difconet.run {difconet}

RUNS A DIFCONET ANALYSIS

Description

Estimates the DIFferential COrrelation NETworks analysis from a given dataset.

Usage

difconet.run(data, predictor, metric=c(1,2,3,4,5,6), cutoff=0.3, blocs=5000, 
  num_perms=10, comparisons="all", perm_mode="columns", use_all_perm = TRUE,
  save_perm=FALSE, speedup=0, verbose=TRUE, metricfunc=NULL, 
  corfunc=function(a,b) cor(a,b,method="spearman") )

Arguments

data

data.frame or matrix represent the dataset. Genes in rows, samples in columns.

predictor

Factor or numeric vector representing the classes of each column in data. The correlations will be estimated for each class separately.

metric

The metrics needed to be calculated. Valid values are 1 to 6 and 8. 1 to 6 are already implemented and shown in details. 8 specifies a user-defined metric specified in metricfunc.

cutoff

Cut off values used for metric 1 and/or 3.

blocs

Number of rows per block. Because of memory issues, the correlations are estimated by blocks of genes. This value represent the size of the block. Larger values requires more memory if needed. Lower values requiere more cycles and therefore it is slower but makes it computable depending on database size and memory.

num_perms

Number of permutations.

comparisons

Character or list. If character, it could be "all" to specify all possible combinations of classes. If set to "seq", classes are taken in order and comparisons are done by first versus second, second versus third, and so on. If this is a list containing vectors of two elements, the estimations are done for the specific comparisons included (numeric or character).

perm_mode

Character. It determines the how the permutated data is generated. It can be permutated by "columns", permutated by "rows" (all classes/stages), or permutated by rows within each class separately using "rows.class", or "all" in which all data is shuffled.

use_all_perm

Logical. If TRUE, it uses all permutated data to estimate the p-value, otherwise it uses only the same row permutations to estimate the p-value (it requires a lot more permutations).

save_perm

Logical. If TRUE, it save all permutated data. It may require more memory.

speedup

Numeric. Determines whether the calculation will be sped up. This is experimental. The value specify which metric will be used to speed up. This is done by modeling the dependency of the metric and p-value using 1 percent of the rows.

verbose

Logical. Determines if printing progress information.

metricfunc

Function. Specify the function to be used if a metric==8 is included. The function should receive dObj, a, and b which correspond to the difconet object and the a and b vectors of correlations needed to estimate the value of the metric. It is assumed a distance-like measure (non-negative) and values close to 0 means no difference whereas larger values represent more dissimilar correlations.

corfunc

Function. Specify the function that estimates the correlations, similar to the cor function. The default uses cor and spearman coefficients.

Details

Run the whole process of estimation differences in correlations for a given dataset. The estimations are done for all metric values, all cutoff values across all comparisons.

Value

A difconet object represented as a list. The items are the followings:

stage

Vector. A copy of predictor (classes).

labels

Vector. The levels or values of the different classes.

comparisons

The specified comparisons parameter.

num_perms

The specified number of permutations num_perms parameter.

perm_mode

The specified number of permutations perm_mode parameter.

use_all_perm

The specified number of permutations use_all_perm parameter.

speedup

The specified speedup parameter.

verbose

The specified verbose parameter.

metricfunc

The specified metricfunc parameter.

combinations

A data.frame of the combinations that were compared.

stages.data

A list of datasets. This is only the original data split by classes.

combstats

A list of all comparisons made. Each element contains a matrix whose rows represent the genes and columns represent the results of all metrics (metric.dist : metric value, metric.p : p-value, metric.q : q-value, metric.expr.p : p-value of differential expression for comparison purposes, metric.expr.q : q-value of differential expression.)

combdens

A list of the densities of the metric for observed data and permutations. This can be used to compare the estimated metric statistics.

permutations

List. If save_perm==TRUE, it saves all permutated data.

Author(s)

Elpidio Gonzalez and Victor Trevino [e-mail]

References

Gonzalez-Valbuena and Trevino 2017 Metrics to Estimate Differential Co-Expression Networks Journal Pending volume 00–10

See Also

difconet.build.controlled.dataset.

Examples

xdata <- matrix(rnorm(1000), ncol=100)
xpredictor <- sample(c("A","B","C","D"),100,replace=TRUE)
dObj <- difconet.run(xdata, xpredictor, metric = 4, num_perms = 10,              
  comparisons = list(c("A","D"), c("A","B"), c("B","D")),
  perm_mode = "columns")

## Not run: 
  #xpredictor contains A, B, C, and D.
  #xdata contains the data matrix
  dObj <- difconet.run(xdata, xpredictor,
  metric = c(1,2,4),
  cutoff = 0.6,
  blocs = 7000,
  num_perms = 10,              
  comparisons = list(c("A","D"), c("A","B"), c("B","D")),          
  perm_mode = "columns")

## End(Not run)