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updated 20 months ago by
Ram
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written 9.2 years ago by
yasjas
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The class1 and class2 variables are unnecessary in your code (use data$Health1 and data$cancer2)
If I understand, you are comparing healthy1 vs cancer 1 and for each line computing a t-test. With no replicates for each group... The concept of the t-test is that you estimate if the means of the groups differ, taking into account the mean and variance of your groups. You NEED replicates, else you can only compute a log fold change.
There are some very good R packages for differential analysis. Just precise us if cancer1/cancer2 are biological replicates, and tell us what are the units of your Healthy / cancer columns:
Are they from microarray or RNASeq data?
How are they computed from your raw data? Like raw read counts or FPKM for RNASeq for example
that s my data frame and I have Hepatocytes_B1, Hepatocytes_B2 as replicates and same for Huh7_b1, Huh7_b2
what I wanted to see, it s if there is any difference in the expression of the genes comparing healthy (hepatocytes) vs cancer(huh7) so I wanted to do Hepatocytes_b1 vs Huh7_b1 and Hepatocytes_b2 vs Huh7_b2, don't know if it s correct to do like that...
For your question those values come from an microarray
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updated 20 months ago by
Ram
44k
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written 9.2 years ago by
yasjas
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70
In this case, I suggest you use marray for post treatment of your reads (if it is not done yet and you are doing two colours microarray) and limma from the Bioconductor packages for the statistical analysis.
I had a really good tutorial http://pastebin.com/wVLD3Amy, you can find the R command lines here. The first part allows you to do some quality analysis, the second is about using limma to find differentially expressed genes. Some data files are missing, sorry, but the visualizations are nice.
When you are done, you can do some clustering using http://www.tm4.org/mev.html and classify your hits using Gene Ontology (GO) terms.
If you want more detailed help, tell us how you got your numerical values and what microarray you used. Once again, we can help you do t-test but they are not optimal here...
The class1 and class2 variables are unnecessary in your code (use
data$Health1
anddata$cancer2
)If I understand, you are comparing healthy1 vs cancer 1 and for each line computing a t-test. With no replicates for each group... The concept of the t-test is that you estimate if the means of the groups differ, taking into account the mean and variance of your groups. You NEED replicates, else you can only compute a log fold change.
There are some very good R packages for differential analysis. Just precise us if cancer1/cancer2 are biological replicates, and tell us what are the units of your Healthy / cancer columns:
that s my data frame and I have
Hepatocytes_B1
,Hepatocytes_B2
as replicates and same forHuh7_b1
,Huh7_b2
what I wanted to see, it s if there is any difference in the expression of the genes comparing healthy (hepatocytes) vs cancer(huh7) so I wanted to do
Hepatocytes_b1
vsHuh7_b1
andHepatocytes_b2
vsHuh7_b2
, don't know if it s correct to do like that...For your question those values come from an microarray