zmatrix

Share:

Description

generate a matrix of c statistics

Usage

1
2
3
4
zmatrix(obj, y.vars, fold, trainingFun = masomenos, cvFun = funCV, 


    cvSubsetFun = cvSubsets)

Arguments

obj

a list of ExpressionSet, matrix or RangedSummarizedExperiment objects.

If its elements are matrices, columns represent samples

y.vars

a list of response variables, all the response variables shold be

matrix, data.frame(with 2 columns) or Surv object

fold

cvFun parameter, in this case passes to funCV()

trainingFun

training function

cvFun

function to perform cross study within one set

cvSubsetFun

function to divide the expression sets into subsets for cross validation

Value

outputs one matrix of validation statistics

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

Examples

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
library(curatedOvarianData)


library(GenomicRanges)


data(GSE17260_eset)


data(E.MTAB.386_eset)


data(GSE14764_eset)


esets <- list(GSE17260=GSE17260_eset, E.MTAB.386=E.MTAB.386_eset, GSE14764=GSE14764_eset)


esets.list <- lapply(esets, function(eset){


  return(eset[1:500, 1:20])


})





## simulate on multiple ExpressionSets


set.seed(8) 





y.list <- lapply(esets.list, function(eset){


  time <- eset$days_to_death


  cens.chr <- eset$vital_status


  cens <- c()


  for(i in seq_along(cens.chr)){


    if(cens.chr[i] == "living") cens[i] <- 1


    else cens[i] <- 0


  }


  y <- Surv(time, cens)


  return(y)


})





# generate on original ExpressionSets


z <- zmatrix(esets.list, y.list, 3)





# generate on simulated ExpressionSets


simmodels <- simBootstrap(esets.list, y.list, 10, 100)


z <- zmatrix(simmodels$obj.list, simmodels$y.vars.list, 3)





# support matrix


X.list <- lapply(esets.list, function(eset){


  newx <- exprs(eset) ### columns represent samples !!


  return(newx)


}) 


z <- zmatrix(X.list, y.list, 3)





# support RangedSummarizedExperiment


nrows <- 200; ncols <- 6


counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)


rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),


                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),


                     strand=sample(c("+", "-"), 200, TRUE))


colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),


                     row.names=LETTERS[1:6])


sset <- SummarizedExperiment(assays=SimpleList(counts=counts),


                             rowRanges=rowRanges, colData=colData)





time <- sample(4500:4700, 6, replace=TRUE)


cens <- sample(0:1, 6, replace=TRUE)


y.vars <- Surv(time, cens)





z <- zmatrix(list(sset[,1:3], sset[,4:6]), list(y.vars[1:3,],y.vars[4:6,]), 3)