Entering edit mode
Bass, Kevin
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10
@bass-kevin-4176
Last seen 10.3 years ago
Hi,
I am having a problem with using the function limmaCtData on a qPCRset
object created with the package HTqPCR. When I try to execute
limmaCtData, I get the following error:
"Error in chol.default(V) :
the leading minor of order 2 is not positive definite"
Below, I will describe the experimental design and the steps taken to
create my qPCRset object. Then I will paste the commands used, and
their results, in the steps leading up to running the limmaCtData
function on my qPCRset object.
We have 21 96-well plates. Each plate contains 5 experimental groups
and 4 genes--2 target genes, and 2 endogenous controls. Each
experimental group sampled all 4 genes, and there were 3 biological
replicates per sample, for a total of 12 wells per experimental group.
Every 7 plates among the total 21 plates constitutes a "set" of
plates: they each contain the same 14 target genes. This means that
each gene, in each experimental condition, has 3 samples among the 21
plates--one sample per experimental condition for each 7-plate set.
The goal is to compare the Ct values for each gene in each
experimental group, to the Ct values for the same gene in every other
experimental group.
Using rbind (HTqPCR), I collated 7 of the data files into one file,
so that all 14 genes could be analyzed simultaneously, at least among
a single set of plates--once I had figured that part out, I had
planned on combining the 3 sets.
To give a clear idea what my data looks like--and how it was
implemented in my qPCRset object--this is the Slot "history" and Slot
"exprs" of my combined qPCRset object (with the data removed):
Slot "exprs":
01_veh+FA 02_low+FA 03_mid+FA 04_high+FA
05_no_treatment
PGES
PGES
PGES
c-Fos
c-Fos
c-Fos
SPP1
SPP1
SPP1
CD200
CD200
CD200
COX-1
COX-1
COX-1
COX-2
COX-2
COX-2
OX-42
OX-42
OX-42
iBA-1
iBA-1
iBA-1
IL-2
IL-2
IL-2
IL-4
IL-4
IL-4
IL-6
IL-6
IL-6
IL-8
IL-8
IL-8
IL-10
IL-10
IL-10
CD4
CD4
CD4
Slot "history":
history
1 raw8: readCtData(files = "NS398_08b.txt", path = barrPath,
n.features = 12,
2 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
3 header = TRUE, n.data = 5)
4 raw9: readCtData(files = "NS398_09b.txt", path = barrPath,
n.features = 12,
5 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
6 header = TRUE, n.data = 5)
7 raw10: readCtData(files = "NS398_10b.txt", path = barrPath,
n.features = 12,
8 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
9 header = TRUE, n.data = 5)
10 raw11: readCtData(files = "NS398_11b.txt", path = barrPath,
n.features = 12,
11 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
12 header = TRUE, n.data = 5)
13 raw12: readCtData(files = "NS398_12b.txt", path = barrPath,
n.features = 12,
14 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
15 header = TRUE, n.data = 5)
16 raw13: readCtData(files = "NS398_13b.txt", path = barrPath,
n.features = 12,
17 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
18 header = TRUE, n.data = 5)
19 raw14: readCtData(files = "NS398_14b.txt", path = barrPath,
n.features = 12,
20 flag = NULL, feature = 5, type = 7, position = 2, Ct = 6,
21 header = TRUE, n.data = 5)
22 rbind(deparse.level, ..1, ..2, ..3, ..4, ..5, ..6, ..7)
23 normalizeCtData(q = raw_monster2, norm = "deltaCt",
deltaCt.genes = "GAPDH")
24 filterCtDataNew(q = d.raw2, remove.type = "Endogenous
Control")
25 setCategory(q = fd.raw2, Ct.max = 100, Ct.min = 0,
quantile = 0.9,
So, then I prepared the matrix for analysis with limma:
> design<-model.matrix(~0+sampleNames(test.d.raw2))
Warning message:
In model.matrix.default(~0 + sampleNames(test.d.raw2)) :
variable 'sampleNames(test.d.raw2)' converted to a factor
> colnames(design)<-c("VehFA","LowFA","MidFA","HighFA","NoTreat")
> print(design)
VehFA LowFA MidFA HighFA NoTreat
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
5 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`sampleNames(test.d.raw2)`
[1] "contr.treatment"
> contrasts<-makeContrasts(VehFA-LowFA, VehFA-MidFA, VehFA-HighFA,
+ VehFA-NoTreat, LowFA-MidFA, LowFA-HighFA, LowFA-NoTreat,
+ MidFA-HighFA, MidFA-NoTreat,HighFA-NoTreat, levels=design)
> colnames(contrasts)<-c("V-L", "V-M", "V-H", "V-NT", "L-M", "L-H",
+ "L-NT", "M-H", "M-NT", "H-NT")
> print(contrasts)
Contrasts
Levels V-L V-M V-H V-NT L-M L-H L-NT M-H M-NT H-NT
VehFA 1 1 1 1 0 0 0 0 0 0
LowFA -1 0 0 0 1 1 1 0 0 0
MidFA 0 -1 0 0 -1 0 0 1 1 0
HighFA 0 0 -1 0 0 -1 0 -1 0 1
NoTreat 0 0 0 -1 0 0 -1 0 -1 -1
> test.d.raw2b<-test.d.raw2[order(featureNames(test.d.raw2)), ]
======================================================================
=
> qDE.limma <- limmaCtData(test.d.raw2b,design=design,
+ contrasts=contrasts,ndups=3,spacing=1)
Error in chol.default(V) :
the leading minor of order 2 is not positive definite
In addition: Warning message:
In sqrt(dfitted.values) : NaNs produced
> traceback()
6: .Call("La_chol", as.matrix(x), PACKAGE = "base")
5: chol.default(V)
4: chol(V)
3: gls.series(y$exprs, design = design, ndups = ndups,
spacing = spacing, block = block, correlation = correlation,
weights = weights, ...)
2: lmFit(data, design = design, ndups = ndups, spacing = spacing,
correlation = dup.cor$consensus, ...)
1: limmaCtData(test.d.raw2b, design = design, contrasts = contrasts,
ndups = 3, spacing = 1)
Any ideas on why I am getting this error and what I might do to avoid
it? If there is any other information needed, please let me know.
Thanks,
Kevin
bassk1 at email.chop.edu
=====
Kevin Bass, Research Technician
Barr Lab
Children's Hospital of Philadelphia
Abramson Research Center
3615 Civic Center Blvd, Suite 714
Philadelphia PA 19104-4399
