VIP: Vector in Partition
In RHclust: Vector in Partition
VIP R Documentation
Vector in Partition
Description
Clustering of subjects based on similar patterns of gene expression, DNA methylation, and SNPs.
Usage
VIP(Simulated = NULL, SNP = NULL, CPG = NULL, GE = NULL,
SNPname = NULL, CPGname = NULL, GEname = NULL, v,
optimize = c('off','min','slope','elbow'),
iter_max = 1000, nstart = 5, fit = c('aic','bic'),
seed = NULL, type = c('Default','NoCPG','NoSNP'),
ct = c('mean','median'), verbose = FALSE)
Arguments
Simulated
set to name of simulated data built from SimData(), else set to NULL for real data.
SNP
Data frame or data matrix containing categorical SNP data. Input must be in form of N x M, with N rows of subjects and M columns of SNPs. Rownames are permitted. Run SimData()$SNP for examples.
CPG
Data frame or data matrix containing numeric CPG data. Input must be in form of N x M, with N rows of subjects and M columns of CPG. Rownames are permitted. Run SimData()$CPG for examples.
GE
Data frame or data matrix containing numeric GE data. Input must be in form of N x M, with N rows of subjects and M columns of GE. Rownames are permitted. Run SimData()$GE for examples.
SNPname
Names for SNP data. Data must be a data frame of Nx2 dimensions with SNP sites as column 1, and GE indexes in column 2. Order of SNPs must match the order of the SNP columns in the argument SNP. See SimData()$SNP_Index for examples.
CPGname
Names for CPG data. Data must be a data frame of Nx2 dimensions with CPG sites as column 1, and GE indexes in column 2. Order of CPGs must match the order of the CPG columns in the argument GE. See SimData()$CPG_Index for examples.
GEname
Names for GE data. Data must be a data frame of Nx2 dimensions with GE sites as column 1, and GE indexes in column 2. Order of GEs must match the order of the GE columns in the argument GE. See SimData()$GE_Index for examples.
v
Numeric scalar or vector of number for clusters, or a range of clusters with format c(l,u) for cluster l:u
optimize
Returned the optimal number of clusters. Input 'min' returns cluster assignment with lowest WSS for clusters in v. Input 'slope' indicates whether the algorithm should pick the lowest WSS value based on the first increasing slope. Input 'elbow' fits a line between the first and last fitted WSS and finds the corresponding cluster with the maximum distance to that line. All but 'slope' return plots.
iter_max
Maximum number of iterations allowed.
nstart
If nstart > 1, repetitive computations with random initializations are computed and the result with minimum tot_dist is returned.
fit
Penalizing factor for WSS of clusters. Can be set to either 'aic' or 'bic'.
seed
Optional input to sample the same initial cluster centers.
type
Optional input for special cases for data without CPGs or SNP inputs. Options include "Default", "NoSNP", or "NoCPG"
ct
Central tendency option for cluster assignment. Options include 'mean' or 'median'.
verbose
Logical whether information about the cluster procedure should be given.
Details
Similar to k-means and k-proto clustering, this algorithm computes clusters based on weighted factors of mixed data relative to genetic/epigenetic data. Clusters are assigned using sumed euclidean distance of numerics (GE and CPG) weighted by matching categorical (SNP) data. Central tendancy of numeric data can be set to either mean or median with input ct.
Data must be ordered such that rows in each data set correspond to the same subject and order of the indexes match the order of the columns in the data. The current algorithm does not allow for any missing data. The aim is for GE, CPG, and SNP data to be clustered into v groups such that within sum of squares is minimized. If groups of clusters are close, the algorithm may not converge correctly and signals a warning if cluster size is reduced.
Optimization functionality was used for simulated data analysis, but is allowed for user exploratory analysis as well. 'min' simply returns the lowest fitted WSS fit parameter. 'slope' loops through clusters in v and returns the cluster based on the first increasing slope of fitted WSS. For example, if AIC output is c(100,80,35,50), cluster 3 would be returned since the slope increases from 3 to 4. If there is no increasing slope, the 'min' optimizer will be returned. 'elbow' seeks to find the elbow of the plot based on saturation point. This worked the best for simulation studies but requires more clusters to make proper predictions, in our case it required a range of at least 5 clusters c(1,5) to search to correctly identify the 3 simulated clusters. . For ease of exploratory analysis, v=1 is allowed.
Value
size
Number of subjects assigned to each cluster.
cluster
Vector of cluster assignment.
GECenters
Matrix of cluster centers for GE.
CPGCenters
Matrix of cluster centers for CPG.
SNPCenters
Matrix of cluster centers for SNP.
within
Vector of within cluster sum of squares with one component per cluster.
tot_within
Sumed total of within-cluster sum of squares.
Moved
Number of iterations before convergence.
AIC
Value of tot_within with aic penalizer.
BIC
Value of tot_within with bic penalizer.
outputPlot
Returns the tot_within, aic, bic, and v values for ploting.
Author(s)
jkhndwrk@memphis.edu
References
Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. 10.2307/2346830.
Examples
## simple output of 3 clusters assignments
sd = SimData(1, g = 36, c(33,33,34))
VIPout = VIP(sd, v = 3)
# loop through clusters 1-10 and outputs plot of WSS, AIC, and BIC
VIPout = VIP(sd, v = c(1,10))
# loop through clusters 1-10 but picks first instance of increasing slope
VIPout = VIP(sd, v = c(1,10), optimize = 'slope')
# Individual inputs
sd = SimData(1, g = 36, k = c(33,33,34))
VIPout = VIP(SNP = sd$SNP, CPG = sd$CPG, GE = sd$GE,
SNPname = sd$SNP_Index, CPGname = sd$CPG_Index,
GEname = sd$GE_Index,
v = c(1,5), optimize = 'off', nstart = 5)
## Varying clusters
sd = SimData(k = c(10,40,50))
out = VIP(sd, v = c(1,6), optimize = 'elbow', nstart = 30)
RHclust documentation built on Aug. 15, 2023, 9:07 a.m.
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| VIP | R Documentation |
Vector in Partition
Description
Clustering of subjects based on similar patterns of gene expression, DNA methylation, and SNPs.
Usage
VIP(Simulated = NULL, SNP = NULL, CPG = NULL, GE = NULL,
SNPname = NULL, CPGname = NULL, GEname = NULL, v,
optimize = c('off','min','slope','elbow'),
iter_max = 1000, nstart = 5, fit = c('aic','bic'),
seed = NULL, type = c('Default','NoCPG','NoSNP'),
ct = c('mean','median'), verbose = FALSE)
Arguments
Simulated |
set to name of simulated data built from SimData(), else set to NULL for real data. |
SNP |
Data frame or data matrix containing categorical SNP data. Input must be in form of N x M, with N rows of subjects and M columns of SNPs. Rownames are permitted. Run SimData()$SNP for examples. |
CPG |
Data frame or data matrix containing numeric CPG data. Input must be in form of N x M, with N rows of subjects and M columns of CPG. Rownames are permitted. Run SimData()$CPG for examples. |
GE |
Data frame or data matrix containing numeric GE data. Input must be in form of N x M, with N rows of subjects and M columns of GE. Rownames are permitted. Run SimData()$GE for examples. |
SNPname |
Names for SNP data. Data must be a data frame of Nx2 dimensions with SNP sites as column 1, and GE indexes in column 2. Order of SNPs must match the order of the SNP columns in the argument SNP. See SimData()$SNP_Index for examples. |
CPGname |
Names for CPG data. Data must be a data frame of Nx2 dimensions with CPG sites as column 1, and GE indexes in column 2. Order of CPGs must match the order of the CPG columns in the argument GE. See SimData()$CPG_Index for examples. |
GEname |
Names for GE data. Data must be a data frame of Nx2 dimensions with GE sites as column 1, and GE indexes in column 2. Order of GEs must match the order of the GE columns in the argument GE. See SimData()$GE_Index for examples. |
v |
Numeric scalar or vector of number for clusters, or a range of clusters with format c(l,u) for cluster l:u |
optimize |
Returned the optimal number of clusters. Input 'min' returns cluster assignment with lowest WSS for clusters in v. Input 'slope' indicates whether the algorithm should pick the lowest WSS value based on the first increasing slope. Input 'elbow' fits a line between the first and last fitted WSS and finds the corresponding cluster with the maximum distance to that line. All but 'slope' return plots. |
iter_max |
Maximum number of iterations allowed. |
nstart |
If nstart > 1, repetitive computations with random initializations are computed and the result with minimum tot_dist is returned. |
fit |
Penalizing factor for WSS of clusters. Can be set to either 'aic' or 'bic'. |
seed |
Optional input to sample the same initial cluster centers. |
type |
Optional input for special cases for data without CPGs or SNP inputs. Options include "Default", "NoSNP", or "NoCPG" |
ct |
Central tendency option for cluster assignment. Options include 'mean' or 'median'. |
verbose |
Logical whether information about the cluster procedure should be given. |
Details
Similar to k-means and k-proto clustering, this algorithm computes clusters based on weighted factors of mixed data relative to genetic/epigenetic data. Clusters are assigned using sumed euclidean distance of numerics (GE and CPG) weighted by matching categorical (SNP) data. Central tendancy of numeric data can be set to either mean or median with input ct.
Data must be ordered such that rows in each data set correspond to the same subject and order of the indexes match the order of the columns in the data. The current algorithm does not allow for any missing data. The aim is for GE, CPG, and SNP data to be clustered into v groups such that within sum of squares is minimized. If groups of clusters are close, the algorithm may not converge correctly and signals a warning if cluster size is reduced.
Optimization functionality was used for simulated data analysis, but is allowed for user exploratory analysis as well. 'min' simply returns the lowest fitted WSS fit parameter. 'slope' loops through clusters in v and returns the cluster based on the first increasing slope of fitted WSS. For example, if AIC output is c(100,80,35,50), cluster 3 would be returned since the slope increases from 3 to 4. If there is no increasing slope, the 'min' optimizer will be returned. 'elbow' seeks to find the elbow of the plot based on saturation point. This worked the best for simulation studies but requires more clusters to make proper predictions, in our case it required a range of at least 5 clusters c(1,5) to search to correctly identify the 3 simulated clusters. . For ease of exploratory analysis, v=1 is allowed.
Value
size |
Number of subjects assigned to each cluster. |
cluster |
Vector of cluster assignment. |
GECenters |
Matrix of cluster centers for GE. |
CPGCenters |
Matrix of cluster centers for CPG. |
SNPCenters |
Matrix of cluster centers for SNP. |
within |
Vector of within cluster sum of squares with one component per cluster. |
tot_within |
Sumed total of within-cluster sum of squares. |
Moved |
Number of iterations before convergence. |
AIC |
Value of tot_within with aic penalizer. |
BIC |
Value of tot_within with bic penalizer. |
outputPlot |
Returns the tot_within, aic, bic, and v values for ploting. |
Author(s)
jkhndwrk@memphis.edu
References
Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. 10.2307/2346830.
Examples
## simple output of 3 clusters assignments
sd = SimData(1, g = 36, c(33,33,34))
VIPout = VIP(sd, v = 3)
# loop through clusters 1-10 and outputs plot of WSS, AIC, and BIC
VIPout = VIP(sd, v = c(1,10))
# loop through clusters 1-10 but picks first instance of increasing slope
VIPout = VIP(sd, v = c(1,10), optimize = 'slope')
# Individual inputs
sd = SimData(1, g = 36, k = c(33,33,34))
VIPout = VIP(SNP = sd$SNP, CPG = sd$CPG, GE = sd$GE,
SNPname = sd$SNP_Index, CPGname = sd$CPG_Index,
GEname = sd$GE_Index,
v = c(1,5), optimize = 'off', nstart = 5)
## Varying clusters
sd = SimData(k = c(10,40,50))
out = VIP(sd, v = c(1,6), optimize = 'elbow', nstart = 30)
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