Difference between revisions of "20.109(S09): Microarray data analysis (Day8)"

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(New page: {{Template: 20.109(S09)}} <font color - red>REVISE TO REFLECT NEW ASSESSMENT, ETC</font color> ==Introduction== Sony Playstation or Microsoft X-box? Boxers or briefs? Coke or Pepsi? We a...)
 
(→‎Protocols)
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#What do you see? Are the duplicates in agreement? Are there particular genes you expect to see up or down regulated in the two samples. Ask the questions you want about this data...
 
#What do you see? Are the duplicates in agreement? Are there particular genes you expect to see up or down regulated in the two samples. Ask the questions you want about this data...
 
#save as XLS worksheet or workbook  
 
#save as XLS worksheet or workbook  
 +
 +
===Data Analysis===
 +
 +
 +
===Data Interpretation===
 +
 +
Now that you’ve decided which genes you believe are differentially expressed between your two samples, you need some way of making sense of your data. In particular, you might like to see if some of the genes with apparent increased or decreased expression share common features.
 +
 +
As a first approach, you might manually list the ten genes most highly upregulated in your sample as compared to your control, then learn a little about each one using a gene ontology website. http://www.geneontology.org/  Do the ten genes come from a common family? What about the ten most downregulated genes?
 +
 +
Of course, you have data on tens of thousands of genes, and manual analysis is not the most practical way to mine your data. Instead, you can use freely available databases and programs to find groups of genes that are statistically over- or under-represented compared to their expected value. For example, say you are analyzing a pool of 15, 500 genes, and 3600 of them (23%) are up-regulated. If 1100 genes exist in a particular family, then you would expect 23% of them, or ~250 to be up-regulated, assuming that the up-regulated genes are randomly distributed. If instead 700 of them are up-regulated, that family is over-represented. The steps for this analysis are outlined below.
 +
 +
#Go to http://gostat.wehi.edu.au/ GOstat. Note the appropriate way to cite it at the bottom of the page, then proceed to the search form.
 +
#Choose the appropriate database for mouse, which is mgi. (Check Details to confirm.)
 +
#Choose a maximum p-value to display, such as 0.01.
 +
#Change Cluster GOs from -1 to 0. This will display your results as clusters of related gene families.
 +
#*Note that if the gene family BLANK is up-regulated, that alone might statistically up-regulate the higher-level family BLANK.
 +
#Change display to HTML, GO Stats only.
 +
#Leave multiple testing as the default. Any of these choices attempts to correct for the sheer number of statistical tests you are doing. Keep in mind, that if you make 10,000 comparisons, a simple p=0.05 value would give 500 false positive results!
 +
#In the first box, Group IDs, submit the systematic names of either your up-regulated or your own-regulated gene list.
 +
#For the second box, choose the file mouse-all.txt, which contains the full list of genes on your mouse microarray. Note that the MGI database may not be able to recognize all the genes, and that this also informs the statistical analysis.
 +
#Finally, click Submit and wait for your results to appear.
 +
#On the left-hand side, you should see GO groups listed in blue, followed by all the differentially expressed genes in that group. Further to the right, you see the number of genes expected to be differentially regulated for your sample and the number actually differentially regulated. There is an associated p-value with this over- or under-representation.
 +
 +
Take your time studying this list, noting which p-values are the highest, and which GO groups appear. Are any of these groups associated with systems that you would expect to be affected by the presence of an siRNA (if your control sample had no siRNA), or by the presence of a working versus scrambled siRNA (if that was your control)?
 +
 +
 +
 
DONE!
 
DONE!
  
 
==For next time==
 
==For next time==
 
Your first draft of your lab report is due next time. Remind yourself of the class expectations for [http://openwetware.org/wiki/20.109(F07):Guidelines_for_writing_a_lab_report your report]. Some extra information to guide you when you prepare your lab report is included [[20.109(F07): Expression engineering report| here]]. Email your report to nkuldell, astachow and nlerner AT mit DOT edu.
 
Your first draft of your lab report is due next time. Remind yourself of the class expectations for [http://openwetware.org/wiki/20.109(F07):Guidelines_for_writing_a_lab_report your report]. Some extra information to guide you when you prepare your lab report is included [[20.109(F07): Expression engineering report| here]]. Email your report to nkuldell, astachow and nlerner AT mit DOT edu.

Revision as of 16:47, 23 January 2009


20.109(S09): Laboratory Fundamentals of Biological Engineering

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REVISE TO REFLECT NEW ASSESSMENT, ETC

Introduction

Sony Playstation or Microsoft X-box? Boxers or briefs? Coke or Pepsi? We all know that taste can’t be mandated, but then how do standards arise. Standards are a fundamental and required aspect of engineering. Without them, machines can’t talk to each other, hardware is difficult to repair, and profits disappear (try to estimate the recent earnings by Betamax). In many cases, standards are government mandated, e.g. the US public school curriculum, cell phone technology in Europe, internet protocols worldwide. On other occasions, external events or pressures influence standards. Sweet N’ Low was essentially the only artificial sweetener on the market until the saccharine it contained was “shown” to cause cancer in lab rats. On rare occasions, standards arise through extreme behavior. In 1888, Thomas Edison wanted to demonstrate the superior safety of direct current (the technology his company marketed) so he publicly electrocuted dogs with 1000 volts of alternating current, the technology his competitor, Westinghouse, was marketing for use in homes.
Macintosh HD-Users-nkuldell-Desktop-metric-english.jpg

How do standards arise when there is no traditional financial market for them? In the case of BioBricks, the Registry of Standard Biological Parts is relying on the goodwill of the community to contribute standard parts that conform to the Registry’s rules. The payoff isn’t market share of the biological parts market, but rather the establishment of a shared resource that is reliable, reusable and useful. Community compliance to standards for microarray experiments and data analysis is similarly driven. Despite disagreement within the scientific community about how to collect meaningful microarray data, a “Minimum Information About a Microarray Experiment” (MIAME) checklist has been generated and is largely adhered to. “Minimum information” means only that the microarray data can be examined and interpreted by others…not a high bar for publication standards but one that is difficult to achieve since the arrays themselves are provided by different commercial vendors who disclose different amounts of information about their arrays. Moreover, the effort required to annotate MIAME data is significant and authors vary in their compliance.

Corroboration of published microarray data is further compounded by a lack of standards surrounding the data analysis itself. Processing the raw data mixes art and science. Algorithms used vary dramatically, and a single data set can appear compelling or noisy, depending on the analysis choices made by the investigator. For example, Cy3 and Cy5 are commonly used fluorescent probes but others dyes can be used and may be processed with different background correction and normalization factors. Not surprisingly, experiment protocols make a difference too. Researchers who indirectly label may find different outcomes than researchers who perform the same experiment but directly incorporate fluorescent dyes into their RNA. Also worth noting is human error, since microarrays experiments require many steps over many days. There are even stories of people scanning their slides backwards and consequently mis-identifying every spot on the array.

This lack of consensus should be both liberating for you today and also burdensome. You will have great freedom in how to analyze and interpret your data. Some initial steps are suggested but then you’re free to try different approaches that you are interested in and that make sense to you. You will need to carefully annotate and justify the choices you make, to allow others to understand and critique your approach. Good luck and have fun!

Protocols

Here is a rough outline of the steps you can take to examine your microarray data. There are many variations on this that are acceptable and that may be more interesting or appropriate for you. You should explore the data as you see fit.

  1. open txt file in xls (tab delimited)
  2. delete top 9 rows
  3. label a new worksheet for working with your data
  4. copy columns for: GeneName, SystematicName, Description, gMeanSignal, rMeanSignal gMedianSignal, rMedianSignal, gBGMeanSignal, rBGMeanSignal, gBGMedianSignal, rBGMedianSignal
  5. format the numerical cells as numbers with no decimal place
  6. consider mean and median variations and background, to correct as you see fit. Be sure you keep track in your notebook or in the xls file of your analytical decisions.
  7. start new column with ratio of green signal/red signal.
  8. start new column called log2green/red and use data in green/red column as =LOG(cell#,base), for example =LOG(D3,2) and drag corner to apply formula to all 44K cells. Again format to whole numbers if this does not happen automatically.
  9. Select entire sheet by clicking on diamond in corner then sort by log2 (green/red).
  10. Sort cells in decending order according to log2green/red
  11. What do you see? Are the duplicates in agreement? Are there particular genes you expect to see up or down regulated in the two samples. Ask the questions you want about this data...
  12. save as XLS worksheet or workbook

Data Analysis

Data Interpretation

Now that you’ve decided which genes you believe are differentially expressed between your two samples, you need some way of making sense of your data. In particular, you might like to see if some of the genes with apparent increased or decreased expression share common features.

As a first approach, you might manually list the ten genes most highly upregulated in your sample as compared to your control, then learn a little about each one using a gene ontology website. http://www.geneontology.org/ Do the ten genes come from a common family? What about the ten most downregulated genes?

Of course, you have data on tens of thousands of genes, and manual analysis is not the most practical way to mine your data. Instead, you can use freely available databases and programs to find groups of genes that are statistically over- or under-represented compared to their expected value. For example, say you are analyzing a pool of 15, 500 genes, and 3600 of them (23%) are up-regulated. If 1100 genes exist in a particular family, then you would expect 23% of them, or ~250 to be up-regulated, assuming that the up-regulated genes are randomly distributed. If instead 700 of them are up-regulated, that family is over-represented. The steps for this analysis are outlined below.

  1. Go to http://gostat.wehi.edu.au/ GOstat. Note the appropriate way to cite it at the bottom of the page, then proceed to the search form.
  2. Choose the appropriate database for mouse, which is mgi. (Check Details to confirm.)
  3. Choose a maximum p-value to display, such as 0.01.
  4. Change Cluster GOs from -1 to 0. This will display your results as clusters of related gene families.
    • Note that if the gene family BLANK is up-regulated, that alone might statistically up-regulate the higher-level family BLANK.
  5. Change display to HTML, GO Stats only.
  6. Leave multiple testing as the default. Any of these choices attempts to correct for the sheer number of statistical tests you are doing. Keep in mind, that if you make 10,000 comparisons, a simple p=0.05 value would give 500 false positive results!
  7. In the first box, Group IDs, submit the systematic names of either your up-regulated or your own-regulated gene list.
  8. For the second box, choose the file mouse-all.txt, which contains the full list of genes on your mouse microarray. Note that the MGI database may not be able to recognize all the genes, and that this also informs the statistical analysis.
  9. Finally, click Submit and wait for your results to appear.
  10. On the left-hand side, you should see GO groups listed in blue, followed by all the differentially expressed genes in that group. Further to the right, you see the number of genes expected to be differentially regulated for your sample and the number actually differentially regulated. There is an associated p-value with this over- or under-representation.

Take your time studying this list, noting which p-values are the highest, and which GO groups appear. Are any of these groups associated with systems that you would expect to be affected by the presence of an siRNA (if your control sample had no siRNA), or by the presence of a working versus scrambled siRNA (if that was your control)?


DONE!

For next time

Your first draft of your lab report is due next time. Remind yourself of the class expectations for your report. Some extra information to guide you when you prepare your lab report is included here. Email your report to nkuldell, astachow and nlerner AT mit DOT edu.

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