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
164
165
166
167
168
169
170
171
172
173
174
|
library(edgeR)
options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
set.seed(0); u <- runif(100)
# generate raw counts from NB, create list object
y <- matrix(rnbinom(80,size=5,mu=10),nrow=20)
y <- rbind(0,c(0,0,2,2),y)
rownames(y) <- paste("Tag",1:nrow(y),sep=".")
d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=1001:1004)
filterByExpr(d)
# estimate common dispersion and find differences in expression
d <- estimateCommonDisp(d)
d$common.dispersion
de <- exactTest(d)
summary(de$table)
topTags(de)
d2 <- estimateTagwiseDisp(d,trend="none",prior.df=20)
summary(d2$tagwise.dispersion)
de <- exactTest(d2,dispersion="common")
topTags(de)
de <- exactTest(d2)
topTags(de)
d2 <- estimateTagwiseDisp(d,trend="movingave",span=0.4,prior.df=20)
summary(d2$tagwise.dispersion)
de <- exactTest(d2)
topTags(de)
summary(exactTest(d2,rejection.region="smallp")$table$PValue)
summary(exactTest(d2,rejection.region="deviance")$table$PValue)
d2 <- estimateTagwiseDisp(d,trend="loess",span=0.8,prior.df=20)
summary(d2$tagwise.dispersion)
de <- exactTest(d2)
topTags(de)
d2 <- estimateTagwiseDisp(d,trend="tricube",span=0.8,prior.df=20)
summary(d2$tagwise.dispersion)
de <- exactTest(d2)
topTags(de)
# mglmOneWay
design <- model.matrix(~group,data=d$samples)
mglmOneWay(d[1:10,],design,dispersion=0.2)
mglmOneWay(d[1:10,],design,dispersion=0)
fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5/4)
lrt <- glmLRT(fit,coef=2)
topTags(lrt)
fit <- glmFit(d,design,dispersion=d$common.dispersion,prior.count=0.5)
summary(fit$coefficients)
fit <- glmFit(d,design,prior.count=0.5/4)
lrt <- glmLRT(fit,coef=2)
topTags(lrt)
dglm <- estimateGLMCommonDisp(d,design)
dglm$common.dispersion
dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
summary(dglm$tagwise.dispersion)
fit <- glmFit(dglm,design,prior.count=0.5/4)
lrt <- glmLRT(fit,coef=2)
topTags(lrt)
dglm <- estimateGLMTrendedDisp(dglm,design)
summary(dglm$trended.dispersion)
dglm <- estimateGLMTrendedDisp(dglm,design,method="power")
summary(dglm$trended.dispersion)
dglm <- estimateGLMTrendedDisp(dglm,design,method="spline")
summary(dglm$trended.dispersion)
dglm <- estimateGLMTrendedDisp(dglm,design,method="bin.spline")
summary(dglm$trended.dispersion)
dglm <- estimateGLMTagwiseDisp(dglm,design,prior.df=20)
summary(dglm$tagwise.dispersion)
dglm2 <- estimateDisp(dglm, design)
summary(dglm2$tagwise.dispersion)
dglm2 <- estimateDisp(dglm, design, prior.df=20)
summary(dglm2$tagwise.dispersion)
dglm2 <- estimateDisp(dglm, design, robust=TRUE)
summary(dglm2$tagwise.dispersion)
# Continuous trend
nlibs <- 3
ntags <- 1000
dispersion.true <- 0.1
# Make first transcript respond to covariate x
x <- 0:2
design <- model.matrix(~x)
beta.true <- cbind(Beta1=2,Beta2=c(2,rep(0,ntags-1)))
mu.true <- 2^(beta.true %*% t(design))
# Generate count data
y <- rnbinom(ntags*nlibs,mu=mu.true,size=1/dispersion.true)
y <- matrix(y,ntags,nlibs)
colnames(y) <- c("x0","x1","x2")
rownames(y) <- paste("Gene",1:ntags,sep="")
d <- DGEList(y)
d <- calcNormFactors(d,method="TMM")
fit <- glmFit(d, design, dispersion=dispersion.true, prior.count=0.5/3)
results <- glmLRT(fit, coef=2)
topTags(results)
d1 <- estimateGLMCommonDisp(d, design, verbose=TRUE)
glmFit(d,design,dispersion=dispersion.true, prior.count=0.5/3)
calcNormFactors(d$counts,method="TMMwsp")
d2 <- estimateDisp(d, design)
summary(d2$tagwise.dispersion)
d2 <- estimateDisp(d, design, prior.df=20)
summary(d2$tagwise.dispersion)
d2 <- estimateDisp(d, design, robust=TRUE)
summary(d2$tagwise.dispersion)
# Exact tests
y <- matrix(rnbinom(20,mu=10,size=3/2),5,4)
group <- factor(c(1,1,2,2))
ys <- splitIntoGroupsPseudo(y,group,pair=c(1,2))
exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
y <- matrix(rnbinom(5*7,mu=10,size=3/2),5,7)
group <- factor(c(1,1,2,2,3,3,3))
ys <- splitIntoGroupsPseudo(y,group,pair=c(1,3))
exactTestDoubleTail(ys$y1,ys$y2,dispersion=2/3)
exactTestBetaApprox(ys$y1,ys$y2,dispersion=2/3)
y[1,3:4] <- 0
design <- model.matrix(~group)
fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
summary(fit$coefficients)
lrt <- glmLRT(fit,contrast=cbind(c(0,1,0),c(0,0,1)))
topTags(lrt)
design <- model.matrix(~0+group)
fit <- glmFit(y,design,dispersion=2/3,prior.count=0.5/7)
lrt <- glmLRT(fit,contrast=cbind(c(-1,1,0),c(0,-1,1),c(-1,0,1)))
topTags(lrt)
# simple Good-Turing algorithm runs.
test1 <- 1:9
freq1 <- c(2018046, 449721, 188933, 105668, 68379, 48190, 35709, 37710, 22280)
goodTuring(rep(test1, freq1))
test2 <- c(312, 14491, 16401, 65124, 129797, 323321, 366051, 368599, 405261, 604962)
goodTuring(test2)
# Dispersion estimation with fitted values equal to zero
ngenes <- 100
nsamples <- 3
y <- matrix(rnbinom(ngenes*nsamples,size=5,mu=10),ngenes,nsamples)
Group <- factor(c(1,2,2))
design <- model.matrix(~Group)
y[1:5,2:3] <- 0
y <- DGEList(counts=y,group=Group)
y <- estimateCommonDisp(y)
y$common.dispersion
y <- estimateGLMCommonDisp(y,design)
y$common.dispersion
y <- estimateGLMTrendedDisp(y,design)
y$trended.dispersion[1:10]
summary(y$trended.dispersion)
y <- estimateGLMTagwiseDisp(y,design)
y$tagwise.dispersion[1:10]
summary(y$tagwise.dispersion)
y <- estimateDisp(y,design)
y$prior.df
y$common.dispersion
y$trended.dispersion[1:10]
summary(y$trended.dispersion)
y$tagwise.dispersion[1:10]
summary(y$tagwise.dispersion)
|
