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)
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
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?
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
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.
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?