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Table 1 Comparison between CellPD, cellGrowth, and Excel

From: Quantifying differences in cell line population dynamics using CellPD

 

CellPD

cellGrowth

Spreadsheet (Excel)

0.25 h sampling rate (95 samples) max_growth_rate (±SEM) h-1

0.375 (±0.00963)

0.3971 (±0.0045)

0.3751 (±0.0045)

3 h sampling rate (7 samples) max_growth_rate (±SEM) h-1

0.438 (±0.0326)

0.1916 (±0.0045)

0.3044 (±0.0096)

6 h sampling rate (3 samples) max_growth_rate (±SEM) h-1

0.462 (N/A)

Breaks down

0.2519 (±0.0435)

Usability benchmark: Tt otal, lead author Usability benchmark: Ttotal, lead author

6 m 55 s

6 m 35 s

2 m 27 s

Usability benchmark: Tanalysis, lead author Usability benchmark: Tanalysis, lead author

5 m 43 s

3 m 17 s

2 m 27 s

Usability benchmark: Ttotal, 12 scientists unfamiliar with CellPD

Approximate range, in minutes [20, 30]

N/A

N/A

Usability benchmark: Tanalysis, 12 scientists unfamiliar with CellPD

Approximate range, in minutes [14, 26]

N/A

N/A

Run time

~30 s

<1 s

<1 s

Video of timed use case

https://youtu.be/3xR9x_2pBKs

https://youtu.be/DO-LkVVglIg

https://youtu.be/YCyCfzl7yFY

Comments on ease of use

Tutorial available, drag and drop option

Good tutorial to use custom data

Present on (viritually) every computer, many tutorials available online

Comments on input user interface

Executable file, command line option

Command-line in R

Manual input of formulas within the GUI

Comments on output user interface

Easy to read webpages with downloadable plots

Option to display and save an informative plot

Easy to create simple graphs

Typical UI

https://youtu.be/3xR9x_2pBKs?t=276

https://youtu.be/DO-LkVVglIg?t=272

https://youtu.be/YCyCfzl7yFY?t=12

Typical output

https://youtu.be/3xR9x_2pBKs?t=406

https://youtu.be/DO-LkVVglIg?t=391

https://youtu.be/YCyCfzl7yFY?t=158

Feature comparison matrix:

Uncertainty quantification

Yes

If user computes it

If user computes it

Parametric growth models

Yes

Yes

If user creates them

Nonparametric growth models

No

Yes

If user creates them

Publication quality graphs

Yes

No

No

Fully annotated results in a standardized markup language

Yes

No

No

Open Source

Yes

Yes

No

Language written

Python

R

C/C++, C++/Java/Python

Required software to run

Spreadsheet editor (Excel, LibreOffice), Web browser (internet Explorer will suffice)

R

Excel, LibreOffice

Required computational expertise

No specialized experience

Working knowledge of R

Familiarity with spreadsheets

  1. All three tools correctly estimate the growth rate when provided with a large number of samples. cellGrowth is more precise than CellPD for higher number of samples (i.e., shorter sampling intervals). However, even with fewer samples (i.e., larger sampling intervals), CellPD correctly estimates the growth rate (within the 95 % confidence interval). For fewer samples (i.e., larger sampling intervals), both cellGrowth and Excel become unreliable. CellPD is slower than cellGrowth or Excel for an experienced user, but CellPD does not require prior programming knowledge (unlike cellGrowth) and it also creates multiple useful outputs (Excel does not generate publication-quality graphs and cellGrowth has the option of creating a single graph which the user can export). CellPD is quicker to set up than cellGrowth, but it takes longer to run in order to create the multiple outputs. Excel usually requires no set up (beyond installing Microsoft Office), and it is often already installed in a research computer. The lead author computed the usability benchmark running a fixed, “clean” Windows 7 configuration on a Virtual Machine (VM). This VM included an installation of LibreOffice 5.1.4 and was run in a Lenovo ThinkPad Yoga with an Intel Core i7-4600U CPU with 8GB of Ram running Windows 10 (64-bit)
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BMC Systems Biology

ISSN: 1752-0509

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