Hello,

I am still using qbase (hoping to upgrade one of these days)  I didn't think I
could use the free version of biogazelle since I have 14 runs I wish to evaluate
together.

I am having trouble however with my dataset.  If you are still willing to have a
look I would appreciate your help.

I have two normalizers and 5 target genes.  The number of samples I have
required that I run two plates per gene.  I included a control sample and a
standard curve.  All samples are in triplicate.  I entered the data into qbase
and was able to get what looked like a correct sample identification, plate
setup and identification of the interrun calibrators ie the control and the
standard curve samples for each run.  However, the program reported that it
could not rescale, error said that no interrun calibration with NRQ data found. 
Runs could not be scaled or calibrated.  Couldn't group the interrun
calibrators.  It appeared to me that the program was not sure how to use the
interun calibrators associated with each run between the plates showing the same
genes.

I entered each qPCR plate as a run in the experiment.  So each gene was in two
runs of the experiment.  I tried creating the entry with different structure
such that there were two runs for each experiment and an experiment represented
a gene but found that there was not crosstalk at the project level such that
could get normalization and output of all information.

Thanks for any help.
Doris

and the differences between qBase and qBasePlus are summarized on
http://www.biogazelle.com/products/qbaseplus/faq

There are 3 kind of licenses available for qBasePlus: a payable full license with support and upgrade protection, a limited free license, and a fully functional demo license (valid for 2 weeksof evaluation). Current qBase users enjoy a promotional offer of 20% discount on the qBasePlus price (offer valid till March 24, 2008, enter promotion code: QBP1ACTG).

As of today, download of the old qBase software is terminated, and the qBase discussion forum will be closed for further message posting (while the posted messages will remain accessible).

Thank you for your support and feedback the past 3 years. We are confident that you will enjoy qBasePlus as well!

Jan Hellemans and Jo Vandesompele
Ghent University Hospital, Belgium
qbase@...

Hello Marcelo,

qBase does not do any statistical analysis on biological replicates.
The program treats samples with the same name as technical replicates.
  Samples with different names are considered different samples,
without recognizing biological replicates.  Further statistical
analysis can be performed on the exported normalized relative quantities.

Jan

--- In qBase@yahoogroups.com, "Marcelo Laia" <marcelolaia@...> wrote:
>
> Hi,
>
> We did a analysis on qBASE and we found a problem in set up the proper
> biological replication.
>
> We have four biological replicates for each condition:
>
> condition 1 --> 24_1, 24_2, 24_3 and 24_4
> condition 2 --> 3dai_1, 3dai_2, 3dai_3 and 3dai_4
>
> After the analysis we choose the endogenous control genes and do the
> final step in qBASE.
>
> When we go to Results, Multi Gene view, the graphics shows all samples
> (24_1, 24_2, 24_3, 24_4, 3dai_1, 3dai_2, 3dai_3 and 3dai_4) instead of
> the two conditions.
>
> For jump this problem we renamed the samples of condition 1 to 24h and
> the samples for condition 2 to 3dai. Is this correct?
>
> After that change all expression to 24h are setted to 1 and the
> expressions for 3dai are variable. Is this correct?
>
> How I could test if the difference between conditions are statistical
> significantly? How test you advice me for to do this analysis? qBASE
> do this test?
>
> Thank you very much
>
> Marcelo
>

Hi,

We did a analysis on qBASE and we found a problem in set up the proper
biological replication.

We have four biological replicates for each condition:

condition 1 --> 24_1, 24_2, 24_3 and 24_4
condition 2 --> 3dai_1, 3dai_2, 3dai_3 and 3dai_4

After the analysis we choose the endogenous control genes and do the
final step in qBASE.

When we go to Results, Multi Gene view, the graphics shows all samples
(24_1, 24_2, 24_3, 24_4, 3dai_1, 3dai_2, 3dai_3 and 3dai_4) instead of
the two conditions.

For jump this problem we renamed the samples of condition 1 to 24h and
the samples for condition 2 to 3dai. Is this correct?

After that change all expression to 24h are setted to 1 and the
expressions for 3dai are variable. Is this correct?

How I could test if the difference between conditions are statistical
significantly? How test you advice me for to do this analysis? qBASE
do this test?

Thank you very much

Marcelo

Hello,

I am trying to find a way to compare the relative expression values of
multiple genes. I gather from the qBase manual that it can be possible
to view the expression histograms of many genes at once, but only for
an overall detection of correlation or anti-correlation across the
sample; not for comparing the expression value of several genes in a
single sample.

My understanding from the relative expression calculations is that we
cannot compare the expression value of two genes on the same sample
because the calibrating quantification cycle used is the average Ct
across all sample for a gene. Thus two genes won't have the same
calibrating Ct, and then won't be quantitatively comparable on the
same sample afterwards.

In the qBase paper (Hellemans et al., Genome Biol. 2007, 8:R19), it is
pointed out that any calibrating Ct can be used, but for the sake of
error minimisation the average Ct is used. So I am wondering if I set
a arbitrary calibrating Ct constant in all my genes of interest (say
25), could I do a quantitative comparison of the relative expression
value thus obtained for my genes on the same sample?

thanks for any insight or comment about this!

Phil

Hello,

I am trying to find a way to compare the relative expression values of
multiple genes. I gather from the qBase manual that it can be possible
to view the expression histograms of many genes at once, but only for
an overall detection of correlation or anti-correlation across the
sample; not for comparing the expression value of several genes in a
single sample.

My understanding from the relative expression calculations is that we
cannot compare the expression value of two genes on the same sample
because the calibrating quantification cycle used is the average Ct
across all sample for a gene. Thus two genes won't have the same
calibrating Ct, and then won't be quantitatively comparable on the
same sample afterwards.

In the qBase paper (Hellemans et al., Genome Biol. 2007, 8:R19), it is
pointed out that any calibrating Ct can be used, but for the sake of
error minimisation the average Ct is used. So I am wondering if I set
a arbitrary calibrating Ct constant in all my genes of interest (say
25), could I do a quantitative comparison of the relative expression
value thus obtained for my genes on the same sample?

thanks for any insight or comment about this!

Phil

I have 2 questions regarding elimination of samples:

1. We included 9 endogenous control genes in our qPCR setup (TaqMan
low density arrays, 32 genes run i triplicate) used for expression
profiling during a differentiation study (15 time points). In one of
the samples only one replicate was obtained for a particular control
gene. Subsequently we eliminated that control gene before the final
evaluation (in geNorm) of which genes to use for normalization. Since
all further enterpretation of data rely on the choise of control genes
we guess that  this would be the most correct way to do it? The mean
M-value in geNorm for the 3 genes we ended up with was 0.2355 and the
paiwise variation below 0.10 - which is excellent.

2. After normalization of the data, again at one time point we had a
target gene with only one replicate (of three) coming up - however
since  the expression level of the target gene can be seen in relation
to time points before and after and fits well in curve over time, we
guess it is ok to include (mentioning of course that the value
respresents only one replicate) - or? Otherwise we normally say that 2
of 3 replicates should be present with a difference in CT below 0.5
(CT´s below 32 - for CT´s above a difference of 1.0).

We have had MANY discussions in our lab how to deal with these things
and would deeply appreciate input from other users.

qBase is not compatible with Excel 2007.  We advise Microsoft Office 2003.
Biogazelle will release qBasePlus in February 2008.  This program is
based on the qBase technology and will run independently from any
Office version or even operating system.

Jan

--- In qBase@yahoogroups.com, Torstein Lindstad
<torstein.lindstad@...> wrote:
>
>
> Hi
>
> Is it possible to use Excel 2007 with qBase. I have problems using the
> analyzer.
>
> Torstein
>

Hi

Is it possible to use Excel 2007 with qBase. I have problems using the
analyzer.

Torstein

I'm using qbase with Excel xp (2002) on a windows machine.

I've initialized my data and performed raw data quality control.  When
I calculate relative quantities, however, I receive the following
error: run type error '13': mismatch error

Oddly if I unselect my two reference genes I do not receive this error.

Help!

thanks,
jackie

Dear all,

We start a experiment and we planning to analyse it in qBASE.

So, we starting reading the qBASE tutorial, 1 Initial experiment.

But, we are with some doubts.

Our system is an Applied 7500, and we are using SyBrGreen and we will use 2 REF to compare relative expresion in 37 GOI in 8 samples from two groups (4 treated and 4 untreated). I would like to see relative expression between treated and untreated (we looking for the treatment effect on those genes).
[37 GOI + 2 REF] * [8 UNKN + 0 STD + 1 NTC] * 2 R = 702 wells

1. We choose Absolute or Relative in instrument's set up?
2a. If we choose Absolute quantification, we set the REF genes as UNKN or STD or NTC in instrument set up?
2b. If we choose Relative quantification, we found ENDO and Target on Task, but not NTC!
3. I will need to run ~8 plates (702/96). So, I will need to run the two REF genes in all plates? In this case, I will need to run 3 GOI and the 2 REF per plate. Or may be run the REF in the fist plate and in the next we can run 5 GOI per plate?
4. We don't planing to use standard. This is Ok or not?

Please, could you help me on how we can set up our experiment for forwarding use qBASE properly?

Thank you very much!!!

--
Marcelo Luiz de Laia
Jaboticabal - SP - Brazil

"Você vê as coisas como elas são e pergunta: por quê? Mas eu sonho com coisas que nunca foram e pergunto: por que não? " - Bernard Shaw

Does everyone use qbase with a mastercycler eppendorf?

Hi,
I have 2 questions for problems with qbase !
1. Is qbase compatible with windows vista ? I have installed qbase on
my laptop with windows vista but when I try to open qbase :
An error message appear with : 'erreur d'exécution - fichier
introuvable ...'  or 'execution error - unfinding file '
What can I do ?
2. I want to analyze TLDA 384 miRNA from Applied Biosystems well plate
and according to the manual, I have made an excel file with the column
headings - Well, Sample, Detector, Task and Ct !The initilization is
completed and the raw data quality control but how can I reach the
result for each miRNA ? All results of expression is 1 !!

Your program is very easy to use and very rapid ! thanks a lot,
Violaine

Hi,

I have an experimental setup where I had to make three cDNA plates
to fit all my samples in (duplicate), all have a 5-point dilution
curve (triplicate) of the same sample.
When I try to analyze my data in qBase I run into problems, which
seem to me to be linked to the fact that there are three standard
curves for each gene, so the program will not calculate the
amplification efficiency for each gene.

(I have calculated the amlpification efficiency for each plate in
the LightCycler 480 software when I ran each of the plates, and it
is not necessarily the same for the same gene in separate runs, so
it would make more sense to include one amplification efficiency per

plate rather than one per gene.)

Has anyone had similar problems or have any ideas as to how I can
fix or get around this?

I will be very happy for any help/response!

Hi,

I have an experimental setup where I had to make three cDNA plates
to fit all my samples in (duplicate), all have a 5-point dilution
curve (triplicate) of the same sample.
When I try to analyze my data in qBase I run into problems, which
seem to me to be linked to the fact that there are three standard
curves for each gene, so the program will not calculate the
amplification efficiency for each gene.

(I have calculated the amlpification efficiency for each plate in
the LightCycler 480 software when I ran each of the plates, and it
is not necessarily the same for the same gene in separate runs, so
it would make more sense to include one amplification efficiency per
plate rather than one per gene.)

Has anyone had similar problems or have any ideas as to how I can
fix or get around this?

I will be very happy for any help/response!

Hello,

Firstly, thank you for the opportunity to use qBase. I have just
started using it and I am having a problem when I get to the data
analysis step. When I try to normalize relative quantities it gets to
the last part (evaluation of reference gene quality) and gives me a
Run-time error 1004 - "unable to get the average property of the
worksheet function class". It will not let me select the Debug button,
so I don't know what I am doing wrong!
I have tried using excel 2002, 2003 and 2007 and I get the same error
each time. I have checked my input files and none of the cells are
empty, apart from the NTC Ct cells. I have been using the Corbett
format described in the manual. Is there anything else I can do?

Thank you.

Comments on question 1

Although you may have validated your reference genes before it is
still advisible to verify the stability of these reference genes in
the actual experiment (= quality control).
It is normal that the NF vary from sample to sample.  This is the
reason why you need to perform normalization.  A 60-fold difference is
quite large.  Have you normalized your RNA before proceeding to the RT
step?

Comments on question 2

You can get a quick indication of the reliability of your assay by
looking at the reproducability.  Your technical replicates should be
very similar (delta Ct < 0.5 cycle).  A very thorough check to
evaluate wether your assay still performs as it should, you can create
a dilution series and see wether you still obtain a (log-) linear
relationship between your Ct values and the input quantities at these
higher cycle numbers.

--- In qBase@yahoogroups.com, "isabelle.schrauwen"
<isabelle.schrauwen@...> wrote:
>
> Hi,
>
> I have 2 questions:
>
> 1. I have samples with different quality I want to compare. I used 2
> ref genes that are normally very stable in this tissue (we tested this
> before), after calculation of the NF, it ranged from 0.1 to 6 between
> the samples, illustrating clearly that there is a difference in
> quality and/or quantity. Will it matter to use more reference genes
> and is there even a reliable method to compare samples with a
> different quality and/or quantity? These are the only samples I have
> and it is difficult to collect new ones.
>
> 2. I have a gene I want to investigate that has a very low expression
> in the tissue I have RNA from. In all samples the Cp value is around
> 34-38 (in non-diluted cDNA)? Is it still reliable to test expression-
> differences? What is the maximum Cp value for a reliable analysis.
>
> Kind regards
>

The NF and M are both related to the reference genes, but in a
completely different way.  The M value must be interpreted as a
quality parameter for the stability of the selected reference genes.
The NF (normalization factor) values are a measure to correct for the
differences in the amount of total input DNA between samples.  Your
results indicate that the amount of DNA in your two groups differ by
about a factor 10.  Although normalization is performed to correct for
this difference, it is still advisible to minimize these differences
in future experiments.

Jan

--- In qBase@yahoogroups.com, "o_o_phil" <o_o_phil@...> wrote:
>
> Hello,
>
> I am concerned with difference among NFs for our experimental setup of
> two groups of tissue pools. The NFs are near 0.3 in one group, and 2.5
> in the other. In this setup we used 7 references genes, and the
> overall M is 0.650.
>
> Using geNorm we selected the two most stable genes among the 7,
> obtaining a M of 0.169 with the two. However, the NF histogram looks
> about the same as with the 7 ref genes: near 0.3 for one tissue group
> and near 2.5 for the other group. What's the difference between M and
> NF histogram when assessing ref genes stability?
>
> How can we interpret the final CNRQ histogram for our genes of
> interest based on those results about the reference genes? Is this
> strong difference among NFs disrupt the normalization, or on the other
> hand removes the expression bias detected among the reference genes?
> Can we make fold difference assumption in our genes of interest
> knowing this difference between NFs?
>
> thanks in advance for any insight you can give me!
>
> Phil
>

Hello,

I am concerned with difference among NFs for our experimental setup of
two groups of tissue pools. The NFs are near 0.3 in one group, and 2.5
in the other. In this setup we used 7 references genes, and the
overall M is 0.650.

Using geNorm we selected the two most stable genes among the 7,
obtaining a M of 0.169 with the two. However, the NF histogram looks
about the same as with the 7 ref genes: near 0.3 for one tissue group
and near 2.5 for the other group. What's the difference between M and
NF histogram when assessing ref genes stability?

How can we interpret the final CNRQ histogram for our genes of
interest based on those results about the reference genes? Is this
strong difference among NFs disrupt the normalization, or on the other
hand removes the expression bias detected among the reference genes?
Can we make fold difference assumption in our genes of interest
knowing this difference between NFs?

thanks in advance for any insight you can give me!

Phil

hi everyone,

i m not jst a new user of qbase, and also new in the lab. i did my
quantification pcr on the bio-rad icycler and annlysed the data by
the qBase. now, i meet a problem---how can i do my statistical
analysis using the analysed data ?  my experiment is to examine 7
kinds gene expression after treatment and compare the expression
level with the control group( untreated cells). I detected 7 kinds
gene expression level at 5 different timepoints after treatment and
did a duplicate for every sample. according to the qbase output, i
can see the expression level change but i would know further ---- is
this level change  statistical significant or not ?? BTW, i also used
two house keeping genes to normalize the RNA amout, but NF outcome is
not ideal ranging from 0.05 to 30,and both of them having a M value
0.96. i searched some relative messages in our group, it seems in my
actual case, i have to add another housekeeping gene in to get a
around 1 NF. dose my saying make any sense?  i used the same amount
total RNA doing the RT, and i checked the RNA quality by running a
gel indicating clear nice bands of 28s and 18s.

i m sorry if my Q sounds stupid but i just cant make it by my own.
any help would be appreciated!

thnx

I was wondering if there are any users that are converting there data
to either log base 2 or log base 10 and how they are handling to
conversions of the error measurments.

Thanks
Matt T

Answers inserted into the text.

--- In qBase@yahoogroups.com, Adrian Marius Peres Bota
<aperesbota@...> wrote:


Dear Jo

Would you please have a look to the questions bellow that rose during
an internal meeting?

1)       What is/are the advantage(s) to use the mean Ct for
calculating the relative quantities (qBase) instead of the min Ct
(GeNorm)?

==> As explained in the qBase paper, the choice of the reference Ct
(mean or min or ...) does not affect your results.  When taking into
account the error on the amplification efficiency the overall error
can be minimized by using the mean Ct as the reference Ct.

2)        Knowing such different approach (qBase vs. GeNorm), why is
it necessary to estimate firstly the ref. genes in GeNorm and then use
them in qBase if we are not applying the same method for calculating
the relative quantities.

==> geNorm allows you to select the best set of reference genes in a
pilot experiment.  By calculating the same quality parameter (geNorm M
value), qBase allows you to verify wether your reference genes are
still stable in your actual experiment.  Therefore geNorm and qBase
can be considered complimentary: they perform validation and quality
control respectively.

3)       Would it be possible to implement the GeNorm approach in
qBase allowing then comparisons?

==> The development of qBase has been terminated.  Biogazelle will
however release a professional version (qBasePlus) which will
integrate geNorm in the future.

Looking forward to your answer.

Best regards,
Adrian

Damon,

Inter-run calibration needs to be performed for every single gene
since run-to-run variation can be gene (mastermix, ...) dependent.

--- In qBase@yahoogroups.com, "damon.tumes" <damon.tumes@...> wrote:
>
> Hi, I have one more question about the inter run calibration performed
> by qbase. If I decied to only include IRC's for one or two control
> genes in my analysis wil qbase use these genes to perform inter run
> calibration on all samples (i.e. all genes in an experiment that are
> present on different runs with just the control IRC's) or will inter
> run calibration only be performed on those genes that have IRC's
> present?
>
> Best Regards,
>
> Damon
>

Hi,

I have started using QBase after the presentations I saw last week
and the conference in Rhode Island.

I use the 7900HT platform with either 384 well plates or the TLDA
card.

I am analyzing an 8 card TLDA experiment. 96 genes and 8 samples.
QBase has been handling the data very well, except when I make multi
gene histograms.

Is there a way to copy and paste those histograms with the background
data to another excel page. I am usually marking between 6-18 genes
at a time but when I try to copy and paste or even paste special the
whole thing crashes.

This may be due to the size of my data set, and I have the exported
calculations for all that I can use but it would be easier if I could
export just those that are selected.

Any advice would be appreciated.

Thanks
Matt T.

Hi,

I have started using QBase after the presentations I saw last week
and the conference in Rhode Island.

I use the 7900HT platform with either 384 well plates or the TLDA
card.

I am analyzing an 8 card TLDA experiment.  96 genes and 8 samples.
QBase has been handling the data very well, except when I make multi
gene histograms.

Is there a way to copy and paste those histograms with the background
data to another excel page.  I am usually marking between 6-18 genes
at a time but when I try to copy and paste or even paste special the
whole thing crashes.

This may be due to the size of my data set, and I have the exported
calculations for all that I can use but it would be easier if I could
export just those that are selected.

Any advice would be appreciated.

Thanks
Matt T.

Hi Alessandra and all.

I got this same problem: "Run-time error '1004': application-defined
or object-defined error" in Excel 2000 SP3.

Does upgrade to 2003 solved that problem?

Another question: there is a qBase version for Linux (Debian :) )? Or
a package for GNU R?

Thank you very much

Marcelo

--- In qBase@yahoogroups.com, Hayley McGrice <hayley.mcgrice@...> wrote:
>
> Hi Alessandra
>
> I experienced the same program when trying to run qBase with excel
> 2000.  Upgrading to excel 2003 fixed this problem straight away.
>
> Hope this helps
> Hayley
>

> > Hi
> >
> > I just downloaded qBase earlier this week, but I
> > am having trouble getting the program started. The
> > Excel program freezes everytime I try to analyze
> > an experiment, so I haven't been able to complete
> > the tutorial. I am also unablea to import my runs,
> > because I get the following message: "Run-time error
> > '1004': application-defined or object-defined error"
> >
> >Has anyone encountered this problem before? What can
> > I do to solve it?
> >
> > Thanks,
> >
> > Alessandra Splendore

Hi,

I have 2 questions:

1. I have samples with different quality I want to compare. I used 2
ref genes that are normally very stable in this tissue (we tested this
before), after calculation of the NF, it ranged from 0.1 to 6 between
the samples, illustrating clearly that there is a difference in
quality and/or quantity. Will it matter to use more reference genes
and is there even a reliable method to compare samples with a
different quality and/or quantity? These are the only samples I have
and it is difficult to collect new ones.

2. I have a gene I want to investigate that has a very low expression
in the tissue I have RNA from. In all samples the Cp value is around
34-38 (in non-diluted cDNA)? Is it still reliable to test expression-
differences? What is the maximum Cp value for a reliable analysis.

Kind regards