Pingouin: statistics in Python

Vallat, Raphaël

doi:10.21105/joss.01026

Cited by 1,243 publications

(812 citation statements)

References 6 publications

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“…For general statistical analysis, SciPy (Jones et al, 2001) was used. Partial correlations were estimated using Pingouin (Vallat, 2018). Plots were generated using matplotlib (Hunter, 2007) and seaborn (Waskom et al, 2018) while figures were generated using the Python package rohan (Dandage, 2019).…”

Section: Data Analysis and Visualizationmentioning

confidence: 99%

Paralog dependency indirectly affects the robustness of human cells

Dandage

Landry

2019

Molecular Systems Biology

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The protective redundancy of paralogous genes partly relies on the fact that they carry their functions independently. However, a significant fraction of paralogous proteins may form functionally dependent pairs, for instance, through heteromerization. As a consequence, one could expect these heteromeric paralogs to be less protective against deleterious mutations. To test this hypothesis, we examined the robustness landscape of gene loss‐of‐function by CRISPR‐Cas9 in more than 450 human cell lines. This landscape shows regions of greater deleteriousness to gene inactivation as a function of key paralog properties. Heteromeric paralogs are more likely to occupy such regions owing to their high expression and large number of protein–protein interaction partners. Further investigation revealed that heteromers may also be under stricter dosage balance, which may also contribute to the higher deleteriousness upon gene inactivation. Finally, we suggest that physical dependency may contribute to the deleteriousness upon loss‐of‐function as revealed by the correlation between the strength of interactions between paralogs and their higher deleteriousness upon loss of function.

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Section: Data Analysis and Visualizationmentioning

confidence: 99%

Paralog dependency indirectly affects the robustness of human cells

Dandage

Landry

2019

Molecular Systems Biology

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“…The condition order was counter-balanced: participants 1 and 2L first performed the task with action and then Table 2: Offline performance Abbreviations: P, participant; R 2 , coefficient of determination (multi-output regression); F1, macro-average F1-score (multi-label classification) P R 2 F1 1 0.51 0.82 2R 0.38 0.74 2L 0.40 0.78 3 0.51 0.82 position control, and participants 2R and 3 with the reversed order. We performed all statistical analyses in Python using the Pingouin library [27].…”

Section: Discussionmentioning

confidence: 99%

Discrete action control for prosthetic digits

Krasoulis

Nazarpour

2020

Preprint

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Objective We aim to develop a paradigm for simultaneous and independent control of multiple degrees of freedom (DOFs) for upper-limb prostheses. Approach We introduce action control, a novel method to operate prosthetic digits with surface electromyography (EMG) based on multi-label, multi-class classification. At each time step, the decoder classifies movement intent for each controllable DOF into one of three categories: open, close, or stall (i.e., no movement). We implemented a real-time myoelectric control system using this method and evaluated it by running experiments with one unilateral and two bilateral amputees. Participants controlled a six-DOF bar interface on a computer display, with each DOF corresponding to a motor function available in multi-articulated prostheses. Main results We show that action control can significantly and systematically outperform the state-of-the-art method of position control via multi-output regression in both task-and non-task-related measures. Improvements in median task performance ranged from 20.14% to 62.32% for individual participants. Analysis of a post-experimental survey revealed that all participants rated action higher than position control in a series of qualitative questions and expressed an overall preference for the former. Significance Action control has the potential to improve the dexterity of upper-limb prostheses. In comparison with regression-based systems, it only requires discrete instead of real-valued ground truth labels, typically collected with motion tracking systems. This feature makes the system both practical in a clinical setting and also suitable for bilateral amputation. This work is the first demonstration of myoelectric digit control in bilateral upper-limb amputees. Further investigation and pre-clinical evaluation are required to assess the translational potential of the method.Discrete action control for prosthetic digits

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“…To account for multiple comparisons, we used the Holm-Bonferroni correction method. Statistical analysis was performed in in Python using the Pingouin library 49 .…”

Section: Discussionmentioning

confidence: 99%

Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

Krasoulis

Nazarpour

2020

Preprint

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The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Although such methods have produced highly-accurate results in offline analyses, their success in real-time prosthesis control settings has been rather limited. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent decoding based on multi-label, multi-class classification. At each moment in time, our algorithm classifies movement action for each available DOF into one of three categories: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.3/11 5/11

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Pingouin: statistics in Python

Abstract: LicenseAuthors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY).

Cited by 1,243 publications

References 6 publications

Paralog dependency indirectly affects the robustness of human cells

Paralog dependency indirectly affects the robustness of human cells

Discrete action control for prosthetic digits

Myoelectric digit action decoding with multi-label, multi-class classification: an offline analysis

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