What is the cutoff used for define high or low expression level of gene for survival analysis
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3.8 years ago
mygamefun3 • 0

Hi everyone

In RNA-seq analysis, we need to separate samples into two groups for survival analysis. How can I define high level or low level for a gene according to counts or FPKM. Use median? average or quantile?

In TCGA or Oncomine, how are they define the cutoff for a gene ?

Thanks.

RNA-Seq • 5.3k views
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3.8 years ago

There's no definitive answer to your question. I would not advise going by the median or average. Quantile is a reasonable idea, or tertiles, with the higher third being regarded as "high expression".

An even better idea would be to convert your data to the Z scale, i.e., standard deviations from the mean, and then choose absolute 3, 4, 5, or 6 (3, 4, 5, or 6 standard deviations from the mean) as potential cut-offs. I trust that you have QC'd your data already and that low count transcripts have been removed.

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Thanks for your advice.

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Hi Kevin,

I would like to look at survival of a specific gene between high and low expression tumor samples. For this, I want to divide tumor samples into high and low. I have counts data and transformed them into z-score.

Do you think the below one is right way to divide the samples?

event_rna <- t(apply(z_rna, 1, function(x) ifelse(x > 1.96,1,ifelse(x < -1.96,2,0))))


Or Should I follow this high and low samples separation

In that they used FPKM and took the median and based on that they separated samples into high and low.

Which one is the right solution?

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Hey bro, There is no right or wrong. Your function looks okay!

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Thank you very much Kevin.

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Dear kevin, i have used median cutoff via the code below, but for quantile how can i change the code?

median_value = median(clin_df$gene_value) clin_df$gene = ifelse(clin_df$gene_value >= median_value, "High expression", "Low expression")  it did work with quantile(clin_df$gene_value), but with warnings and i don't think this is the right way, and adding "Mid" is also face with error. thank you kindly.

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