TaqMan SPPs

From LipidomicsWiki

Contents 1 Input 2 Output 3 Processing 4 Proposals and discussion points 5 Implementation 6 Processing Pipeline

Input

After performing RT-PCR experiments using TLDA cards from Applied Biosystems, the platform application software (mainly SDS RQ Manager) will analyses the generated fluorescence curves, and therefore provide a Ct value for each detector. Following this primary analysis, data shall be exported as a tab delimited txt format. More information for this exporting step is available by clicking here.

There are six mandatory columns that must be present on the output file in order to make it readable by Integromics RT-PCR analysis software. Those columns are:
     • #: defines a row number for each detector contained in each plate
     • Plate: defines the plate name
     • Pos: appears as an alpha-numerical value and it defines a well identifier within each plate
     • Sample: defines the sample name
     • Detector: defines the target gene name and TaqMan Assay ID
     • Task: defines task for each detector, and appears as target (also Unknown), or Endogenous Control (reference genes used for normalization, also NTC or Standard)
     • Ct: Cycle threshold value determined by the application software analysis

Output

The analysis of RT-PCR experiments with Integromics software is expected to provide an output file containing a list of genes that are differentially expressed with statistical significance. Such an output file must be a Microsoft Excel file and should contain the following columns:
     • Detector: defines gene name and TaqMan assay ID
     • P-value: obtained from the performed statistical test
     • Adj. P-value: obtained after the adjustment of the false discovery rate
     • Log10RQ: defines the fold change obtained after comparison of calibrator group against target group, and it appears in a logarithmic scale
     • Significancy: defines whether the obtained results from relative quantification are significant, based on the defined cut-off for the performed statistical test

     • Ct status: flag for Ct values above a defined threshold

     • Ensembl gene ID for each detector

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Processing

1. Load Data

1.1 Import Plate raw data files (only in tab delimited text file (*.txt))
Following the loading of files (in the expected SPP input format), the number of plates contained in the study should be automatically recognized.
Upon loading files, it will be possible to assign a numerical Ct value (corresponding to the maximum PCR cycle of the run or end cycle) for “undetermined Ct values”. Undetermined value refers to a missing value that occurs when the fluorescence intensity signal emitted within a reaction did not cross the threshold. A caution mark will tag those values.

1.2 Import or create Experimental design
Experiment design information defines the experimental conditions or dependent variables or factors that were used for the experiment. Such an information will be mandatory for relative quantification of gene expression.

1.3 Rename samples
Experiments based on ABI TLDA’s can show flexibility in terms of card configurations, e.g. two cards can share only a subset of the genes, different samples can be analyzed in one card or alternatively one card can profile a single sample, etc. In addition samples in different cards may be technical replicates and therefore should be aggregated. These facts require an application that allows users to control this level of experiment configuration, which will be provide as an option to rename samples.
Therefore this step will allow regrouping identical samples that have been assigned with different sample names, which are technical replicates. If identical samples for the same factor remain with different sample names, they will be treated as biological replicates for further analysis.

 

2 Quality control of Biological replicates
In order to visualize the quality of the Sample Group Replicates and detect abnormal behavior within a sample group or between different sample groups, a visual interface will permit the user to inspect the correlation among samples of biological replicates. .Thus it will be possible to discard aberrant samples for the relative quantification step..

3 Detectability criteria

Under Detectability Criteria the user will be able to define a Ct threshold that will flag any detector with a Ct value above the defined threshold.
These flagged detectors could provide a useful alert about detectors with very low degree of expression or no expression at all. Values above the defined threshold (for example 38) are usually unreliable and so it is recommended to flag these detectors in order to identify them for results interpretation.
  

4 Normalization with Endogenous Detectors
The purpose of Normalization is to correct differences in the amount of nucleic acid input. The basic principle when using endogenous genes to normalize data is that they have to be expressed at constant levels across the samples and that their expression does not vary under the experimental conditions tested. Endogenous control detectors should have relatively small variability across all the samples.
Some of the most commonly used endogenous control genes are: β-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPD), 18S ribosomal RNA (rRNA), β-glucuronidase (GUSB), β-2 microglobulin (B2M), phosphoglycerate kinase (PGK1), hypoxanthine phosphoribosyltransferase (HPRT).
The Delta Ct (ΔCt) is the resulting value representing the difference between the Ct value of a detector and the most stable endogenous control gene(s) for the same sample. 
                                                                 ΔCt = Ct (Target) - Ct (Endogenous Control)

5 Relative Quantification
Differential Expression is the difference between the average of the ΔCt value of the target sample and the average ΔCt value for the corresponding calibrator sample. This value will be used to calculate expression fold value as follows.

                                                               RQ = 2-Δ(ΔCT)

                                                               Log10RQ = Log10 2-Δ(ΔCT)

- Log10RQ = 0 means no expression.
- Log10RQ = 1 means that the test sample is expressed 10 times greater than in the calibrator sample.
- Log10RQ = -1 means that the test sample is expressed 10 times less than in the calibrator sample.

Also a statistical test will be performed to get the significance of the relative quantification resultst.

Proposals and discussion points

Described RT-PCR analysis software is available on request: lipiminer@integromics.com

Implementation

Implementation of TaqMan SPPs

Processing Pipeline

Processing Pipeline for TaqMan SPPs

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