# Normalization of data with rpkm

I'm very i difficult with normalization of my data. I was searching for transposable elements in my genome, and after this step, I made counts of reads in some transcripts. I produced something like that:

head(table_tissues_filtered_TE)
Lengths ova testes lobe retina suckers brain1 brain2 skin stage
Simple_repeat_80      134  58     77   48     69     115     137  131  195     75
tRNA_1                 59   0     14   12      1      19      12   14   21    104
Simple_repeat_87       26   1     33   12      3      15      24   21   19    180
Simple_repeat_114      22   0      0    0      1       0       0    0    2      7
Simple_repeat_115      30   0      0    0      0       0       0    0    0      1
Simple_repeat_123      22   2      3   317     45      13    652  651   15     21
axial                    gland viscera
Simple_repeat_80                 99                       35     557
tRNA_1                            9                        0       3
Simple_repeat_87                  9                        0       4
Simple_repeat_114                 0                      204       0
Simple_repeat_115                 0                       42       0
Simple_repeat_123               333                        5       4


where Lengths are the Length of each elements (simple repeats, etc), and the other columns indicate the reads counted with Featurecounts. I've another thable with the number of reads for each tissues:

head(reads_table)
ova   testes lobe   retina  suckers   brain1  brain2  skin   stage   axial
522444 310243 226146  102307  126055   489389  668243  372728 262536  233754
gland  viscera
24817   25689


I would make a RPKM analysis to normalize the data using R, but I don't know exactly of to do it. Probably I should use a cycle for but I don't know how to apply it.Anyone can help me? thank you!!!

• RPKM is Reads per kilobase of transcript / 1 million so this is a normalized value. Wondering what has 2 brains and suckers though. – glyph Jun 9 at 22:37
• Could you please explain a bit more about what you're trying to (i.e. the general problem you're trying to find a solution to)? It's very unlikely that "a RPKM analysis" is the right answer. Assuming you'd like to do differential expression, using tools like DESeq or EdgeR on the count table are likely to be a better thing to do. – gringer Jun 10 at 0:33
• To rephrase gringer’s comment more strongly: Do not use RPKM. Read bioinformatics.stackexchange.com/a/69/29. – Konrad Rudolph Jun 10 at 11:15