Hi,

I am starting with RUVseq  and I have questions and some errors to understand-solve.

First (sorry if it is really naive question) I would like to know if it is possible to introduce ERCC spikes a posteriori in our data. I guess the answer will be no, but maybe you have tried or you have some suggestions. We have mouse ran-seq. 

I am using the other two alternatives with the next error messages:

For RUVs:

> differences<- matrix(data=c(1:2, 3:4), byrow=TRUE, nrow=2)

> differences

     [,1] [,2]

[1,]    1    2

[2,]    3    4

> set3<-RUVs(set,genes,k=1,differences)

Error in solve.default(a[, cIdx, drop = FALSE] %*% t(a[, cIdx, drop = FALSE]),  : 

  no right-hand side in 'b'

I guess the problem is the number of replicates (only 2) , but I don't know how to set it up correctly. 

For RUVr I think I have the same problem: 

> set4<-RUVr(set, genes, k=1, res)

Error in svd(E[, cIdx]) : a dimension is zero

I have been trying to set "cIdx" by reading the reference manual, but I couldn't achieve it to work properly. 

Thanks in advance. Best, 

S.

Hi,

I recently started to use RUV-seq too.

I'm not sure if I understand well your first question but the ERCC spikes have to be spiked into the RNAseq mix before sequencing.

You can try using housekeeping genes as controls.

For you error messages, could you tell us how you defined your 'set' and 'genes' object ?

Hi Marie,

Yeap, I know that spikes need to be introduced before, but as we didn't I was wondering if it is possible to introduce afterwards taking into a count sequencing depth. Maybe it is a bit crazy idea, but if housekeeping genes do not change much across samples I guess that it is not really difficult to predict their counts. It will be quite artificial for sure, but I was wondering if some tried to do it. A bit crazy maybe...sorry. 

'Set' and 'genes' defined as:

set<-newSeqExpressionSet(as.matrix(filtered),
phenoData=data.frame(x,row.names=colnames(filtered)))

genes<-rownames(filtered)[grep("ˆENS",rownames(filtered))]

Thanks a lot Marie, 
Sergio
 

Can you show the result of "head(genes)" please ?

Are you sure all your genes names start by "ENS" ?
 

Ok, I see the problem...

> genes<-rownames(filtered)[grep("ˆENS",rownames(filtered))]

> head(genes)

character(0)

> head(filtered)

                   WNN1 WNN2 WEN1 WEN2

ENSMUSG00000051951 2649 1290 2772 1660

ENSMUSG00000025902  262  555  275  498

ENSMUSG00000033845 1393 1388 1374 1616

ENSMUSG00000025903 1826 2029 1954 2231

ENSMUSG00000033813 4532 4441 4666 5300

ENSMUSG00000002459 1734 1817 1870 2332

> tail(filtered)

                   WNN1 WNN2 WEN1 WEN2

ENSMUSG00000081137  419  574  493  571

ENSMUSG00000035299  953 1550 1297 1687

ENSMUSG00000072844  559  689  597  977

ENSMUSG00000087263   83  120   68  153

ENSMUSG00000086695   10   21   14   35

ENSMUSG00000069053   53   46   46   39

How I can solve it? I did the table with featureCounts instead of HTSeq , and I did then 

EE<-read.table("counts.txt", row.names=1 ,  header=T )

colnames(EE)<-c( "Chr", "Start", "End", "Strand", "Length", "WNN1", "WNN2", "WEN1", "WEN2")

EEb <- EE[c('WNN1','WNN2','WEN1','WEN2')]

> head(EEb)

                   WNN1 WNN2 WEN1 WEN2

ENSMUSG00000090025    0    0    0    0

ENSMUSG00000064842    0    0    0    0

ENSMUSG00000051951 2649 1290 2772 1660

ENSMUSG00000089699    0    0    0    0

ENSMUSG00000088390    2    0    0    1

ENSMUSG00000089420    0    0    0    0

Any guess? Thanks a lot!

Sergio

Ok, ok. I have done 

genes<-rownames(filtered)

> head(genes)

[1] "ENSMUSG00000051951" "ENSMUSG00000025902" "ENSMUSG00000033845"

[4] "ENSMUSG00000025903" "ENSMUSG00000033813" "ENSMUSG00000002459"

And now both methods RUVr and RUVs are working, but I need to check if makes sense. I didn't realise that already all my gene names starts by ENS. 

Thanks a lot Marie!!! Yeah!!!

Sergio

Ok I think you did well !

I'm glad if it works now.

Best

Yeaaaaaah , yeah!!!  it worked!!!  I have check the edgeR results and seems nice.  Concerning my first naive question (XD) I didn't realise that there is some similar strategy explained in the documentation (sorry, always on rush) , check point 2.4 Empirical control genes. 

I am happy :D

Thanks a lot Marie!

Sergio