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tutorial [2013/03/27 11:11] sotacam |
tutorial [2013/03/30 11:07] sotacam |
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| Please, follow these instructions to run NOISeqBIO: | Please, follow these instructions to run NOISeqBIO: | ||
| - | - Install NOISeq Bioconductor package (see [[downloads|Downloads]]). | + | **1)** Install NOISeq Bioconductor package (see [[downloads|Downloads]]) and load it: ''library(NOISeq)''. |
| - | - Download the zip file containing the R scripts for NOISeqBIO (from [[downloads|Downloads]]). | + | |
| - | - Unzip this file. | + | **2)** Download the zip file containing the R scripts for NOISeqBIO (from [[downloads|Downloads]]) and unzip it. |
| - | - To load the NOISeqBIO functions from the R console, please type: ''> source("NOISeqBIO_v00/loadingFunctions.R")''. Remember you have to change the path if you are in another working directory. | + | |
| - | - Convert your data into a NOISeq object using the //readData()// function from NOISeq package (see NOISeq tutorial for more information). | + | **3)** To load the NOISeqBIO functions in R from your working directory: |
| - | - | + | |
| + | # Remember you have to change this path if you are not in the same directory: | ||
| + | > mypath = "NOISeqBIO_v00" | ||
| + | |||
| + | > source(file.path(mypath, "auxiliar.R")) | ||
| + | > source(file.path(mypath, "MDbio.R")) | ||
| + | > source(file.path(mypath, "allMDbio.R")) | ||
| + | > source(file.path(mypath, "noiseqbio.R")) | ||
| + | > source(file.path(mypath, "classes.R")) | ||
| + | > source(file.path(mypath, "normalization.R")) | ||
| + | > source(file.path(mypath, "fewreplicates.R")) | ||
| + | |||
| + | **4)** Read your data as in NOISeq (see the following example with Marioni's data, where liver and kidney tissues are compared) to convert them to a NOISeq object: | ||
| + | > data(Marioni) | ||
| + | > mydata = readData(data = mycounts, factors = myfactors) | ||
| + | |||
| + | **5)** To apply NOISeqBIO with TMM normalization (you can choose the same normalization procedures as in NOISeq): | ||
| + | > mynoiseq.test = noiseqbio(mydata, norm = "tmm", factor = "Tissue", r = 10) | ||
| + | The parameter //r// is the number of permutations of sample labels to generate null distribution. | ||
| + | |||
| + | **6)** Finally, select the differentially expressed genes: | ||
| + | # Probability cutoff. It is equivalent to a FRD (adjusted p-value) of 0.05 | ||
| + | > myq = 0.95 | ||
| + | > mydeg = mynoiseq.test@results[[1]][which(mynoiseq.test@results[[1]][,"prob"] > myq),] | ||
| + | > nrow(mydeg) | ||
| + | [1] 2837 | ||
| + | > head(mydeg) | ||
| + | Kidney Liver theta prob | ||
| + | ENSG00000187642 12.8606633 4.332902 0.8107475 0.9601231 | ||
| + | ENSG00000188290 17.7544458 4.961376 1.3160369 0.9999999 | ||
| + | ENSG00000187608 19.2702201 34.329320 -0.7153955 0.9543329 | ||
| + | ENSG00000188157 691.1874857 141.575959 5.9237953 1.0000000 | ||
| + | ENSG00000186891 0.8230924 2.736435 -0.7348520 0.9568449 | ||
| + | ENSG00000078808 372.5016774 483.708484 -1.0066939 0.9787270 | ||
| + | Please, remember that this is a toy example and only about 5000 genes are considered, so the results may not be trustable. | ||
| + | |||
| + | Unfortunately, the DE plots in NOISeq package cannot be used yet on these DE results. Please, find below how to draw a plot to illustrate the DE results: | ||
| + | > cond1 = log2(rowMeans(mycounts[,grep("Kidney", colnames(mycounts))]) + 1) | ||
| + | > cond2 = log2(rowMeans(mycounts[,grep("Liver", colnames(mycounts))]) + 1) | ||
| + | > plot(cond1, cond2, cex = 0.7, xlab = "Kidney (Average expression in log-scale)", | ||
| + | ylab = "Liver (Average expression in log-scale)", main = "NOISeqBIO on Marioni's data") | ||
| + | > points(cond1[rownames(mydeg)], cond2[rownames(mydeg)], pch = 20, col = "red", cex = 0.5) | ||
tutorial.txt · Last modified: 2014/01/08 11:55 by sotacam
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