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tutorial [2013/03/27 11:28] 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. | + | |
- | - 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. | + | |
- | - Follow this example of application of NOISeqBIO with your own data: | + | |
- | > mydata = readData() # as in NOISeq (please go to the Tutorial for more information) | + | **2)** Download the zip file containing the R scripts for NOISeqBIO (from [[downloads|Downloads]]) and unzip it. |
- | > myresults = noiseqbio() | + | |
- | > myq = 0.95 # Cutoff for the probability of differential expression (1-FDR) | + | **3)** To load the NOISeqBIO functions in R from your working directory: |
- | > mydeg = | + | |
+ | # Remember you have to change this path if you are not in the same directory: | ||
+ | > mypath = "NOISeqBIO_v00" | ||
| | ||
- | Please, note that you can choose the same normalization procedures as in NOISeq. | + | > 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) |