ct2vl 1.0.3

A small package that converts SARS-CoV-2 PCR test Ct values to viral loads

ct2vl: C_t values to viral loads

A python package and command line tool to convert SARS-CoV-2 PCR C_t values to viral loads.

Installation

Assuming python and pip are installed, at the command line, run

pip install ct2vl

Usage

Python package

from ct2vl.conversion import Converter
converter = Converter(traces="traces.csv", LoD=100.0, Ct_at_LoD=37.96)
ct_values = [23.1, 31.8, 28.4, 34.0, 30.2]
viral_loads = converter.ct_to_viral_load(ct_values)

The traces argument of Converter can also accept a pandas DataFrame or numpy ndarray.

Command line tool

To calibrate ct2vl run

python3 -m ct2vl calibrate <traces> <LoD> <Ct_at_LoD>

For example

python3 -m ct2vl calibrate example/path/traces.csv 100.0 37.96

Once ct2vl has been calibrated, C_t values can be converted to viral loads with

python3 -m ct2vl convert <Ct>

One or multiple C_t values can be passed. For example

python3 -m ct2vl convert 23.1

or

python3 -m ct2vl convert 23.1 31.8 28.4 34.0 30.2

Output can be saved to a file by providing a filepath to the optional flag --output

python3 -m ct2vl convert 23.1 --output example/path//viral_loads.tsv

Input

The command line tool has two modes calibrate and convert.

• mode: calibrate uses positive PCR traces and their corresponding C_t values to calibrate ct2vl for a given machine
  1. traces: Filepath to a csv file containing PCR reaction traces
  2. LoD: Limit of detection (LoD): copies of SARS-CoV-2 viral genomes/mL (copies/mL; viral load at the LoD)
  3. Ct_at_LoD: C_t value at the limit of detection (LoD)
• mode: convert calculates the viral loads for given C_t values
  1. Ct: A list of C_t values that will be converted to viral loads
  2. --outfile: An optional filepath to save the results to

For calibrate mode, traces is a csv file where each row corresponds to a PCR reaction curve and each column is a cycle in the reaction (example below).

Example traces csv file

	0	1	2	3	4	5	6	7	8	...	29	30	31	32	33	34	35	36	37	38
0	0.000000	0.000000	0.000000	0.000000	0.000000	0.036339	0.105185	0.140072	0.213080	...	75.820797	103.521744	128.057320	146.543328	158.994255	166.878167	171.755301	174.695708	176.480445	178.004733
1	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.032859	0.106156	0.115075	...	1.498253	3.242639	6.929736	14.413807	26.735120	42.908856	60.642276	77.436979	90.894471	98.702497
2	0.008563	0.077690	0.112795	0.112795	0.112795	0.112795	0.112795	0.112795	0.250068	...	239.545742	240.219129	240.706006	241.006463	241.206473	241.304155	241.389261	241.421420	241.421420	241.421420
3	0.000000	0.000000	0.043038	0.061215	0.061215	0.061215	0.061215	0.061215	0.061215	...	177.787281	185.406029	190.989406	195.223471	198.308876	200.551725	202.243363	203.463125	204.342186	205.015795
4	0.000000	0.000000	0.000000	0.014523	0.038724	0.083699	0.116689	0.131569	0.131569	...	173.993467	183.424795	190.361277	195.482481	199.194926	201.862966	203.861077	205.329761	206.289263	206.824331
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16	0.000000	0.000000	0.050974	0.085428	0.085428	0.085428	0.085428	0.085428	0.085428	...	194.717208	201.251802	206.238474	209.977472	212.850522	214.995915	216.711037	217.980163	218.880729	219.491032
17	0.000000	0.000000	0.000000	0.000000	0.000000	0.080598	0.126204	0.224708	0.254782	...	187.883601	199.664452	208.208690	214.361070	218.809716	222.239450	224.701419	226.574815	227.972374	229.085984
18	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.092015	0.215467	0.297682	...	51.416916	80.692981	112.770737	142.339464	165.674804	182.084870	193.083210	200.171681	204.779740	208.237394
19	0.443534	0.443534	0.443534	0.443534	0.443534	0.443534	0.443534	0.443534	0.443534	...	70.164310	97.490787	122.766640	142.936795	157.475829	167.632976	174.804737	179.870366	183.435423	186.212804
20	0.000000	0.060721	0.060721	0.064403	0.064403	0.064403	0.064403	0.064403	0.064403	...	225.043424	226.333617	227.403273	228.214905	228.866112	229.370140	229.680560	229.890248	229.967121	229.967121

Example command line output

	LoD	Ct_at_LoD	Ct	viral_load	log10_viral_load
1	100	37.83	14.73	3.3277e+08	8.52214
2	100	37.83	20.27	7.98283e+06	6.90216
3	100	37.83	18.21	3.13511e+07	7.49625
4	100	37.83	18.05	3.48959e+07	7.54277
5	100	37.83	15.53	1.92109e+08	8.28355

Fine details

If you use conda: This package is currently only available from PyPI; however, using pip with conda is not really recommended, and conda skeleton requires a setup.py (the deprecated way of doing things). This recipe for installing in a conda environment seems to work:

conda install -c conda-forge grayskull
grayskull pypi ct2vl
cd ct2vl
conda build .
cd ..
conda install --use-local ct2vl

That being said, if you have suggestions or feedback on how to support conda installation, let us know.