User: ewre

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ewre210
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Posts by ewre

<prev • 73 results • page 1 of 8 • next >
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Comment: C: Differentially expressed genes between sensitive and non sensitive with a simple
... Better to have a look at BH method of FDR correction. I tried to understand that but every time after I forgot it quickly :). By the way, [here][1] is a related question you may have a glance. [1]: https://stackoverflow.com/questions/29992944/how-does-r-calculate-the-false-discovery-rate ...
written 4 months ago by ewre210
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Answer: A: Pipeline for analyzing Microarray & RNA-seq GSE files from NCBI GEO
... Not sure if this [one][1] is helpful for you cause it deals with ArrayExpress which is EBI instead of NCBI. As I know, most of the datasets in GEO can be found in ArrayExpress. [1]: http://ewre.info/?p=1317 ...
written 4 months ago by ewre210
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Answer: A: remove NAs values from gset
... For your question, based on your code, you need to remove NAs from `gset` because `ex` has nothing to do with `gset` after it has been initialized with `exprs(gset)`. Hope I've get your point. What's more, I think it's better to **impute** the missing values rather than `na.omit` them, you will lo ...
written 4 months ago by ewre210
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Comment: C: Differentially expressed genes between sensitive and non sensitive with a simple
... Hi, regarding your comments: 1. `x1 <- t(y[group[,1]])` will make x1 as a matrix with one row and n columns. Although in your situation, the resulting t test pvalue is not affected by this, but it's dangerous like a time bomb you never know when it will explode. 2. for FDR correction part, two ...
written 4 months ago by ewre210
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Answer: A: Differentially expressed genes between sensitive and non sensitive with a simple
... For the code: First, if I am correct, you set wrong `MARGIN` parameter in `apply` function, should be `1` instead of `2`. you want to calculate across genes. Second, according to the help page of `t.test`, x1 and x2 should be `vector` instead of `matrix`. Third, better to make `my.t.test` return ...
written 4 months ago by ewre210
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Answer: A: RNA Seq- Performing pearson correlation with clinical variables
... Maybe spearman correlation between gene expression FPKM value and your clinical variable is a good choice. Also, if you find both gene A and gene B is correlated with, say, blood pressure. You'd better check whether gene A and gene B are correlated or not. ...
written 4 months ago by ewre210
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Answer: A: Extract only rows with main chromosomes (1-22, X, Y) on first column?
... A straight forward way: mainChr = c(as.character(1:22),'x','X','y','Y') data = read.delim('your.bed',stringsAsFactor = F,header = F) data_with_mainChr = data[data$V1 %in% mainChr,] if you want to use `readr` and `dplyr` which is more efficient when dealing with big files: ma ...
written 4 months ago by ewre210
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Answer: A: How can I perform Differential expression analysis with just one control and one
... You can't. But you can try to calculate fold change and pick out genes of interest based on fold change. Be careful because it will be noisy. ...
written 4 months ago by ewre210
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Answer: A: RankProd RP function length error
... Hi, you need at least >=2 samples in each group to run it. Otherwise, gr2 will become a vector instead of a matrix (nrow(grp2) is NULL) and the code thus get choked. ...
written 4 months ago by ewre210
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Answer: A: Gene Set Enrichment between two dataset
... Here you go if you want to use fisher's exact test. enrich.test = function(PathGenes, interestGenes, totalGenes) { ################################################ ### | inPath | not inPath | ###------------------------------------------- ### Predi ...
written 4 months ago by ewre210

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