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*

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