**50**wrote:

Hello, I'm trying to understand how to analyze microarray data with R and Bioconductor. I found this quite nice and complete course for debutant (both for R and microarray analysis) : Link

However, I'm stuck with "Exercise 8 : create a heat map of normalized intensities of the first 50 genes using ggplot()" in the "Tutorial: Comparing two groups of samples"

So briefly, I came to the point where I have RMA normalized expression data in a data matrix, obtained with :

```
data = ReadAffy(celfile.path=celpath)
data.rma = rma(data)
data.matrix = exprs(data.rma)
```

Then, here is the part of code in the correction that block for me to create the heatmap :

```
#Exercise8: create a heat map of the first 50 genes using ggplot()
sampleNames = vector()
featureNames = vector()
heatlogs = vector()
for (i in 1:6)
{
sampleNames = c(sampleNames,rep(ph@data[i,1],50))
featureNames = c(featureNames,rownames(data.matrix[100:150,]))
heatlogs = c(heatlogs,data.matrix[100:150,i])
}
heatData = data.frame(norm_logInt=heatlogs,sampleName=sampleNames,featureName=featureNames)
dataHeat = ggplot(heatData, aes(sampleName,featureName))
dataHeat + geom_tile(aes(fill=norm_logInt)) +
scale_fill_gradient(low="green", high="red")
```

More precisely, it's the heatData that don't work, returning this error :

```
Error in data.frame(norm_logInt = heatlogs, sampleName = sampleNames, :
arguments imply differing number of rows : 306, 300
```

So I kind of understand what is happening here, I have between sampleNames, featureNames and heatlogs a different number of rows. However, I can't figure out where or what is the probleme above this

In order for you to see them :

```
> featureNames
[1] "245000_at" "245001_at" "245002_at" "245003_at" "245004_at" "245005_at"
[7] "245006_at" "245007_at" "245008_at" "245009_at" "245010_at" "245011_at"
[13] "245012_at" "245013_at" "245014_at" "245015_at" "245016_at" "245017_at"
[19] "245018_at" "245019_at" "245020_at" "245021_at" "245022_at" "245023_at"
[25] "245024_at" "245025_at" "245026_at" "245027_at" "245028_at" "245029_at"
[31] "245030_at" "245031_at" "245032_at" "245033_at" "245034_at" "245035_at"
[37] "245036_at" "245037_at" "245038_at" "245039_at" "245040_at" "245041_at"
[43] "245042_at" "245043_at" "245044_at" "245045_at" "245046_at" "245047_at"
[49] "245048_at" "245049_at" "245050_at" "245000_at" "245001_at" "245002_at"
[55] "245003_at" "245004_at" "245005_at" "245006_at" "245007_at" "245008_at"
[61] "245009_at" "245010_at" "245011_at" "245012_at" "245013_at" "245014_at"
[67] "245015_at" "245016_at" "245017_at" "245018_at" "245019_at" "245020_at"
[73] "245021_at" "245022_at" "245023_at" "245024_at" "245025_at" "245026_at"
[79] "245027_at" "245028_at" "245029_at" "245030_at" "245031_at" "245032_at"
[85] "245033_at" "245034_at" "245035_at" "245036_at" "245037_at" "245038_at"
[91] "245039_at" "245040_at" "245041_at" "245042_at" "245043_at" "245044_at"
[97] "245045_at" "245046_at" "245047_at" "245048_at" "245049_at" "245050_at"
[103] "245000_at" "245001_at" "245002_at" "245003_at" "245004_at" "245005_at"
[109] "245006_at" "245007_at" "245008_at" "245009_at" "245010_at" "245011_at"
[115] "245012_at" "245013_at" "245014_at" "245015_at" "245016_at" "245017_at"
[121] "245018_at" "245019_at" "245020_at" "245021_at" "245022_at" "245023_at"
[127] "245024_at" "245025_at" "245026_at" "245027_at" "245028_at" "245029_at"
[133] "245030_at" "245031_at" "245032_at" "245033_at" "245034_at" "245035_at"
[139] "245036_at" "245037_at" "245038_at" "245039_at" "245040_at" "245041_at"
[145] "245042_at" "245043_at" "245044_at" "245045_at" "245046_at" "245047_at"
[151] "245048_at" "245049_at" "245050_at" "245000_at" "245001_at" "245002_at"
[157] "245003_at" "245004_at" "245005_at" "245006_at" "245007_at" "245008_at"
[163] "245009_at" "245010_at" "245011_at" "245012_at" "245013_at" "245014_at"
[169] "245015_at" "245016_at" "245017_at" "245018_at" "245019_at" "245020_at"
[175] "245021_at" "245022_at" "245023_at" "245024_at" "245025_at" "245026_at"
[181] "245027_at" "245028_at" "245029_at" "245030_at" "245031_at" "245032_at"
[187] "245033_at" "245034_at" "245035_at" "245036_at" "245037_at" "245038_at"
[193] "245039_at" "245040_at" "245041_at" "245042_at" "245043_at" "245044_at"
[199] "245045_at" "245046_at" "245047_at" "245048_at" "245049_at" "245050_at"
[205] "245000_at" "245001_at" "245002_at" "245003_at" "245004_at" "245005_at"
[211] "245006_at" "245007_at" "245008_at" "245009_at" "245010_at" "245011_at"
[217] "245012_at" "245013_at" "245014_at" "245015_at" "245016_at" "245017_at"
[223] "245018_at" "245019_at" "245020_at" "245021_at" "245022_at" "245023_at"
[229] "245024_at" "245025_at" "245026_at" "245027_at" "245028_at" "245029_at"
[235] "245030_at" "245031_at" "245032_at" "245033_at" "245034_at" "245035_at"
[241] "245036_at" "245037_at" "245038_at" "245039_at" "245040_at" "245041_at"
[247] "245042_at" "245043_at" "245044_at" "245045_at" "245046_at" "245047_at"
[253] "245048_at" "245049_at" "245050_at" "245000_at" "245001_at" "245002_at"
[259] "245003_at" "245004_at" "245005_at" "245006_at" "245007_at" "245008_at"
[265] "245009_at" "245010_at" "245011_at" "245012_at" "245013_at" "245014_at"
[271] "245015_at" "245016_at" "245017_at" "245018_at" "245019_at" "245020_at"
[277] "245021_at" "245022_at" "245023_at" "245024_at" "245025_at" "245026_at"
[283] "245027_at" "245028_at" "245029_at" "245030_at" "245031_at" "245032_at"
[289] "245033_at" "245034_at" "245035_at" "245036_at" "245037_at" "245038_at"
[295] "245039_at" "245040_at" "245041_at" "245042_at" "245043_at" "245044_at"
[301] "245045_at" "245046_at" "245047_at" "245048_at" "245049_at" "245050_at"
> sampleNames
[1] "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1"
[11] "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1"
[21] "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1"
[31] "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1"
[41] "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1" "wt1"
[51] "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2"
[61] "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2"
[71] "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2"
[81] "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2"
[91] "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2" "wt2"
[101] "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3"
[111] "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3"
[121] "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3"
[131] "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3"
[141] "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3" "wt3"
[151] "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1"
[161] "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1"
[171] "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1"
[181] "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1"
[191] "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1" "apum1"
[201] "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2"
[211] "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2"
[221] "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2"
[231] "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2"
[241] "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2" "apum2"
[251] "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3"
[261] "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3"
[271] "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3"
[281] "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3"
[291] "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3" "apum3"
> head(heatlogs)
245000_at 245001_at 245002_at 245003_at 245004_at 245005_at
6.644076 13.221866 9.406594 10.387840 11.354398 3.398136
```

Thank you for your help !

**46k**• written 3.2 years ago by giroudpaul •

**50**

Have a think how many entries are there in data.matrix[100:150, i]

How many would there be in data.matrix[100:101, i] ?

4.4k