.xlsx|Data]] Wild-type: E113.5-male vs. E13.5-female E13.5-female: wild-type vs. TET1-KO [[Image:FPKM_wtE13.5m_vs_wtE13.5f.png|300px]][[Image:FPKM_wtE13.5f_vs_koE13.5f.png|300px]] [[Media:FPKM_wtE13.5m_vs_wtE13.5f.xlsx|Data]] [[Media:FPKM_wtE13.5f_vs_koE13.5f.png.xlsx|Data]] *I randomly picked a few genes that showed biggest differences, and checked their functions. Clearly the data make sense biologically. Examples include: **Dazl and Sohlh2 were up-regulated by over 100-fold from E9.5 to E11.5. **Taf7l was up-regulated by ~50 folds from E11.5 to E13.5f. **Lefty1 and Lefty2 expressed at >100x higher in E13.5m than in E13.5f. **Tet1's expression was almost completely lost in ko-E13.5f. ===Global analysis of all data sets=== *First I need to combine the gene expression levels (FPKM) for all data sets in one matrix. For this I write a script [[Media:combine_geneTracking.txt|combine_geneTracking.pl]], and ran it in the directory:/home/kunzhang/SingleCell/Data/HL101 *For hierarchical clustering analysis, missing values are allowed in the matrix. I loaded the matrix into R, and create a dendrogram using the following commands: x=read.table("Mm_Indx3-15_FPKM_matrix.txt",header=TRUE,row.names=1); x.cor = cor(x,use="pairwise.complete.obs",method="pearson") x.d = as.dist(1-x.cor) x.h = hclust(x.d,"average") plot(x.h) [[Image:PGC-RNAseq-Hierarchical_analysis_092011.png|400px]] *Replicates tend to cluster together, suggesting that the quality of the data is good. *For PCA analysis, no missing value is allowed. So I set $no_missing_values = 1, and re-created the matrix. The PCA plot was generated with the following commands: methylMatrix=read.table("Mm_Indx3-15_FPKM_matrix_no_NA.txt",header=TRUE,row.names=1) pca1 <- prcomp(t(methylMatrix), scale=TRUE) mycolors=c("cyan", "green", "blue", "magenta","cyan", "green", "blue", "blue","magenta","magenta","dark red","red","red"); plot(pca1$x, pch=20, col=mycolors); plot(pca1$x, type="n") text(pca1$x, rownames(pca1$x), cex=0.8, col=mycolors) [[Image:PGC-RNAseq-PCA_plot_092011.png|500px]] *PC1 seems to explain the difference in developmental stage, whereas PC2 accounts for biological or technical variability among replicates. *Tet1 knock-out seem to slow down the development into the E13.5 stage.
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