ComBat should be done after normalization, and only of there are clear
signs of batch effects after normalization (either through
significance testing, clustering, or principle component analysis).
On Aug 21, 2013, at 12:33 AM, amit kumar subudhi wrote:
Hello Dr. Evan,
One more doubt, hopefully you will answer it. Is it recommended that
before doing ComBat, required normalization on the data should be
carried out or after ComBat we can do the normalization step? This
particular question making me confused. Please answer to this question
if you can.
With best regards
Amit
On Mon, Aug 19, 2013 at 7:12 PM, amit kumar subudhi
<amit4help@gmail.com<mailto:amit4help@gmail.com>> wrote:
This reply solved my problem. Thanks again Dr. Evan for your kind and
prompt reply and suggestions.
Regards
Amit
On Mon, Aug 19, 2013 at 7:08 PM, Johnson, William Evan
<wej@bu.edu<mailto:wej@bu.edu>> wrote:
Yes, it should be fine to remove batch effects on the larger dataset
and then use a smaller subset to do your comparisons. In fact, this
approach might even be preferred even if it were possible to adjust
for batch in the smaller subset.
On Aug 19, 2013, at 9:34 AM, amit kumar subudhi wrote:
Thanks again for the reply Dr. Evans,
This set of samples is a subset from a larger set and contain many
more samples in each batch. When I have performed the ComBat on the
larger dataset I could able remove the batch effects to some extend.
To Inform you, the known batch effect here is the different dates of
hybridization and a simple hierarchical analysis showed that most of
the samples are clustering based on the date of hybridization and
hence tried the ComBat to remove the batch effects. The third batch
contains most of the uncomplicated malaria samples. The subset of
samples that I have posted here contains specific symptoms pertaining
to severe malaria and hence selected for comparison with uncomplicated
malaria samples.
Question- As I have mentioned above, I have applied the ComBat to
remove the batch effects from the larger data set, can I take the
smaller set of samples from the larger data set to find out
deferentially regulated genes? Answer to this question would really be
helpful.
With best regards
Amit
On Mon, Aug 19, 2013 at 6:31 PM, Johnson, William Evan
<wej@bu.edu<mailto:wej@bu.edu>> wrote:
Okay, yes this is clear now. Your batch and covariate status are
completely confounded. In other words, if you see a difference between
"severe" and "uncomplicated" you won't know if this is really due to a
covariate effect or if this is due to a batch (batch 3) effect. In
short, this is really an experimental design issue and ComBat cannot
help you.
If you were to remove the "malaria" covariate, then ComBat would work,
but it would also take out all malaria covariate effects as well. How
bad are the batch effects between batches 1 and 2? Do you expect batch
3 to have a similar level of batch differences? You could combine
batches 1 and 2, and then look for differences with batch 3--but you
wouldn't know whether the differential expression is due to the
treatment or due to batch--hence the confounding...
Sorry I couldn't be much more of a help, but like I said, the issue
here is due to experimental design.
Evan
On Aug 19, 2013, at 8:55 AM, amit kumar subudhi wrote:
Hello Dr. Evan,
Thanks for the prompt reply. Below is the whole pheno table. Looking
at the whole table might give you an idea about the probable cause of
the error. Batch 1 and 2 contains only severe malaria samples where as
batch 2 contains uncomplicated malaria samples.
sample batch malaria
AL 1 1 Severe
AO 2 1 Severe
AQ 3 1 Severe
AP 4 1 Severe
CF 5 2 Severe
CL 6 2 Severe
CU 7 2 Severe
CV 8 2 Severe
GA_UC 9 3 uncomplicated
GB_UC 10 3 uncomplicated
GC_UC 11 3 uncomplicated
GE_UC 12 3 uncomplicated
GR_UC 13 3 uncomplicated
With best regards
On Mon, Aug 19, 2013 at 5:50 PM, Johnson, William Evan
<wej@bu.edu<mailto:wej@bu.edu>> wrote:
Amit,
The "singularity" error you are getting occurs when your covariates
are confounded with batch (or with each other). In the example you are
trying is there a batch that contains only one covariate level and is
that covariate level exclusive to the batch? If this does not make
sense, post your 'pheno' variable in a reply and I will be happy to
help you figure out the problem.
Evan
On Aug 19, 2013, at 6:00 AM, <bioconductor- request@r-project.org<mailto:bioconductor-request@r-project.org="">>
<bioconductor-request@r-project.org<mailto:bioconductor- request@r-project.org="">> wrote:
> Date: Sun, 18 Aug 2013 19:58:35 +0530
> From: amit kumar subudhi
<amit4help@gmail.com<mailto:amit4help@gmail.com>>
> To: bioconductor@r-project.org<mailto:bioconductor@r-project.org>
> Subject: [BioC] ComBat_ Error in solve.default(t(design) %*% design)
:
> Lapack routine dgesv: system is exactly singular: U[4, 4] = 0
> Message-ID:
> <cadxjrxwkyc3provl3rnmyc03qpyvh_vdvxvzymu- wkvmw+nkiw@mail.gmail.com<mailto="" :cadxjrxwkyc3provl3rnmyc03qpyvh_vdvxvzymu-="" wkvmw%2bnkiw@mail.gmail.com="">>
> Content-Type: text/plain
>
> Hello to all ComBat users,
>
> I am trying to remove the batch effects from some of my microarray
data but
> at last I am getting an error message which read as
>
> Found 3 batches
> Found 1 categorical covariate(s)
> Standardizing Data across genes
> Error in solve.default(t(design) %*% design) :
> Lapack routine dgesv: system is exactly singular: U[4,4] = 0
>
> The head(edata) looks like this
> AL AO AP AQ
CF
> GT_pfalci_specific_0000001 16.053898 16.080540 16.101114 16.046898
16.087206
> GT_pfalci_specific_0000002 10.051407 10.477143 8.369233 10.657850
13.312936
> GT_pfalci_specific_0000003 8.910620 8.683393 7.812817 8.496099
10.920685
> GT_pfalci_specific_0000004 6.603195 8.993232 6.476777 6.792369
3.319346
> GT_pfalci_specific_0000005 9.813562 11.084574 9.055613 11.568550
12.977261
> GT_pfalci_specific_0000006 15.989252 15.993513 15.963054 16.000675
15.983985
> CL CU CV GA_UC
GB_UC
> GT_pfalci_specific_0000001 16.082037 16.071299 16.090370 15.971335
15.994304
> GT_pfalci_specific_0000002 12.653076 9.703247 8.827624 5.697412
8.060719
> GT_pfalci_specific_0000003 11.470758 10.548943 10.718349 6.132614
8.007271
> GT_pfalci_specific_0000004 5.328515 8.398546 6.351136 3.045112
3.891578
> GT_pfalci_specific_0000005 8.520699 11.791610 11.535907 6.791468
9.930246
> GT_pfalci_specific_0000006 15.980660 15.984256 15.970124 13.353012
13.740395
> GC_UC GE_UC GR_UC
> GT_pfalci_specific_0000001 15.855644 16.090246 16.086956
> GT_pfalci_specific_0000002 9.026398 8.015609 7.814614
> GT_pfalci_specific_0000003 5.341252 8.658231 5.788790
> GT_pfalci_specific_0000004 4.191565 3.040515 3.517175
> GT_pfalci_specific_0000005 5.446910 11.982848 5.477334
> GT_pfalci_specific_0000006 11.872469 13.675290 13.117105
>
> GT_pfalci_specific_0000006 15.983985 15.970124
>
> and the head(pheno) looks like this
> sample batch malaria
> AL 1 1 severe
> AO 2 1 severe
> AP 3 1 severe
> AQ 4 1 severe
> CF 5 2 severe
> CL 6 2 severe
>
>
> the commands that I have used for ComBat is
> mod = model.matrix(~as.factor(malaria), data=pheno)
> combat_edata = ComBat(dat=edata, batch=batch, mod=mod, numCovs=NULL,
> par.prior=TRUE, prior.plots=FALSE)
>
> head(mod) looks like this
> (Intercept) as.factor(malaria)uncomplicated
> AL 1 0
> AO 1 0
> AP 1 0
> AQ 1 0
> CF 1 0
> CL 1 0
>
> Why I am getting this error meassage? Please help me out. When I am
taking
> the larger sample size (n=33) I could able to remove the batch
effects but
> a subset of those samples giving me the above problem.
>
>
> --
> Amit Kumar Subudhi
> Research Scholar,
> CSIR-Senior Research Fellow,
> Molecular Parasitology and Systems Biology Lab,
> Department of Biological Sciences ,
> FD III, BITS, Pilani,
> Rajasthan- 333031
> e mail-
> amit4help@gmail.com<mailto:amit4help@gmail.com>
> amit.subudhi@pilani.bits-pilani.ac.in<mailto:amit.subudhi@pilani .bits-pilani.ac.in="">
> Mob No- 919983525845
--
Amit Kumar Subudhi
Research Scholar,
CSIR-Senior Research Fellow,
Molecular Parasitology and Systems Biology Lab,
Department of Biological Sciences ,
FD III, BITS, Pilani,
Rajasthan- 333031
e mail-
amit4help@gmail.com<mailto:amit4help@gmail.com>
amit.subudhi@pilani.bits-pilani.ac.in<mailto:amit.subudhi@pilani.bits- pilani.ac.in="">
Mob No- 919983525845
--
Amit Kumar Subudhi
Research Scholar,
CSIR-Senior Research Fellow,
Molecular Parasitology and Systems Biology Lab,
Department of Biological Sciences ,
FD III, BITS, Pilani,
Rajasthan- 333031
e mail-
amit4help@gmail.com<mailto:amit4help@gmail.com>
amit.subudhi@pilani.bits-pilani.ac.in<mailto:amit.subudhi@pilani.bits- pilani.ac.in="">
Mob No- 919983525845
--
Amit Kumar Subudhi
Research Scholar,
CSIR-Senior Research Fellow,
Molecular Parasitology and Systems Biology Lab,
Department of Biological Sciences ,
FD III, BITS, Pilani,
Rajasthan- 333031
e mail-
amit4help@gmail.com<mailto:amit4help@gmail.com>
amit.subudhi@pilani.bits-pilani.ac.in<mailto:amit.subudhi@pilani.bits- pilani.ac.in="">
Mob No- 919983525845
--
Amit Kumar Subudhi
Research Scholar,
CSIR-Senior Research Fellow,
Molecular Parasitology and Systems Biology Lab,
Department of Biological Sciences ,
FD III, BITS, Pilani,
Rajasthan- 333031
e mail-
amit4help@gmail.com<mailto:amit4help@gmail.com>
amit.subudhi@pilani.bits-pilani.ac.in<mailto:amit.subudhi@pilani.bits- pilani.ac.in="">
Mob No- 919983525845
[[alternative HTML version deleted]]
