qPyCR is written for Python 2.7 and uses a command line interface to generate numerical and graphical output.
REQUIREMENTS
qPyCR requires the installation of the Python modules: numpy, scipy, pandas, and matplotlib. We recommend using pip2 to obtain packages.
Mac users should see this guide on Python installation, to obtain an updated release, separate from the default bundled installation.
INPUT
qPyCR accepts '.csv' formatted fluorescence data from qPCR reactions. The .csv sheet should contain a 'Cycle' column. Subsequent reaction columns are grouped based on their headers. Column headers should be labeled as <Group>-<Sample>, without spaces or special characters. Example input data is provided with the source code.
USAGE
The source installation directory contains two '.py' files and two folders labeled ./infiles/ and ./outfiles/.
Place the properly formatted '.csv' file in the ./infiles/ directory.
Open a shell and navigate to the source installation directory.
Here's the basic usage example:
> python2 qPyCR.py -f example.csv
For more information on usage options:
> python2 qPyCR.py --help
usage: qPyCR.py [-h] [-f FILENAME] [-i INDIR] [-o OUTDIR] [-n NORMALIZE]
[-w WEIGHT] [-c CYCLES]
optional arguments:
-h, --help show this help message and exit
-f FILENAME, --file FILENAME
--file=<your-file.csv>
-i INDIR, --in_dir INDIR
directory for incoming files, default = ./infiles/
-o OUTDIR, --out_dir OUTDIR
directory for outgoing files, default = ./outfiles/
-n NORMALIZE, --norm NORMALIZE
limit (default), max, global
-w WEIGHT, --weight WEIGHT
if True, use weighting function in fitting
-c CYCLES, --cycles CYCLES
Number of cycles for fit
OUTPUT
Output files are generated in the ./outfiles/ directory and are labeled according to their input file (example.csv-*):
*norm_* : Normalized output based on user input (default: limit)
*raw_* : Non-normalized output
*_stats_* : Initial seed , max, and Kd values
*_fit_* : Global abundance fits for each curve modeled by these values
*_group* : Grouped stats/fit based on column headers.
Stats include means and standard deviations for each group.
Fits are generated from the mean of the stats.
*.png/pdf : Graphical output of abundance fits from matplotlib