| Title: | Reproducibility of Gene Expression Clusters |
|---|---|
| Description: | This is a function for validating microarray clusters via reproducibility, based on the paper referenced below. |
| Authors: | Amy Kapp <[email protected]> and Rob Tibshirani <[email protected]> |
| Maintainer: | Rob Tibshirani <[email protected]> |
| License: | GPL-2 |
| Version: | 0.9 |
| Built: | 2024-11-15 03:04:55 UTC |
| Source: | https://github.com/cran/clusterRepro |
Validate gene expression clusters by determining whether or not they are reproducible
clusterRepro(Centroids, New.data, Number.of.permutations)clusterRepro(Centroids, New.data, Number.of.permutations)
Centroids |
The matrix of centroids with annotated rows. The labeled rows are either genes (for gene clusters) or samples (for sample clusters) and the columns are the centroids. |
New.data |
The matrix of gene expression data (with annotated rows) independent of the dataset used to form the centroids. For gene clusters, the rows are samples and the columns are genes. For sample clusters, the rows are genes and the columns are samples. |
Number.of.permutations |
The number of times the centroids will be permuted to generate the null distribution. |
This function looks for gene expression clusters found in one dataset in another independent dataset. The centroids from the first dataset are used to classify the independent data and the corresponding in-group proportions (IGPs) are computed. These in-group proportions are compared to null distributions of in-group proportions to produce p-values. The IGP null distributions are generated by repeatedly permuting the centroids within the box aligned with the principal components, classifying the independent data, and calculating the corresponding IGPs.
Actual.Size |
The number of columns of New.data assigned to each centroid. |
Actual.IGP |
The in-group proportions of the groups formed when New.data is classified using Centroids. |
p.value |
The p-values for each of the groups represented by the centroids. |
Number |
The number of permutations used to compute the corresponding p-value. |
Amy Kapp and Robert Tibshirani
Amy Kapp and Robert Tibshirani. Are clusters in one dataset present in another dataset? To be published.
### Generate centroids with annotated rows Centroids <- matrix(rnorm(30, sd = 10), 10) rownames(Centroids) <- letters[1:nrow(Centroids)] ### Generate data with annotated rows Data <- cbind(matrix(rep(Centroids[,1], 10), 10), matrix(rep(Centroids[,2], 15), 10), matrix(rep(Centroids[,3], 20), 10)) Data <- Data + matrix(rnorm(length(Data), sd = 10), nrow(Data)) rownames(Data) <- letters[1:nrow(Data)] ### Classify the data and calculate the corresponding in-group ### proportions and group size Result <- clusterRepro(Centroids, Data, Number.of.permutations = 1) Result$Actual.IGP Result$Actual.Size ### Generate null distributions and compare to actual in-group proportions to obtain p-values Result2 <- clusterRepro(Centroids, Data, Number.of.permutations = 1000) ### If the number of rows in the centroid matrix does not match the ### number of rows in the data matrix and the row labels are unique, this ### function will only use the rows that the two matrices have in common. Data <- matrix(rnorm(200), 20) rownames(Data) <- letters[(nrow(Data)+6):7] Result <- IGP.clusterRepro(Data, Centroids) Result2 <- clusterRepro(Centroids, Data, Number.of.permutations = 1000)### Generate centroids with annotated rows Centroids <- matrix(rnorm(30, sd = 10), 10) rownames(Centroids) <- letters[1:nrow(Centroids)] ### Generate data with annotated rows Data <- cbind(matrix(rep(Centroids[,1], 10), 10), matrix(rep(Centroids[,2], 15), 10), matrix(rep(Centroids[,3], 20), 10)) Data <- Data + matrix(rnorm(length(Data), sd = 10), nrow(Data)) rownames(Data) <- letters[1:nrow(Data)] ### Classify the data and calculate the corresponding in-group ### proportions and group size Result <- clusterRepro(Centroids, Data, Number.of.permutations = 1) Result$Actual.IGP Result$Actual.Size ### Generate null distributions and compare to actual in-group proportions to obtain p-values Result2 <- clusterRepro(Centroids, Data, Number.of.permutations = 1000) ### If the number of rows in the centroid matrix does not match the ### number of rows in the data matrix and the row labels are unique, this ### function will only use the rows that the two matrices have in common. Data <- matrix(rnorm(200), 20) rownames(Data) <- letters[(nrow(Data)+6):7] Result <- IGP.clusterRepro(Data, Centroids) Result2 <- clusterRepro(Centroids, Data, Number.of.permutations = 1000)
This function classifies gene expression (microarray) data using centroids and calculates the in-group proportions of the resulting groups.
IGP.clusterRepro(Data, Centroids)IGP.clusterRepro(Data, Centroids)
Data |
The matrix of gene expression data with annotated rows. For gene clusters, the labeled rows are samples and the columns are genes. For sample clusters, the labeled rows are genes and the columns are samples. |
Centroids |
The matrix of centroids with annotated rows. The labeled rows are either genes (for gene clusters) or samples (for sample clusters) and the columns are the centroids. |
The Pearson's centered correlation coefficient between each datum and each centroid is calculated. The datum is then classified to the group whose centroid had the highest correlation with the datum. The in-group proportion is defined to be the proportion of data in a group whose nearest neighbors (Pearson's centered correlation) are also classified to the same group.
Class |
The data classification made using the centroids |
IGP |
The in-group proportions for the groups found in the data |
Size |
The number of data classified to each of the groups |
Amy Kapp and Robert Tibshirani
Amy Kapp and Robert Tibshirani. Are clusters in one dataset present in another dataset? To be published.
### Make centroid matrix with annotated rows C <- matrix(rnorm(30), 10) rownames(C) <- letters[1:nrow(C)] ### Make data matrix with annotated rows D <- matrix(rnorm(100), 10) rownames(D) <- letters[1:nrow(C)] ### Classify data and calculate in-group proportions Result <- IGP.clusterRepro(D, C) ### If the number of rows in the centroid matrix does not match the ### number of rows in the data matrix and the row labels are unique, this ### function will only use the rows that the two matrices have in common. D <- matrix(rnorm(200), 20) rownames(D) <- letters[(nrow(D)+6):7] Result <- IGP.clusterRepro(D, C)### Make centroid matrix with annotated rows C <- matrix(rnorm(30), 10) rownames(C) <- letters[1:nrow(C)] ### Make data matrix with annotated rows D <- matrix(rnorm(100), 10) rownames(D) <- letters[1:nrow(C)] ### Classify data and calculate in-group proportions Result <- IGP.clusterRepro(D, C) ### If the number of rows in the centroid matrix does not match the ### number of rows in the data matrix and the row labels are unique, this ### function will only use the rows that the two matrices have in common. D <- matrix(rnorm(200), 20) rownames(D) <- letters[(nrow(D)+6):7] Result <- IGP.clusterRepro(D, C)