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Series Test of Cluster




choosefile *  example








Greater than zero



choosefile   example




1. Global modifications:


2. Background modifications:


3 Profile edition:


1. Function:
Gene expression pattern analysis is used to cluster short time series expression data. The kernel program of this analysis is STEM software (Short Time-series Expression Miner:http://www.sb.cs.cmu.edu/stem/.


2. Application:

To find and visualize the change trend of gene abundance in continuously changing samples or groups, it can also be used for the analysis of protein, metabolites and so on. 3-5 groups data are recommended to analysis.


3. Input:
(1) Input file: Input tab delimited text files with headers. If your data is not TAB delimited, you can use Excel to convert it. The table contains gene expression value, in which the first column is gene IDs and the others are expression information. The sample order in this table will determine the trend order in the result. Gene annotation file contains gene description, pathway annotation, GO annotation, etc, with gene IDs as the first column.

Note: If the file is more than 5 MB in size, you need to upload local data in "My data" section, and then select the cloud file tool page for analysis.

(2) Maximum number of Model Profiles:All gene will be clustered into profiles, and the numbers of profiles will be no more than this threshold. No more than 20 profiles is suggested because too many profiles will fragmentize the result and are hard for your interpretation.

(3) Data normalization:
All time series will be transformed so that the time series starts at 0. This can be done in one of three ways based on the option selected to the left. Given a time series vector of values for a gene (v_0,v_1,...,v_n) the options are:
Option 1.Log normalize data'− the vector will be transformed to (0,log₂(v_1)−log₂(v_0),...,log₂(v_n)−log₂(v_0)). Note that any values which are 0 or negative will be treated as missing.

Option 2.'Normalize data' − the vector will be transformed to (0,v_1−v_0,...,v_n−v_0)
Option 3.'No normalization/add 0' − a 0 will be inserted transforming the vector to (0,v_0,v_1,...,v_n)

(4) P value threshold of significant profiles:The value will determine profiles of genes enrichment significantly after permutation test. The default value is 0.05.

(5) Minimum absolute expression change: After transformation (Log normalize data, Normalize data, or No Normalization/add 0), if the absolute value of the gene's largest change between any two time points is below this threshold, then the gene will be filtered. The default value is 2.

(6) Desc file:This is an alternative parameter. The first column is gene IDs and the others are gene annotation as your customized. The information provided here will be add to the output table and this will help you interpret the result.


4. Output:

(1) all_profile.xls:A table containing cluster results of all profiles.

(2) all.xls:the gene expression information of your input file.

(3) trend_all_by_gene_number.png:The profiles image ordered by gene number in each profile.

(4) trend_all_by_pvalue.png:The profiles image ordered by enrichment p value.

(5) Profile*.png:detailed cluster image of each single profile.

(6) Profile*.xls:a table containing culster results of each single profile.



5. Figure adjustment parameters interpretation:
Graphics adjustment parameters:
Font size: modify the size of all fonts in the drawing000
Font selection: modify all font styles in the drawing
Trend column number: set the number of modules in one line
Display value: check to display the size of P value
Graph sorting: sorting according to the p value of the module or the number of genes in the module
Background modification:
Line thickness: modify the line thickness in the module
Area transparency: modify the background transparency of all modules
Line color: modify the line color
Text color: modify all the text colors in the graphic
Trend color: modify the background color for modules with significantly enriched existing colors


Edit trend:

(1) double-click to delete the module, and click to restore it
(2) Customize module order

(3) Custom module name


Download:
Select "svg" or "png" format to download the resulting graph after adjusting the height and width of the graph.

Results Display      (Click " task ID" to view different analysis results)