CAMPrep {CAMTHC} | R Documentation

## Data preprocessing for CAM

### Description

This function perform preprocessing for CAM, including norm-based filtering, dimension deduction, perspective projection, local outlier removal and aggregation of gene expression vectors by clustering.

### Usage

    CAMPrep(data, dim.rdc = 10, thres.low = 0.05, thres.high = 0.95,
      cluster.method = c("K-Means", "apcluster"), cluster.num = 50,
      MG.num.thres = 20, lof.thres = 0)

### Arguments

`data` | Matrix of mixture expression profiles. Data frame, SummarizedExperiment or ExpressionSet object will be internally coerced into a matrix. Each row is a gene and each column is a sample. Data should be in non-log linear space with non-negative numerical values (i.e. >= 0). Missing values are not supported. All-zero rows will be removed internally.
`dim.rdc` | Reduced data dimension; should be not less than maximum candidate K.
`thres.low` | The lower bound of percentage of genes to keep for CAM with ranked norm. The value should be between 0 and 1. The default is 0.05.
`thres.high` | The higher bound of percentage of genes to keep for CAM with ranked norm. The value should be between 0 and 1. The default is 0.95.
`cluster.method` | The method to do clustering. The default "K-Means" will use `kmeans` function. The alternative "apcluster" will use `apclusterK-methods`.
`cluster.num` | The number of clusters; should be much larger than K. The default is 50.
`MG.num.thres` | The clusters with the gene number smaller than MG.num.thres will be treated as outliers. The default is 20.
`lof.thres` | Remove local outlier using `lofactor` function. MG.num.thres is used as the number of neighbors in the calculation of the local outlier factors. The default value 0.02 will remove top 2% local outliers. Zero value will disable lof.

### Details

This function is used internally by `CAM` function to preprocess data, or used when you want to perform CAM step by step.

Low/high-expressed genes are filtered by their L2-norm ranks. Dimension reduction is slightly different from PCA. The first loading vector is forced to be c(1,1,...,1) with unit norm normalization. The remaining are eigenvectors from PCA in the space orthogonal to the first vector. Perspective projection is to project dimension-reduced gene expression vectors to the hyperplane orthogonal to c(1,0,...,0), i.e., the first axis in the new coordinate system. local outlier removal is optional to exclude outliers in simplex formed after perspective projection. Finally, gene expression vectors are aggregated by clustering to further reduce the impact of noise/outlier and help improve the efficiency of simplex corner detection.

### Value

An object of class "`CAMPrepObj`" containing the following components:

`Valid` | logical vector to indicate the genes left after filtering.
`Xprep` | Preprocessed data matrix.
`Xproj` | Preprocessed data matrix after perspective projection.
`W` | The matrix whose rows are loading vectors.
`cluster` | cluster results including two vectors. The first indicates the cluster to which each gene is allocated. The second is the number of genes in each cluster.
`c.outlier` | The clusters with the gene number smaller than MG.num.thres.
`centers` | The centers of candidate corner clusters (candidate clusters containing marker genes).

### Examples

    #obtain data
    data(ratMix3)
    data <- ratMix3$X

    #set seed to generate reproducible results
    set.seed(111)

    #preprocess data
    rPrep <- CAMPrep(data, dim.rdc = 3, thres.low = 0.30, thres.high = 0.95)

[Package CAMTHC version 1.0.0 Index]