Hello Rakesh,
> mam,
>> qDE.ttest <- ttestCtData(sr.norm[, 1:5], groups =
files$Treatment[1:5],
> calibrator = "Control")
>
> it works well but it give ddct without entering referance gene name
>
I'm not sure I understand your question. There isn't a one single
reference gene across the entire array. For each gene, the result you
get
is the difference between that gene in your treatment versus control,
taking into account that each gene is apparently present twice on each
array.
\Heidi
> my parent file look like this
>
> File Treatment
> 1 control.txt Control
> 2 30min.txt short-revival
> 3 2hr.txt short-revival
> 4 4hr.txt short-revival
> 5 8hr.txt short-revival
> 6 12hr.txt long-revival
> 7 16hr.txt long-revival
> 8 24hr.txt long-revival
> 9 48hr.txt long-revival
>
> the output is
>> qDE.ttest
> genes feature.pos t.test p.value adj.p.value
ddCt
> FC meanCalibrator meanTarget 8 HSP90 B1;B2 14.1918260
> 1.951842e-06 2.537394e-05 -1.3640160 2.5740070 22.545
21.18098
>
> 11 NFK? B9;B10 -11.7361946 7.215020e-06 4.689763e-05
1.5287282
> 0.3465828 23.785 25.31373
> 5 GPX1 C1;C2 -8.9176667 4.528284e-05 1.962256e-04
0.3621420
> 0.7780086 20.030 20.39214
>
> 6 HSP40 B3;B4 6.9024184 1.360182e-04 4.420590e-04
-3.3166287
> 9.9633346 22.745 19.42837
>
> 4 EEF1A1 A3;A4 -3.4429311 1.062206e-02 2.761736e-02
0.2375887
> 0.8481617 23.685 23.92259
>
> 7 HSP70 A11;A12 2.5713740 4.065094e-02 8.807704e-02
-0.6349518
> 1.5528858 17.015 16.38005
> 2 BCl2 A7;A8 -2.3134370 5.388469e-02 8.925181e-02
0.5828524
> 0.6676425 28.975 29.55785
>
> 9 HSPB1 A9;A10 2.2462289 5.492419e-02 8.925181e-02
-0.2327041
> 1.1750353 19.715 19.48230
>
> 12 RL4 A1;A2 -1.9695521 8.565504e-02 1.237239e-01
0.1956580
> 0.8731745 22.085 22.28066
>
> 3 DUSP1 B11;B12 1.9649979 1.128910e-01 1.467583e-01
-0.2893234
> 1.2220670 25.225 24.93568
> 13 TNF? B7;B8 1.1632727 3.219916e-01 3.805355e-01
-1.1370484
> 2.1993061 34.585 33.44795
>
> 10 IL6 B5;B6 0.4165346 6.907912e-01 7.483571e-01
-0.1916190
> 1.1420446 33.990 33.79838
>
> 1 Bax A5;A6 -0.4004043 7.496591e-01 7.496591e-01
0.2457399
> 0.8433831 37.810 38.05574
>
>
>
>
>
>
>
>
>
>
>
>
>
> Bax and EEF1A1 are my referance gene.
> then how possible ddct for these two gene,the output is giving ddct
for
> these too.
>
> and i want to make two group one is short-revival and second is
> long-revival then which script i use
>
> qDE.ttest <- ttestCtData(sr.norm[, 1:5], groups =
files$Treatment[1:5],
> calibrator = "Control")
> or
> qDE.ttest <- ttestCtData(sr.norm[, 1:9], groups =
files$Treatment[1:9],
> calibrator = "Control")
>
> plz help me out mam
>
> Thanking You
>
> On Wed, Aug 15, 2012 at 2:56 PM, rakesh sharma
> <rakeshsaraswat691 at="" gmail.com="">wrote:
>
>> thank you heidi i got it.
>> thankyou very much
>>
>>
>> On Tue, Aug 14, 2012 at 11:53 AM, Heidi Dvinge <heidi at="" ebi.ac.uk="">
wrote:
>>
>>> Hello Rakesh,
>>>
>>> >
>>> > sir,
>>> > i am new on R language and dealing with light-cycler qpcr data
>>> using
>>> > HtqPCR package.everything goes all right till fold change.
>>> >
>>> > when i use t-test then following error occured.
>>> >
>>> >> qDE.ttest <- ttestCtData(sr.norm[, 1:2], groups =
>>> files$Treatment[1:2],
>>> > calibrator = "Control")
>>> > Error in t.test.default(x[, g1], x[, g2], alternative =
alternative,
>>> > paired
>>> > = paired, :
>>> > data are essentially constant
>>> >
>>> This error isn't actually from HTqPCR, but from the underlying
t.test
>>> function. Have you checked whether these samples are indeed
different?
>>> E.g. plot(getCt(sr.norm[, 1:2])). Does the sample apply to all
your
>>> samples, e.g. qDE.ttest <- ttestCtData(sr.norm[, c(1,3)], groups =
>>> files$Treatment[c(1,3)], calibrator = "Control")
>>>
>>> It sounds like you may have a case where for example all
replicates of
>>> a
>>> given gene in both your two samples has the Ct value 40, i.e. is
>>> undetected. Have you replaced such values with NA during your
workflow?
>>>
>>> You can check the variation in your samples using e.g.
plotCtVariation,
>>> or
>>> plotCtCor to look at the correlation between samples. In general,
the
>>> HTqPCR plotting functions are quite useful for figuring our how
your
>>> data
>>> 'behaves'. The vignette gives examples of how to use most of these
>>> plots,
>>> and they're all listed in figure 1.
>>>
>>> > when i select 4 samples then
>>> >
>>> > qDE.ttest <- ttestCtData(sr.norm[, 1:4], groups =
>>> files$Treatment[1:4],
>>> > calibrator = "Control")
>>> > Error in ttestCtData(sr.norm[, 1:4], groups =
files$Treatment[1:4],
>>> > calibrator = "Control") :
>>> > Two factor levels required for 'groups'
>>> >
>>>
>>> In this case you input 4 different groups (Control, 30min, 2hr,
4hr)
>>> into
>>> a 2-sided t-test. This never going to work; exactly 2 different
groups
>>> are
>>> required. You will need to go back and figure out why the 2-sample
>>> approach you tried above doesn't work, i.e. where/why some of your
>>> samples
>>> are too similar to each other.
>>>
>>> HTH
>>> \Heidi
>>> >
>>> > my parent file look like this
>>> >
>>> > File Treatment
>>> > control.txt Control
>>> > 30min.txt 30min
>>> > 2hr.txt 2hr
>>> > 4hr.txt 4hr
>>> > 8hr.txt 8hr
>>> > 12hr.txt 12hr
>>> > 16hr.txt 16hr
>>> > 24hr.txt 24hr
>>> > 48hr.txt 48hr
>>> >
>>> > every sample have 26 features(13 replicate)
>>> >
>>> > so how i perform t- test on these data.
>>> > please sir help me out.
>>> >
>>> >
>>> > thanking you
>>> >
>>> > [[alternative HTML version deleted]]
>>> >
>>> > _______________________________________________
>>> > Bioconductor mailing list
>>> > Bioconductor at r-project.org
>>> >
https://stat.ethz.ch/mailman/listinfo/bioconductor
>>> > Search the archives:
>>> >
http://news.gmane.org/gmane.science.biology.informatics.conductor
>>> >
>>>
>>>
>>>
>>
>>
>> --
>> *Yours Sincerely
>> **Rakesh Sharma
>> C/o- Dr M Mukesh
>> National Fellow
>> DNAFU,
>> NBAGR (ICAR) - 132001
>> Haryana
>> INDIA*
>>
>
>
>
> --
> *Yours Sincerely
> **Rakesh Sharma
> C/o- Dr M Mukesh
> National Fellow
> DNAFU,
> NBAGR (ICAR) - 132001
> Haryana
> INDIA*
>
