ORdensity is a package for the automated discovery of differentially expressed genes. It makes use of the ORdensity method and the associated FP and dFP values to detect the most likely differentially expressed (DE) genes. The details of the method are explained in (Martínez-Otzeta, J. M. et al. 2020; Irigoien, I., and Arenas, C. 2018).
José María Martínez Otzeta firstname.lastname@example.org
Itziar Irigoien email@example.com
Concepción Arenas firstname.lastname@example.org
Basilio Sierra email@example.com
Irigoien, I. and Arenas, C. (2018) Identification of differentially expressed genes by means of outlier detection. BMC Bioinformatics, 19:317
Martínez-Otzeta, J. M., Irigoien, I., Sierra, B., & Arenas, C. (2020). ORdensity: user-friendly R package to identify differentially expressed genes. BMC Bioinformatics, 21, 1-10.
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# There is an example dataframe called simexpr shipped with the package. This data is the # result of a simulation of 100 differentially expressed genes in a pool of 1000 genes. It # contains 1000 observations of 62 variables. Each row correspond to a gene and contains 62 values: # DEgen, gap and the values for the gene expression in 30 positive cases and in 30 negative cases. # The DEgen field value is 1 for differentially expressed genes and 0 for those which are not. # # First, let us extract the samples from each experimental condition from the simexpr database. # For the sake of brevity, we will work with a subset of the database # simexpr_reduced <- simexpr[c(1:15,101:235),] x <- simexpr_reduced[, 3:32] y <- simexpr_reduced[, 33:62] EXC.1 <- as.matrix(x) EXC.2 <- as.matrix(y) # # To create an S4 object to perform the analysis, follow this command # myORdensity <- new("ORdensity", Exp_cond_1 = EXC.1, Exp_cond_2 = EXC.2, B = 20) # # where B = 20 is the number of bootstraps replicates. # # A summary of the object can be generated with the summary function. # summary(myORdensity) # # The summary tells us the estimated optimal clustering of the data, and the number of genes in # each cluster, along with their names. The clusters are ordered in decreasig order according to # the value of the mean of the OR statistic. We see that the mean is higher in the first cluster # than in the second one, which means that the first cluster is more likely composed of true # differentially expressed genes, and the second one less likely. With any number of clusters, the # last ones are likely false negatives. # # If the researcher just wants to extract the differentially expressed genes detected by the # ORdensity method, a call to findDEgenes will return a list with the clusters found, along with # the values of the OR statistic corresponding to each gene, and an indicator showing if the gene # fulfil the strong and/or relaxed selection requirements. Following (Irigoien, I., and Arenas, C. # 2018), two types of differentially expressed gene selection can be made: # # ORdensity strong selection: take as differentially expressed genes those with a large OR value # and with FP and dFP equal to 0. # # ORdensity relaxed selection: take as differentially expressed genes those with a large OR # value and with small FP and dFP values. As a reference to look for small values the expected # number of false positive neighbours is computed. # # The motivation of the clustering is to distinguish those false positives that score high in OR # and low in meanFP and density, but are similar to other known false positives obtained by # bootstrapping. The procedure is detailed in (Irigoien, I., and Arenas, C. 2018) and it uses the # PAM cluster procedure. # # After running this code # result <- findDEgenes(myORdensity) # # the method indicated the numbers of clusters in the optimal clustering, and then we could look # the results # result # # As a rule of thumb, differentially expressed genes are expected to present high values of OR # and low values of meanFP and density. We could also analyze each gene individually inside each # cluster. The motivation of the clustering is to distinguish those false positives that score # high in OR and low in meanFP and density, but are similar to other known false positives # obtained by boostrapping. The procedure is detailed in (Irigoien, I., and Arenas, C. 2018). # # If the researcher is interested in a more thorough analysis, other functions are at their service. # # The data before being clustered can be obtained with the following function # preclusteredData(myORdensity) # # A plot with a representation of the potential genes based on OR (vertical axis), FP (horizontal # axis) and dFP (size of the circle is inversely proportional to its value) can also be obtained. # Genes that fulfil the relaxed criterion are drawn with triangles. # plot(myORdensity) # # By default, the number of clusters computed by the ORdensity method is used. Other values for # the number of clusters can be specified. # plot(myORdensity, numclusters = 5)
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