GeneNetwork and WebQTL:
Part
1: How to study expression variation and covariation (slides 2–16)
Part
2. Discovering upstream modulators (slides 17–30)
RNA
PowerPoint ̉Normal viewÓ has notes that may be useful companions to these slides.
a
PowerPoint Presentation
RWW
07.23.2005
You can also download this PowerPoint at
ftp://atlas.utmem.edu/public/webqtl_demo2.ppt
| Welcome to a short
demonstration of the GeneNetwork and its WebQTL module. Please adjust the
size of the windows on your monitor so that you can see part of this page, as
well as GeneNetwork windows. WebQTL produces a large number of new windows,
so you may need to modify your browser preferences to permit pop-ups. In this
demonstration, we explore one important transcript expressed in the brain:
the amyloid beta precursor protein messenger RNA. A protein product of this
mRNA, the APP protein, is associated with AlzheimerƠs disease. |
|
| My thanks to Dr. Robert
F. Clark and Wenli Cai for testing this PowerPoint demonstration and making
many improvements. |
|
| (Initial version of June
2003 by Rob Williams. Edits July 13, 2005 by RW and RFC. Edit July 14, 2005
by WC. Final edits by RF Clark, July 22, 2005. |
Choose
database
Enter
APP
Select
search
PART 1: How to study variation and
covariation
Choose species, group, and type
| Please link to the web
site: http://www.genenetwork.org |
|
| To begin a search you
make choices about what species, group, and database to explore. |
|
| For this demonstration
enter APP as above and click on the SEARCH button. Make sure that the DEFAULT
SETTINGS are species = Mouse, Group = BXD, Type = Whole Brain, and Database =
INIA BRAIN mRNA M430 (Apr05) PDNN. |
|
| Notes: |
|
| 1. The GeneNetwork and
WebQTL are often used to work with public data sets. However, it is possible
to enter and analyze your own data for specific genetic reference populations
such as the BXD genetic reference population of mice or the HXB strains of rat.
Entering your own data is a more advanced topic, but if you click on the HOME
pop-down menu (upper left), you will see ̉Enter Trait DataÓ that will explain
the process. |
|
| 2. For help on advanced searching methods read the left
side of the page (INTRODUCTION).
If you make a search term too complex, you may get no hits (try
entering ̉amyloid betaÓ for example). If you make it too simple, you may also
get too many. |
|
| 3. Use the asterisk * as a wildcard. For example, to find
all Hoxb transcripts, search for Hoxb*. |
|
| 4. In some cases you can also research for transcripts
and genes using special search strings such as ̉Mb = (Chr1, 100 102)Ó to find all genes on Chromosome
1 between 98 and 104 megabases (donƠt actually use the quotes). Details are
described at http://www.genenetwork.org/searchHelp.html. |
|
| 5. These INFO buttons provide
links to data about the different data types. Try them. |
|
| 6. The SET TO DEFAULT button is used to
change the database default setting to match your typical search categories. |
Please also use the Glossary, FAQ, and
News
| SHORT DETOUR to the HELP
menu. If you are new to the GeneNetwork, you may find it helpful to review
the The Glossary and FAQ pages shown above. We are in the process of making
̉liveÓ demos for some of the key modules in the GeneNetwork. Check the NEWS
every month or two to find out about new features. |
|
Highlight this probe set in red and
click. You do NOT have to select the checkbox
Search results
| RESULTS OF THE APP
SEARCH. A search of the INIA
Brain database generates 18 matches, 10 of which are shown above. The
GeneNetwork will display several hundred matches in pages of 40 each. If a
search generates a larger numbers of hits, then you will need to refine
search terms. |
|
| Notes: |
|
| 1. APP is a great
transcript to introduce you to the complexity and power of new array
platforms that often provide ̉alternativeÓ expression estimates for single
genes. There are seven probe sets that target different parts of the APP
transcript. Which of the alternative measurements is most appropriate and
informative? Have a look at the FAQ page for more on this topic, but general
advice: 1. be skeptical and try to validate that the correct transcript and
gene is being measured; 2. check what part of the transcript is complementary
to the probes; 3. evaluate the performance of individual probes based on
expression level, signal-to-noise and other error terms such as the standard
deviation and error. |
|
| 2. In this particular
case we have highlighted and selected # 5 on this SEARCH RESULTS page. The
annotation for this probe set mentions that it targets the last three exons
and the 3Ơ untranslated region (UTR) of the amyloid precursor protein (APP). That is just what we want. |
|
| 3. Most probe sets have
not been annotated in as much detail as App. Refer tot the FAQ to learn how
to annotate probe sets yourself. |
First page of data: The
Trait Data and Analysis Form
Click here
to learn
about
data
source
| The Trait Data and
Analysis Form is the single most important page from the point of view of
working with GeneNetwork data. Please read the text carefully. Explore the
links, but do not close this page. We will need it many more times in this
demonstration. |
|
| Notes: |
|
| 1. What is this
database? It is called INIA Brain mRNA M430 (Apr05) PDNN, but what does that
actually mean. How much of the brain was used? How were the animals
processed? Most of these types of questions can be answered by clicking on
the DATABASE link. |
|
| 2. Transcript/gene
LOCATION data is usually from the most recent assembly. You can VERIFY the
location of the probes and probe set using the two VERIFY buttons. VERIFY
UCSC performs a sequence alignment (BLAT analysis) of the probes to the most
recent assembly. |
|
| 3. The PROBE TOOL button
provides you with highly detailed information on the probe sequences used to
assemble the probe set. For example, in this case you can find out which
probes correspond to which of the three exons. You can also review the
performance of the individual probes. Please check the GLOSSARY for
additional details on probes. |
|
| 4. The identifiers (IDs)
provide links to other key resources. |
|
Data sources: Metadata
for each resource
| Most of the database
components and resources in The GeneNetwork are linked to metadata pages that
provide a human-readable summary of how, why, where, when, and with whom the
data were generated. Before you get too involved with a data set, it is
naturally important to read this information. While the data in The
GeneNetwork may be accessible and useful, that does not always mean that the
data is public domain and available for you to use in publication or for
profit purposes. If you want to know more about the data ownership and usage,
please read through the POLICIES pop-down menu items. |
Expression estimates for App on the Trait Data form
Trait data for each strain with SE when known. For array data the scale is ~ log base 2. F1 data = 16.723 =
2^16.723 = 108,174
These values can all be changed by the user. (Yes, there is a RESET)
| This slide shows you the
lower parts of the Trait Data and Analysis Form with the data for the first
set of BXD strains |
Critiquing the App data the Trait Data
Use the BASIC STATISTICS button to evaluate the App data. You will find that
App data from the different strains are not equally trustworthy. BXD8
is an obvious outlier without replication (no error bar). BXD33 is also
suspiciously low. BXD5 is noisy.
| The Basic Statistics
page allows you to evaluate some aspects of the data quality. In this case,
BXD8 is a potential problem. An outlier of this type may be generated by a
technical artifact (bad sample?). However, it is also possible that BXD8 just
has genuine low endogenous expression of App and may therefore be a
particularly valuable model for research. There are different ways to treat
problematic data of these types. One way is simply to discard this datum. The
other way is to prevent outliers from have too much influence quantitatively,
while leaving them in their low (or high positions). This is called
windsorizing the data (after King Henry the VIII who had a habit of chopping
heads). In this case, we have windsorized the BXD8 to a value of 16.0 and the
BXD33 to a value of 16.02. Rank is retained. We are making a bet that the two
lowest strains are really low, but we are hedging our bet and just making
them a little lower than BXD90. This removes their ̉undueÓ influence. |
|
| Notes: |
|
| 1. It turns out that
BXD8 is a strain with many odd phenotypes. The whole strain is essentially an
outlier for many traits. Therefore, the low App expression data may be quite
accurate. Still, it would be comforting to have at least two more replicates. |
App expression after
windsorizing
| Now we get a much better
feel for the variation in the error among the cases. Those without error bars
are of course the ̉noisiestÓ of all. This data set is not complete yet (the
aim is to acquire at least one male-female sample for each BXD strain). |
Discovering shared
expression patterns
| Finally, we can now
start an analysis. |
|
| We ask a simple
question: |
|
| Do differences in this
particular App transcript steady-state abundance level correlate with those
of any other transcripts in the same INIA Brain mRNA M430 data set? |
|
| Notes: |
|
| 1.You can CHOOSE many other DATABASES at this point if
you want, but for now letƠs stick with the default. |
|
| 2. There are different
ways to sort the correlations. The most obvious is by p-value (most
significant values at the top of the list), but it is also interesting to
sort the top 100 or top 500 by their gene symbol (gene ID) or by their
chromosomal location (position). |
|
| 3. If you donƠt want
your analysis to be sensitive to outliers, then you may want to choose to use
the Spearman Rank Order method of calculating correlations. |
Transcript neighborhoods
| The Traits Correlation
output window (Correlation Results) compares App expression data with all
other traits in this INIA Brain data set. The most significant 100 or 500
transcripts are sorted by their p-values. The top correlation is that of the
probe set to itself (often a value of 1.0, but in this case we modified the
App values manually by windsorizing the data). The next best correlation is
to another App probe set. The fourth correlation is interesting and suggests
that there may be a link between App and a particular type of ataxia (Atcay). |
|
| Notes: |
|
| 1.Use the checkboxes to the far left to select traits
that you want to study together. Once you have selected interesting traits,
click on the ADD SELECTION button. This puts all of the selected traits into
a SELECTIONS WINDOW for other types of analysis. |
|
| 2. The p-value is not
corrected for multiple tests. A conservative approach for array data would be
to assume 10,000 nominally independent tests. Subtract 4 from the exponent
and if the value is still smaller than 0.05 you may have a real correlation. |
|
| 3. The LITERATURE
CORRELATION is a data type generated by Drs. Ramin Homayouni and Michael
Berry. Click on the header column by the asterisk for more information on
this highly useful data type. |
|
| 4. We are using Pearson
product moment correlations rather that the Spearman rank order correlation.
But you can select either in the previous step. |
App and Atcay transcript scatterplot
| By clicking on the
CORRELATION of the Atcay transcript to the App transcript, you can generate a
Correlation plot between these two transcripts. In this App and Atcay
scatterplot, each point is a strain mean value. For example, BXD33 and BXD8
have low App and Atcay expressions. The two parental strains and the F1 are
also included in this plot. |
App transcript and eight of
its neighbors
| A group of traits from
many different databases can be selected and brought together for joint
analysis. In this case all of the content of the BXD SELECTIONS is from a
single BRAIN database, the top 20 neighbors of the App transcript from the
Correlation Results table. Eight of these neighbors plus App is shown in the
slide. |
|
| Notes: |
|
| 1.All of items in the BXD SELECTIONS were selected using
the SELECT ALL button |
|
| 2. The buttons at the
top (and bottom) of this page can do some cool stuff. We will work with
NETWORK GRAPH first. |
|
| 3. Think of the
SELECTIONS as your shopping cart. You go to different aisles in the
supermarket to acquire different types of items of interest. These could
include transcripts, classical phenotypes (longevity, brain weight, prepulse
inhibition, iron levels in midbrain). ̉Checking outÓ in this case involves
doing some analysis with the items in the cart. |
|
| 4. Different tools
handle different numbers of items. Most will handle up to 100 traits. |
|
App transcript coexpression neighborhood
| Output of the Network
Graph. Warm colors (orange and red) are positive correlations above 0.5
whereas cool colors (green and blue) are negative correlations. Notes: |
|
| 1. All of the nodes (gene/transcripts) on this graph are
clickable. |
|
| 2. For this graph the
App expression values have ̉revertedÓ to their pre-Windsorized values. |
|
| 3. To generate this
graph, we used the default setting:
Size of 16 by 16 inches; Gene Symbols; Don't Show Correlations; Use
curved lines (aka ̉edgesÓ). |
Correlations of App with classical traits
| Going back to the Trait
Data and Analysis Form window, we have computed the correlations between
strain variation in App expression level and other classical phenotypes that
have already been measured in many of the same BXD strains. |
|
| Notes: |
|
| 1.The number of common strains varies widely--in this
case from 14 to 23 strains. |
|
| 2. We can add these
traits (four are selected) to our BXD SELECTIONS window. |
Network Graph of App with classical traits
| We have computed the
Network Graph, now using other types of traits. |
|
| Saline Hot Plate Latency
is the green node labeled 10020. |
|
| Freezing (fear) is the
green node labeled 10447. |
|
| Notes: |
Summary of Part 1:
1. You have learned the basics
about searching for traits
2. You know some methods to check
data quality
3. You know how to edit bad or
suspicious data
4. You know how to review the
basic statistics of a trait
5. You know how to generate a
scattergram between two traits using the Traits
Correlation tool
6. You know how to add items to
your SELECTIONS window
7. You know how to generate a
Network Graph of traits that co-vary.
What does genetic covariance mean? The genetic covariance can be functional and
mechanistic, but it can also be due to linkage disequilibrium. Finally, it can be due to sampling error or poor experimental design. Evaluate the biological plausibility of correlations. Test and be skeptical.
Contact
for comments and improvements:
rwilliam@nb.utmem.edu
kmanly@utmem.edu
The App findings reviewed
in this presentation are part of an ongoing study by R. Williams. R. Homayouni, and
R. Clark (July 15, 2005)
| END |
GeneNetwork and WebQTL:
Part 1: How to study expression variation and covariation (slides 2–16)
Part 2. Discovering
upstream modulators (slides 17–30)
RNA
PowerPoint ̉Normal viewÓ has notes that may
be useful companions to these slides.
a PowerPoint Presentation
RWW 07.23.2005
You can also download this PowerPoint at
ftp://atlas.utmem.edu/public/webqtl_demo2.ppt
| END |
Choose
database
Enter
APP
Select
search
lPART 1: How to study
variation and covariation
Choose
species, group, and type
lPlease also use the
Glossary, FAQ, and News
Highlight this probe set in red and
click. You do NOT have to select the checkbox
Search results
First page of data: The
Trait Data and Analysis Form
Click here
to learn
about
data
source
Data sources: Metadata
for each resource
Expression estimates for App on the Trait Data form
Trait data for each strain with SE when known.
For array data the scale is ~ log base 2. F1 data = 16.723 = 2^16.723 = 108,174
These values can all be changed by the user. (Yes, there is a RESET)
Critiquing the App data the Trait Data
Use the BASIC STATISTICS button to evaluate
the App data. You will find that App data from the different strains are not equally
trustworthy. BXD8 is an obvious outlier without replication (no error bar). BXD33 is also
suspiciously low. BXD5 is noisy.
App expression after
windsorizing
Discovering shared
expression patterns
Transcript neighborhoods
App and Atcay transcript scatterplot
App transcript and eight of
its neighbors
App transcript coexpression neighborhood
lCorrelations of App with classical traits
lNetwork Graph of App with classical traits
lSummary of Part 1:
1. You have learned the basics
about searching for traits
2. You know some methods to check
data quality
3. You know how to edit bad or
suspicious data
4. You know how to review the
basic statistics of a trait
5. You know how to generate a
scattergram between two traits using the Traits Correlation tool
6. You know how to add items to
your SELECTIONS window
7. You know how to generate a
Network Graph of traits that co-vary.
What does genetic covariance mean? The genetic
covariance can be functional and mechanistic, but it can also
be due to linkage disequilibrium. Finally, it can be due to
sampling error or poor experimental design. Evaluate the biological
plausibility of correlations. Test and be skeptical.
Contact for comments and improvements:
rwilliam@nb.utmem.edu
kmanly@utmem.edu
The App findings reviewed in this presentation are part
of an ongoing study by R. Williams. R. Homayouni, and R. Clark (July
15, 2005)
