Hi,
I am very new to microarray analysis. I have some cancer and control samples (idat files) from illumina beadchip to analyze. I was following the tutorial on limma from ( https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf ), page 107.
When I check the EListRaw object, I get lots of ERCC as well which I did not expect!! (code and results are below).
- Why do I have spike ins?
- How can I remove them? (should I actually remove them?)
I ignored this and went through the downstream analysis but the FDR that I get is so hight (0.5 and above) which I think might be the result of I not removing ERCCs.
$ idatfiles = dir("path", pattern = "idat",full.names = TRUE)
$ bgxfile <- "my.bgx"
$ x = read.idat(idatfiles, bgxfile)
$ x$other$Detection <- detectionPValues(x)
$ table(x$genes$Status)
> biotin cy3_hyb ERCC-00002-02 ERCC-00003-01
2 6 1 1
ERCC-00004-01 ERCC-00009-01 ERCC-00012-01 ERCC-00013-01
1 1 1 1
ERCC-00014-02 ERCC-00016-01 ERCC-00017-02 ERCC-00019-01
1 1 1 1
ERCC-00022-02 ERCC-00024-02 ERCC-00025-01 ERCC-00028-02
1 1 1 1
ERCC-00031-02 ERCC-00033-01 ERCC-00034-02 ERCC-00035-02
1 1 1 1
ERCC-00039-01 ERCC-00040-01 ERCC-00041-01 ERCC-00042-01
1 1 1 1
ERCC-00043-01 ERCC-00044-02 ERCC-00046-01 ERCC-00048-01
1 1 1 1
ERCC-00051-01 ERCC-00053-01 ERCC-00054-01 ERCC-00057-01
1 1 1 1
ERCC-00058-02 ERCC-00059-01 ERCC-00060-01 ERCC-00061-02
1 1 1 1
ERCC-00062-01 ERCC-00067-02 ERCC-00069-02 ERCC-00071-01
1 1 1 1
ERCC-00073-01 ERCC-00074-01 ERCC-00075-01 ERCC-00076-02
1 1 1 1
ERCC-00077-01 ERCC-00078-01 ERCC-00079-01 ERCC-00081-02
1 1 1 1
ERCC-00083-01 ERCC-00084-01 ERCC-00085-01 ERCC-00086-01
1 1 1 1
ERCC-00092-02 ERCC-00095-01 ERCC-00096-02 ERCC-00097-01
1 1 1 1
ERCC-00098-02 ERCC-00099-01 ERCC-00104-01 ERCC-00108-02
1 1 1 1
ERCC-00109-02 ERCC-00111-01 ERCC-00112-02 ERCC-00113-01
1 1 1 1
ERCC-00116-02 ERCC-00117-02 ERCC-00120-01 ERCC-00123-01
1 1 1 1
ERCC-00126-02 ERCC-00130-01 ERCC-00131-02 ERCC-00134-01
1 1 1 1
ERCC-00136-01 ERCC-00137-02 ERCC-00138-01 ERCC-00142-02
1 1 1 1
ERCC-00143-01 ERCC-00144-02 ERCC-00145-01 ERCC-00147-01
1 1 1 1
ERCC-00148-01 ERCC-00150-01 ERCC-00154-02 ERCC-00156-01
1 1 1 1
ERCC-00157-02 ERCC-00158-01 ERCC-00160-02 ERCC-00162-01
1 1 1 1
ERCC-00163-01 ERCC-00164-01 ERCC-00165-01 ERCC-00168-01
1 1 1 1
ERCC-00170-01 ERCC-00171-01 housekeeping labeling
1 1 7 2
low_stringency_hyb negative regular
8 770 47231
•
written
by
maria2019
1 answer
You need to leave the control probes in the data for the purpose of background correction and normalisation. Then, if you perform background correction and normalisation via neqc(), these control probes should be automatically removed.
After normalisation, you can do further filtering based on the detection p-values. Any other control probes that still remain in the data may be identified via x$genes$Source. Others that can be filtered out include those with no gene symbol (x$genes$Symbol == "")
Thus, filtering that I perform post-normalisation is like this:
Control <- project.bgcorrect.norm$genes$Source=="ILMN_Controls"
NoSymbol <- project.bgcorrect.norm$genes$Symbol == ""
isexpr <- rowSums(project.bgcorrect.norm$other$Detection <= 0.05) >= 3
project.bgcorrect.norm.filt <- project.bgcorrect.norm[!Control & !NoSymbol & isexpr, ]
dim(project.bgcorrect.norm)
dim(project.bgcorrect.norm.filt)
Kevin
Log in to answer this question.