ExpExpIntegration1.1
Integrates gene/transcript/other expression
data with binary explanatory data derived in some way. The input matrices
are two N x M matrices that must have their columns (samples) and rows in the same order.
Each row shall in the exprMatrix has the gene, transcript or other such expression
values samplewise. The labelMatrix has must have a vector 0 and 1 on each row. This
represents a partition of the samples rowwise.
The output of the component contains a score ("weight") for each gene shared by the
explanatory and expression data.
The alpha value is the result of permutation test denoting the probability that
H0: "Large weight is due to random event" is erroneously rejected. The weight is
calculated for each row such that that the vector of the labelMatrix is used to
partition the values of exprMatrix into two groups. Then, the mean of the expression
values of the group labeled with 1 is subtracted from the mean of the other group. This
is divided by the sum of the standard deviations of these two groups to obtain final
weight.
Both input matrices can contain missing (NA) values. Values in the label matrix that
are not 0, 1 or NA are effectively ignored in the weight calculation. Rows with NA
values in the label matrix are also ignored i.e., no weight is calculated for these
genes.
Riku LouhimoIntegrationAsser.jar The binary explanatory values. The samplewise expression values. Defines the percentage of missing measurements in the expression matrix we are willing to allow. Defines the number of permutations applied during permutation testing. Defines the idColumn. The output idColumn is the column name of idColumn in the label matrix. Defines the which type of CNV data is being used. Defines the minimum size of a group in the label matrix.