QPCR

Two python scripts aim at calculating the Delta_Ct, Delta_Delta_Ct, Fold Changes, Student's t-test, and P-value value which produced by Quantitative real-time polymerase chain reaction (qRT-PCR).

Why

I have too much qpcr data to process. It costs me a lot of time. I want a simple way to do this.

Compute QPCR results from QPCR output

Notes:

1. For ABi 7900 users, data column names must be 'Sample Name','Detector Name','Ct','Ct Mean'. But 'Ct StdEV' is optional.
2. For ABi ViiA 7 or Q7 users, you can use qpcrRead.py to extract data computing results directly.
3. For Other machines, you could use qpcrCalculate.py to calculate results with your data. the Input file format has to exactly be the same as test_interest_data.xls.

You should specify internal control name and experimental control name for your data sets. The following file formats are supported: xls, xlsx, csv, txt.

Dependency

Python2.7 or Python3+

• numpy
• scipy: for t-test
• pandas
• xlrd: excel reader
• xlwt: excel writer
• matplotlib: for plotting (to do)

Before using this module, see help

    python qpcrRead.py -h

    python qpcrCalculate.py -h

Parameters for qpcrCalculate.py

Parameters:

    usage: qpcrCalculate.py [-h] -d DATA [-s SHEET] [-i IC] -e EC [-o OUT]
                            [-m {bioRep,techRep,dropOut,stat}] [--header HEAD]
                            [--tail TAIL] [--version]

Calculate Delta Ct, DDelta Ct, Fold Changes, P-values for QPCR results.

    optional arguments:
      -h, --help            show this help message and exit
      -d DATA, --data DATA  the file(s) you want to analysis. For multi-file
                            input, separate each file by comma.
      -s SHEET, --sheetName SHEET
                            str, int. the sheet name of your excel file you want
                            to analysis.Strings are used for sheet names, Integers
                            are used in zero-indexed sheet positions.
      -i IC, --internalControl IC
                            the internal control gene name of your sample, e.g.
                            GAPDH
      -e EC, --experimentalControl EC
                            the control group name which your want to compare,
                            e.g. hESC
      -o OUT, --outFileNamePrefix OUT
                            the output file name
      -m {bioRep,techRep,dropOut,stat}, --mode {bioRep,techRep,dropOut,stat}
                            calculation mode. Choose from {'bioRep',
                            'techRep','dropOut'.'stat'}.
                            'bioRep': using all data to calculate mean DeltaCT.
                            'techRep': only use first entry of replicates.
                            'dropOut': if sd < 0.5, reject outlier and recalculate mean CT.
                            'stat': statistical testing for each group vs experimental control.
                            Default: 'dropOut'.
      --header HEAD         Row (0-indexed) to use for the column labels of the
                            parsed DataFrame
      --tail TAIL           the tail rows of your excel file you want to skip
                            (0-indexed)
      --version             show program's version number and exit

Usage

Extract Data from ABi machine output and Calculate Foldchange

bash qpcr-run.sh -d test/h9_vii7_export.xls -i GAPDH -e H9_NT_LSB_D16 -m dropOut -o test

Behind the scenes, extract data first

    python qpcrRead.py -d test/h9_vii7_export.xls -o test/test_interest_data.xls

Then calculate Delta_Ct, Delta_Delta_Ct, Fold_Changes

    ## from qpcrRead.py output
    python qpcrCalculate.py -d test/test_interest_data.xls \
                            -i GAPDH \
                            -e H9_NT_LSB_D16 \
                            -m dropOut
                            -o test/20150625_NPC_Knockdown

Not ABi machine output data

For other qRT-QPCR output formats, you can reshape your data structure to be the same as test_interest_data.xls. Then use qpcrCalculate.py directly for your input.

python qpcrCalculate.py -d test/test_interest_data.xls \
                        -i GAPDH \
                        -e H9_NT_LSB_D16 \
                        -m dropOut
                        -o test/20150625_NPC_Knockdown

Perform Student's t-test from n independent experiments.

Step 1: Calculate Delta Ct

Input file format:

1. use qpcrCalulate.py -m {'bioRep','techRep','dropOut'} output results as 'stat' input.
2. or your own file contained column: Delta Ct. See example file in test folder.

Step 2: Run statistical testing

Set -m stat explicitly.

i.e. For 3 independent experiments (biological replicates), try:

• Method 1: assume all input files contained column Delta Ct.

python qpcrCalculate.py -d test/input1.xls,test/input2.xls,test/input3.xls \
                        -e H9_NT_LSB_D16 \
                        -m stat \
                        -o test/output

• Method 2: single input file contained column Delta Ct ( 3 experiments concatenated into one single table).
  then run:

python qpcrCalculate.py -d test/input_combined.xls \
                        -e H9_NT_LSB_D16 \
                        -m stat \
                        -o test/output

Note: if you use -m stat and NOT Delta Ct column in your input, the program will try to run -m bioRep first, and then do statistical testing.

TODO List

1. Generate bar or line Plots using Matplotlib automatically
2. Generate Python Plotting Scripts for customized modification