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Versions
| 6-30-2008 | 1.0.6 | Compatible with R-2.7.x |
| 7-25-2007 | 1.0.0 | This package introduced an approach to measure the pathway/geneset activity in individual samples |
calcfunctionscore
Knowledge-based gene set analysis is quickly gaining popularity due to its ability to detect subtle but coordinated expression changes in functionally related genes as well as its ability to link gene expression changes to higher level biological functions. However, prevailing gene set analysis methods are only applicable to two-class sample comparison situations. Multiple sample class analysis is not possible nor the investigation of potential heterogeneity of pathway/gene set activity among samples in the same class. In addition, existing methods mainly deal with the additive effects of multiple genes without considering higher order gene interaction effects. It is not possible to consider pathway topology in current gene set analysis methods, either. We propose a simple and flexible approach called FunctionScore to measure the pathway/gene set activity in individual samples based on the z-score of gene expression levels. The Function score approach can easily incorporate higher order gene interactions and pathway topology. We expect the FunctionScore method can extend the knowledge-based expression analysis paradigm to meta expression data analysis, genetic genomical analysis, functional correlation between different pathways/gene sets, pathway/gene set level functional heterogeneity as well as roles of gene interactions in various pathophysiological processes.
Here is an example of analysisin WGAS.
For other examples, please run '?calcfunctionscore' in R session
read.gmt
Function ’read.gmt’ creates a ’functionDb’ object by reading a gmt file. Gmt file format detail is at here
write.gmt
Funciton ’write.gmt’ writes a ’functionDb’ object to a gmt file
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Versions
| 05-31-2011 | 1.2.0 | compatible with PM chips |
| 07-30-2009 | 1.1.0 | Released with Custom CDF version 12 |
| 02-11-2008 | 1.0.2 | Minor modifications |
| 12-7-2007 | 1.0.1 | compatible with R 2.6 |
| 7-25-2007 | 1.0.0 | A couple of functions related to custom CDF are in this package. They are also integrated into our WGAS. |
removeprobe
Function 'removeprobe' removes unwanted probes from cdf environment
getsnpprobe
Funciton 'getsnpprobe' returns a list of allele-specific probes based on user-input criteria. The list can be used along with 'removeprobe'
createcdfenv
Function 'createcdfenv' generate cdf environment on-the-fly. No cdf package is needed
read.affybatch.hybrid
Function 'read.affybatch.hybrid' reads different chips into one AffyBatch object by converting coordinate of probes that have the same sequence in different chips. And a new cdf environement variable that would only use common probes would be assigned to global environment for following analyses.
Class functionDb
Class 'functionDb' represents functionally related genes and biological functions. One unique feature is that it can combine bilogical functions that have the same gene list into one function. This is especially helpful when many similiar functions would have the same gene list after their gene list is altered by the expression gene list.
The package is designed for generating gene-normalized exon signal for each sample in a sample set. It requires users to provide 1) exon expression values for individual samples in a sample set 2) gene expression values for individual samples in the same sample set 3) gene-exon relationship. This package only accepts log(expression value) as input. Commonly used R-packages for probe level analysis such as RMA, GCRMA, AffyPLM already generate log2 values in their output. However, users of dCHIP and MAS5 algorithms need to add a log2 transformation step to the corresponding expression values
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Versions
| 8-16-2006 | 1.0.8 | supports Custom CDF version 7 and 8 |
There are four tools in this package. If you meet any problem or have any suggestion, send email to daimh@umich.edu.
'load.py' is for uploading users' own Affy celfiles to our genechip analysis web servers.
'affysplit.py' is for splitting Affymetrix cel files to other chip types. Currently it can split Hs133P, Mm430, Rn230 to Hs133A/Hs133B, Mm430A/Mm430B, Rn230A/Rn230B.
'affyconvert.py' is for converting Affymetrix cel files to other chip types. Currently it can convert Hs133A to Hs133Av2, Mm430A to Mm430Av2, and vice versa.
'probefilter.py' is for excluding a list of probe from a cdf file.
Note: we have flash demos
A. How to install this package
1. Download latest Python version 2.* from Python's offical site according to your own platform, and install it. MBNIUM 1.9 is tested with Python 2.6.2.
2. For Windows platform, download MBNIUM Win32 Package Version 1.9 and run it. For all other platforms, download MBNIUM Source Package Version 1.9. For example, to install in on Linux platform, use following commands to install it
tar xvfz MBNIUM-1.9.tar.gz
cd MBNIUM-1.9
python setup.py install
B. To use the upload program
1. Sign-up an account at here with your email address. And ask your group users to sign-up there too.
2. Send web master your application with the following information to create a group.
For application that only has a few columns. Most applications should be this way
a) Group name
b) Group administrator's WGAS account name, only administrator can upload
c) A list of user WGAS account names
d) A list of column names, data types (String[Length]/Number) and description (optional). Column "Name"(celfile name) and "Chip_type" are always mandatory. Description is especially useful when you have a group of users
For example
a) UM_cancer
b) abc@umich.edu
c) a@foo.com, b@bar.com
d)
| chip_type | String | gene chip type |
| name | String | celfile name |
| age | Number | Patient's age |
| sex | String[1] | Patient's gender |
| tumor | String[60] | Patient's tumor |
| cancer | String[60] | Patient's cancer |
If you has many columns, and you think it is better to group them for easier management, please send me the following information instead.
a) Group name
b) Group administrator's WAGS account name, only administrator can upload
c) A list of user WGAS account names
d) A list of column set names. If one has only a few columns, just leave this blank. Or if one has tens of columns or more, they could be placed in different column sets for easier management.
e) A list of column names and data types (String[Length]/Number), and column set they belong to if you defined column sets at the above step. Name(celfile name) and Chip_type are always mandatory
For example
a) UM_cancer
b) abc@umich.edu
c) a@foo.com, b@bar.com
d) "Sample Information", "Medical History"
e)
| chip_type | String | Sample Information | gene chip type |
| name | String | Sample Information | celfile name |
| age | Number | Sample Information | Patient's age |
| sex | String[1] | Sample Information | Patient's gender |
| tumor | String[60] | Medical History | Patient's tumor |
| cancer | String[60] | Medical History | Patient's cancer |
3. After your application is approved, you need to use our tool to upload your tab file and cel files. The tool is a platform-independant Python package, suitable for Windows, Linux, Mac, many brands of UNIX, Amiga and many other platforms.
3.1 Check out part A "How to install this package" to make sure you already installed the latest version of this package.
3.2 Create a temporary work folder, for example, "D:\upload"
3.3 Create a subfolder 'cel' under the work folder, and copy all cel files that you want to upload to the subfolder. For example, "D:\upload\cel"
3.4 Download tab-delimited file 'files.txt' from web server by selecting "Download attributes files" task, copy the file to the work folder, and rename it to "files.txt", modify it to make it consistent with those cel files you want upload. A tip, using Microsoft Excel to edit it would be easy
3.5 Open a command window, For Windows user, this mean you need to click Start Menu, then click Run Menu, then type "cmd", and click OK button. A black window will show up, this is command window.
3.6 In the command window, go to the work folder. For example, if your work folder is in "D:\upload", you need to type "d:", hit Enter key, then type "cd upload", and hit Ener key again. If the command prompt became "D:\upload\>", you are already in the work folder "d:\upload"
3.7 In the command window, type "C:\Python24\python C:\Python24\Lib\site-packages\MBNIUM\load.py MODE USER GROUP URL" and hit Enter key, to check and upload those files. If there are error messgae returned, fix it and run it again. If it succeeded, you will see a message saying "Done successfully. Whenever it is ready to analyze, you would receive an email"
For parameter "MODE", if you want to append new files, use "append", otherwise if you are sure you want to destroy all existing files, use "truncate".
For parameter "USER", substitute it with the administrator's email address.
For parameter "GROUP", substitute it with the group name.
For parameter "URL", it should be https://arrayanalysis.mbni.med.umich.edu or https://analysis.mbni.med.umich.edu, depending on which server your group is on.
If Python is not installed in the default folder, substitute two "C:\Python24" in the command to its correct path too.
3.8 It would prmopt for password, type your password and then hit Enter key.
3.9 After WGAS server load the uploaded file into database, you would receive an email notification. It is ready to analyze your celfiles
C. To use the celfile splitting/converting program
1. Make sure you already installed the latest version of this package
2. Open a command window.
3. Run "C:\Python24\python C:\Python24\Lib\site-packages\MBNIUM\affysplit.py --help" or "C:\Python24\python C:\Python24\Lib\site-packages\MBNIUM\affyconvert.py --help" to show detailed help message
D. To use 'probefilter.py'
1. Make sure you already installed the latest version of this package
2. Open a command window.
3. Run "C:\Python24\python C:\Python24\Lib\site-packages\MBNIUM\probefilter.py --help" to show detailed help message