heatmap in R
1
0
Entering edit mode
@ifaizurahredzuan-8701
Last seen 6.1 years ago

i was trying to generate heatmap from the gene expression data.. and then suddenly it came out like this. what should i do? should i add some memory into my laptop or what? thanks


  Reached total allocation of 3957Mb: see help(memory.size)
2: In distfun(x) :
  Reached total allocation of 3957Mb: see help(memory.size)
3: In distfun(x) :
  Reached total allocation of 3957Mb: see help(memory.size)
4: In distfun(x) :
  Reached total allocation of 3957Mb: see help(memory.size)

r language microarray geneexpression • 904 views
ADD COMMENT
1
Entering edit mode
Axel Klenk ▴ 980
@axel-klenk-3224
Last seen 19 hours ago
Switzerland

Dear ifaizurahredzuan

well, first of all, you could do what the error message suggests and read help(memory.size).

Most likely, adding some memory will be one option as you have guessed.

You could also try to remove some large objects from your workspace or the least informative genes

from your dataset in order to decrease memory usage (if you haven't already tried this -- you don't tell us).

For a heatmap in particular, I wouldn't use more genes than the laptop screen can display...

Best regards,

 - axel

 

 

ADD COMMENT
0
Entering edit mode

Thanks Axel. im sorry if i asked such obvious question like that. anyway. i want to do an undergraduate research, and the scope is analyzing the gene expression data . this is  my methodology

1. to look for differential gene expression of some genes of a disease

2. generation of heatmap

3. pathway analyses using gene ontology consortium.

 

i dont know who should i look up to, kinda lost here. is the methodology relevant? im using R for method no 1 & 2

ADD REPLY
1
Entering edit mode

All three are quite common and can certainly be handled with R/Bioconductor.

For 1. I'd recommend using the limma package and reading its excellent and comprehensive user's guide.

limma also has a function goana() that may help with 3.

If you keep running in memory issues with 2., try filtering out uninformative, i.e. low-variance, genes.

At least that's what I would try.

 

ADD REPLY
0
Entering edit mode

 

 

ADD REPLY
0
Entering edit mode

aha -- I'm working on linux and have no experience with memory issues on windows but the obvious next questions seem to be: is it a 64-bit windows? and are you running the 64-bit version of R? if the answer is yes two times I'm afraid I cannot help, maybe somebody else can?

 

ADD REPLY

Login before adding your answer.

Traffic: 149 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