consensusFingerprint {pathprint} | R Documentation

## Construct a consensus fingerprint

### Description

Produces a pathway fingerprint that represents the consensus of a series of pathway fingerprints, according to a user-defined threshold

### Usage

    consensusFingerprint(fingerprintframe, threshold)

### Arguments

`fingerprintframe` | matrix of fingerprints from which the consensus will be calculated
`threshold` | threshold value (between 0 and 1)

### Details

For each pathway the mean fingerprint score, m, is calculated, and the consensus defined as
+1 if m > threshold
-1 if m < threshold
0 otherwise

### Value

Vector of consensus pathway fingerprint scores with names corresponding to pathways

### Author(s)

Gabriel Altschuler

### References

Altschuler, G. M., O. Hofmann, I. Kalatskaya, R. Payne, S. J. Ho Sui, U. Saxena, A. V. Krivtsov, S. A. Armstrong, T. Cai, L. Stein and W. A. Hide (2013). "Pathprinting: An integrative approach to understand the functional basis of disease." Genome Med 5(7): 68.

### See Also

`consensusDistance`

### Examples

    require(pathprintGEOData)
    library(SummarizedExperiment)

    # load  the data
    data(SummarizedExperimentGEO)

    ds = c("chipframe", "genesets","pathprint.Hs.gs",
        "platform.thresholds","pluripotents.frame")
    data(list = ds)

    # extract part of the GEO.fingerprint.matrix and GEO.metadata.matrix
    GEO.fingerprint.matrix = assays(geo_sum_data[,300000:350000])$fingerprint
    GEO.metadata.matrix = colData(geo_sum_data[,300000:350000])

    # free up space by removing the geo_sum_data object
    remove(geo_sum_data)

    # Extract common GSMs since we only loaded part of the geo_sum_data object
    common_GSMs <- intersect(pluripotents.frame$GSM,colnames(GEO.fingerprint.matrix))

    # search for pluripotent arrays
    # load fingerprint matrix and pluripotent reference

    # create consensus fingerprint
    pluripotent.consensus<-consensusFingerprint(
        GEO.fingerprint.matrix[,common_GSMs], threshold=0.9)

    # calculate distance from the pluripotent consensus
    geo.pluripotentDistance<-consensusDistance(pluripotent.consensus,
        GEO.fingerprint.matrix)

    # plot histograms
    par(mfcol = c(2,1), mar = c(0, 4, 4, 2))
    geo.pluripotentDistance.hist<-hist(geo.pluripotentDistance[,"distance"],
        nclass = 50, xlim = c(0,1), main = "Distance from pluripotent consensus")
    par(mar = c(7, 4, 4, 2))
    hist(geo.pluripotentDistance[pluripotents.frame$GSM, "distance"],
        breaks = geo.pluripotentDistance.hist$breaks, xlim = c(0,1),
        main = "", xlab = "above: all GEO, below: curated pluripotent samples")

[Package pathprint version 1.16.0 Index]