Dear All, Im am working on a comparison study, I used Limma and DESeq to get the DEGs at 3 different timepoints, 13989 genes. Microarray data: 8 samples (d0d1d3d6d0d1d3d6), rnaseq data: 9 samples (d0d0d0d1d1d3d3d6d6). I would like to make correlation plots to compare the deferentially expression for each gene and the means between the rnaseq and microarray data. I would like to get correlation plots with the correlation coefficient (y-axis) and the mean expression (x-axis) of my microarray data, rnaseq data and compared. Below you see my data. Can anybody help me to get started on this. ## data rnaseq <- read.table("rnaseq.data.csv", sep=",", row.names=2,header=T) ma <- read.table("results.txt", header = TRUE, sep = "\t", row.names=1) > dim(rnaseq)[1] 13989 15> dim(ma)[1] 13989 22> rnaseq[1:3,] X Predicted.function d0mean d1mean d3mean d6mean d1d0log2FC d3d0log2FC d6d0log2FC An00g03020 An01g00010 hypothetical protein [truncated ORF] 22.56114 91.74978 91.6181 101.3439 2.0238643949309 2.02179230258879 2.16734734277586 An00g13235 An01g00020 weak similarity to nucleotide binding protein phnN - Escherichia coli 59.12810 157.12747 186.8015 195.0879 1.41001967811839 1.65959001130216 1.72220839890842 An00g08601 An01g00030 strong similarity to Hgh1 - Saccharomyces cerevisiae 2496.49429 877.02756 1232.1749 1349.5057 -1.50920952339683 -1.01869652997083 -0.887472497442684 d1d0pv d3d0pv d6d0pv d1d0qv d3d0qv d6d0qv An00g03020 5.94705054637849e-07 6.31152681691883e-07 7.02200948285023e-08 1.54756050288183e-06 1.81541452315907e-06 2.0525753410285e-07 An00g13235 4.49160343422521e-06 5.1414728365481e-08 1.88017590489281e-08 1.07953906946646e-05 1.63260807365055e-07 5.78213645833647e-08 An00g08601 1.77223201566024e-15 5.51217466606722e-08 1.8646154133324e-06 8.57739275778034e-15 1.74607849308206e-07 4.7836199300348e-06> ma[1:3,] GSM542228.CEL.gz GSM542335.CEL.gz GSM542336.CEL.gz GSM971682_080805MJA_ANIGERa_100480_03.CEL.gz GSM971683_080805MJA_ANIGERa_100480_07.CEL.gz An08g08350 4.321879 4.445359 4.252581 4.490013 4.381501 An07g04510 3.730946 3.792345 3.809127 4.064768 3.836707 An13g01430 3.288621 3.294919 3.316417 3.698545 3.647623 GSM971684_080805MJA_ANIGERa_100480_04.CEL.gz GSM971685_080805MJA_ANIGERa_100480_08.CEL.gz GSM971686_080805MJA_ANIGERa_100480_05.CEL.gz GSM971687_080805MJA_ANIGERa_100480_09.CEL.gz An08g08350 4.205638 4.307239 4.144671 4.255288 An07g04510 4.085377 3.772753 3.735737 3.740884 An13g01430 3.295512 3.318408 3.247576 3.346043 d0mean d1mean d3mean d6mean d1d0log2FC d3d0log2FC d6d0log2FC d1d0pv d3d0pv d6d0pv d1d0qv d3d0qv d6d0qv An08g08350 20.251257 21.64193 19.112415 18.378909 0.09581765 -0.083501384 -0.13996034 0.2810984767 0.3446514 0.1254112 0.3665784088 0.4153454 0.1768703 An07g04510 13.713003 15.46289 15.232332 13.345771 0.17326503 0.151592308 -0.03916187 0.1125180178 0.1597735 0.7050570 0.1698332488 0.2163462 0.7559045 An13g01430 9.849057 12.75582 9.896785 9.827398 0.37309849 0.006974256 -0.00317616 0.0002314352 0.9237584 0.9652310 0.0007124885 0.9395417 0.9726008 # Correlation of the RNA-Seq means rnaseq.mean.cor <- cor(rnaseq[c(3, 4, 5, 6)]) > rnaseq.mean.cor d0mean d1mean d3mean d6mean d0mean 1.0000000 0.2882263 0.4246321 0.3392603 d1mean 0.2882263 1.0000000 0.7513139 0.6459144 d3mean 0.4246321 0.7513139 1.0000000 0.9496546 d6mean 0.3392603 0.6459144 0.9496546 1.0000000 ## Correlation of the microarray means ma.mean.cor <- cor(ma[c(10, 11, 12, 13)]) > ma.mean.cor d0mean d1mean d3mean d6mean d0mean 1.0000000 0.5614268 0.6269302 0.6293948 d1mean 0.5614268 1.0000000 0.8600314 0.7722005 d3mean 0.6269302 0.8600314 1.0000000 0.9618639 d6mean 0.6293948 0.7722005 0.9618639 1.0000000 ## qplot RNA-Seq rnaseq.d0d1.qplot <- qplot(data=rnaseq,x=d0mean,y=d1mean,log="xy", main = "qplot RNA-Seq d0mean-d1mean") rnaseq.d0d3.qplot <- qplot(data=rnaseq,x=d0mean,y=d3mean,log="xy", main = "qplot RNA-Seq d0mean-d3mean") rnaseq.d0d6.qplot <- qplot(data=rnaseq,x=d0mean,y=d6mean,log="xy", main = "qplot RNA-Seq d0mean-d6mean") ## qplot microarray ma.d0d1.qplot <- qplot(data=ma,x=d0mean,y=d1mean,log="xy", main = "qplot microarray d0mean-d1mean") ma.d0d3.qplot <- qplot(data=ma,x=d0mean,y=d3mean,log="xy", main = "qplot microarray d0mean-d3mean") ma.d0d6.qplot <- qplot(data=ma,x=d0mean,y=d6mean,log="xy", main = "qplot microarray d0mean-d6mean") Thank you for your help! Willemijn van Mossevelde -- *Willemijn van Mossevelde* Tempelhofer Ufer 6/6a 10963 Berlin, Germany *+491575-4422928* Morsweg 48 2312 AE Leiden, The Netherlands *+316-11912998* [[alternative HTML version deleted]]