Transcriptome PCR Array Data Analysis
The data analysis software for Transcriptome PCR Arrays is an excel
spreadsheet that performs the ΔΔCt based fold-change calculations from raw
threshold cycle data from the gene-specific real time-PCR assay. The spreadsheet
delivers results in a tabular format and helps in outlier identification and hit
Download the Data Analysis Spreadsheet
The Excel-based data analysis software for Transcriptome-on-Array system
automatically performs all ΔΔCt based fold-change calculations from uploaded raw
threshold cycle data for the gene-specific and housekeeping gene real time-PCR
assays. The spreadsheet delivers results in a tabular format and helps in
automatic outlier identification and "hit" (transcription factor)
Detailed instructions for using the data analysis software is given on the
webpage above as well as within the spreadsheet. Provided below is a brief
overview of the analysis.
- Read and follow detailed instruction given on "Instruction" sheet.
- Copy plate specific gene information from the website and paste on "Gene
- Copy the gene-specific Ct values from the PCR machine and paste them into the
"Test Data" worksheet.
- Copy the housekeeping gene Ct values from the PCR machine and paste them into
the "Control Data" sheet.
NOTE: Any Ct values reported as greater than or equal to 35 or as N/A (not
detected) is considered a negative call, and recognized as outlier and marked as
- Check "QC report" sheet to identify outliers and any other possible
The "QC Report" sheet is designed to provide the user a quality
assessment of the data generated.
- PCR reaction quality control: It will report the PPC evaluation result for
each plate. If the PPC Ct value for the corresponding assay is higher than 25,
your PCR reaction may not good enough for the whole assay. It will be marked as
- Gene-of-interest expression level in no target control samples: If the
gene-of-interest expression Ct value for the negative control siRNA sample is
greater than 35, the calculated fold changes will not reflect the true
expression changes between the experimental samples and controls and hence will
not be analyzed and will be marked as "Your target gene expression level is
low, the result may not reliable".
- Column and row data reliability analysis: will help the user identify possible
position-related data errors, for example dispensing was skipped for one column
that results in significantly no or low data compared with all of the other
columns or rows. Researchers should be aware of these events and carefully judge
the results. If a significant number of hits come from a single row or column,
it might reflect false positives.
- Users should enter in experimental information in the "Summary"
worksheet and automatically identify list of "hits" (regulators
involved in regulating the expression of the gene-of-interest).
- Select the "Result 2D" worksheet to view the potential hits. The
threshold for a "hit" can also be adjusted within this worksheet.
- Select "3D" worksheet to view the 3D figure for any position
The "hits" selection uses the Median Absolute Deviation (MAD)
method to identify the positive and negative transcriptional regulators. This
method can assess the strength of a "hit" and is a superior hit
selection method compared to traditional methods, especially if the user chooses
to perform fewer replicate experiments. The threshold is calculated for each
individual experiment. If the user wants to use a fixed threshold, such as a
3-fold change, users can set it manually in "Result 2D" worksheet.
If a note reads "Please note you are manually setting the
threshold!" shows up, it means the user has selected the manual threshold
setting. Users can switch back to default setting just by deleting the number on
the "Result 2D" worksheet.
NOTE: Change data only in yellow highlighted cells of data analysis software.
Gray and white cells contain formulas for calculation or results. Please do not