Limma: Warning Messages -- rlm -- Is there a way to fix it?
1
0
Entering edit mode
alakatos ▴ 130
@alakatos-6983
Last seen 4.5 years ago
United States

Hello All,

At the end of my DE  analysis  in limma I received the following warning message: "There were 50 or more warnings (use warnings() to see the first 50)"

warnings()Warning messages:
1: In rlm.default(x = X, y = y, weights = w, ...) : some of ... do not match  'rlm' failed to converge in 20 steps etc.

I tried  to go around  the problem by increasing the maxit option to 50. Unfortunately, it did not solve the problem.

Would you please advise if there is a solution to this problem or  shall I neglect it ?

Thank you for your help and time in advance.

Anita    

My code:

Elistraw = read.idat(idatfiles, bgxfile)

y <- neqc(Elistraw)​
des <- model.matrix(~ dx + batch + sex + age + source + pluritestscore + depression)
des
(Intercept) dxDem dxMCI batch2 batch3 batch4 sexFemale age sourcePBMC pluritestscore depressionDep
1           1     0     1      0      0      0         1  80          0         21.261             0
2           1     1     0      0      0      0         0  81          0         19.290             0
3           1     0     0      0      0      0         0  83          0         23.087             0
4           1     0     0      0      0      0         1  67          0         21.847             0
5           1     0     1      0      0      0         1  67          0         23.738             0
6           1     1     0      0      0      0         0  75          0         23.901             1

fit <- eBayes(lmFit(y,des,trend = TRUE, method="robust"))

There were 50 or more warnings (use warnings() to see the first 50)

fit <- eBayes(lmFit(y,des,trend = TRUE, method="robust", maxit=50))​

There were 50 or more warnings (use warnings() to see the first 50)

Please let me know if you need additional information!

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     
other attached packages:
 [1] Hmisc_4.0-0           foreach_1.4.3         reshape_0.8.6         ggplot2_2.2.0         Formula_1.2-1        
 [6] survival_2.40-1       lattice_0.20-34       flashClust_1.01-2     dynamicTreeCut_1.63-1 cluster_2.0.5        
[11] dplyr_0.5.0           illuminaio_0.12.0     magrittr_1.5          limma_3.30.6         
loaded via a namespace (and not attached):
 [1] Rcpp_0.12.8         RColorBrewer_1.1-2  plyr_1.8.4          iterators_1.0.8     tools_3.3.1         digest_0.6.10      
 [7] rpart_4.1-10        base64_2.0          tibble_1.2          gtable_0.2.0        htmlTable_1.7       Matrix_1.2-7.1     
[13] DBI_0.5-1           gridExtra_2.2.1     stringr_1.1.0       knitr_1.15.1        grid_3.3.1          nnet_7.3-12        
[19] data.table_1.10.0   R6_2.2.0            foreign_0.8-67      latticeExtra_0.6-28 MASS_7.3-45         codetools_0.2-15   
[25] htmltools_0.3.5     scales_0.4.1        splines_3.3.1       rsconnect_0.6       assertthat_0.1      colorspace_1.3-1   
[31] stringi_1.1.2       acepack_1.4.1       lazyeval_0.2.0      openssl_0.9.5       munsell_0.4.3      

 

 

limma warning message • 4.8k views
ADD COMMENT
1
Entering edit mode
@gordon-smyth
Last seen 6 minutes ago
WEHI, Melbourne, Australia

You may be able to just ignore this warning. There is no easy fix anyway. It might be affecting only a subset genes, although it is admittedly hard to check this from the error message. 

I was the one who added the robust option to lmFit() more than 12 years ago, but over the years I have found it to be very seldom necessary. In particular, I've never found it be necessary for Illumina microarray data. So I would suggest you simply try the analysis without using this option. The way that limma analyses the data on the log-intensity scale is quite robust anyway. limma has other ways to deal with outlier samples or outlier genes.

ADD COMMENT
0
Entering edit mode

I really appreciate your valuable advice.

 

 

ADD REPLY

Login before adding your answer.

Traffic: 631 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6