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The workflow of Affymetrix GeneChip data analysis

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In our global gene expression service we offer Affymetrix GeneChips Whole-Transcript Expression arrays and 3’ IVT Expression arrays. 200 ng of total RNA is used in labelling reaction to according to manufacturer’s protocol. Hybridization cocktail is loaded to array cartridge and hybridized in Hybridization Oven 645 for 16 hours on 45-Celsius degree.  Washing steps are fully automated (Fluidics Station 450). For scanning Affymetrix GeneChip Scanner 3000 7G System is used. Primary data are collected by Affymetrix GeneChip Command Console (AGCC) software. DAT file (image file) contains the raw data, AGCC software aligns grid on the image file and generate CEL file. CEL files can be used for further Quality control (QC) and statistical analysis.

 

QC test

For QC analysis we use Affymetrix Expression Console (EC) software. CEL files are imported into EC software, quantile normalization Robust Multichip Analysis (RMA) is performed and CHP files are generated. CHP files contain RMA normalized data.

Quality of the data is determined by checking the internal spike controls (bac spikes and poly-A controls) and the signal distribution of the arrays (relative box plot).

Statistical Analysis

GeneSpring 7.3 and/or 11.5 software (Agilent BioTechonologies) is used for analyzing of Affymetrix GeneChips. CEL files imported into GeneSpring using RMA/GCRMA algorithm. Baseline transformation steps can be applied on samples: median of all samples or median/mean of specific sample.

Determine differentially expressed genes

  1. Add parameters of the samples
  2. Create interpretation to determine experimental groups
  3. Determine expressed genes: filter on expression of raw values between 20-100 percentile
  4. Determine non-changing and changing genes: filter on expression of normalized values between -0.5 to 0.5 (non-changing genes). Remove list of non-changing genes from list of genes raw values between 20-100 percentile = changing genes
  5. Statistical test: parametric/non-parametric test with/without Multiple Testing Correction (Benjamini-Hochberg Correction) ® differentially expressed genes
  6. Data interpretation: Pathway analysis/Gene Ontology analysis

 

 

 

The workflow of 384-well TLDA Micro Fluidic Card analysis

1. Basic analysis Objective:  to check the operability of the cards

We monitor the realization of amplification after every running, and the compliance of curve intensity assays. If a card has a problem, or one of the sample does not work well, we warn our partner immediately to give chance to change the research plan in time.

When all data are collected, we create a detailed basic analysis. Its steps are:

  1. Importing data to SDS software from each card and clicking on analyze
  2. Visual checking of the existence of amplification curves and shapes in every row
  3. Recording the position of aberrant amplified assays
  4. Performing the analyses, export dara to txt format
  5. Making an excel document from txt format
  6. Picking the „Undetermined” assays. These are the so-called „failed Assays”, which may have several explanation:

 a) „Failed assays1”: some assays on some plates create together aberrant signals after analysis. Their dRn value is extremely high, usually over 100, but to these wells Ct value can not be ordered. For further analyses it is strongly recommended to delete these Ct values.

b) „Failed assays2”: dRn < 1
The aberrant amplification curve of these assay types is well seen in the case of individual analysis. These assays are likely to be defective. For further analyses it is strongly recommended to delete these Ct values.

You have to be careful with assays which have generally smaller dRn values, even if they have Ct value. On amplification curve the weak amplification is clearly seen, which may result incorrect measurement.

 

2. Normalization Objective: to normalize the raw Ct values obtained from basic analysis with using appropriate normalizing (housekeeping) genes

Since usually in advance it is not known which are the most ideal genes for normalization in a given experimental system, we recommend to use a minimum of 3-5 genes. For information about the most commonly used normalizing genes click on ABI’s website.

To select the normalizing gene we use the algorithm published in Andersen et al. (2004), Cancer Research 64 5245-5250.

 

3. Biostatistics Objective: to identify the significant different genes from normalized gene expression data

To prepare the biostatistics based on the principles of Integrated Databases part. The result will be easily understandable and based on our partner needs, the results can be ready to publish and consultation.

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