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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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