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Igor pro

Manufactured by GraphPad

Igor Pro is a powerful data analysis and graphing software designed for scientific data analysis. It provides a range of tools and features for visualizing, analyzing, and interpreting scientific data. Igor Pro is widely used in various scientific fields, including physics, chemistry, biology, and engineering.

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17 protocols using igor pro

1

Statistical Analysis of Experimental Data

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We completed analysis using IgorPro and Prism (Graphpad). We evaluated values for normality with a Shapiro-Wilk test, which determined the use of either a parametric (i.e., Student’s) or non-parametric (i.e., Mann–Whitney) tests to calculate statistical significance between groups. One-way or two-way ANOVA, with a Bonferroni post hoct tests were also implemented, where appropriate. Specific tests for each comparison are described in the figure legends. We also evaluated for outliers (ROUT and Grubbs’ test) before statistical evaluation. All data are reported as mean ± SEM. Statistical significance was taken at P < 0.05.
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2

Robust Statistical Analysis of Experimental Data

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Analysis was conducted in Igor Pro and Prism 8 (GraphPad). Data in text are reported as mean ( x- ) ± SEM for parametric or median ( x~ ) for non-parametric data. Error bars on graphs are indicated as ± SEM. Box plots show medians, 25 and 75% (boxes) percentiles, and 10 and 90% (whiskers) percentiles. For parametric data, t-tests were used for two-group comparison, and ANOVA tests were used for more than two group comparisons, followed by a Bonferonni or Šidák post-hoc test for analysis of multiple comparisons. For non-parametric data sets, Mann-Whitney U tests were used to compare two groups while the Kruskal-Wallis test was used to compare more than two groups. For linear regression analysis, the Straight Line analysis function was used in Prism, and an extra sum-of-squares F test was performed to determine significant differences in slope between data sets on the same plot, and to determine whether a line or exponential decay model fits the data better. For exponential fits, the One Phase Decay analysis function in Prism was used to fit a standard curve.
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3

Electrophysiological Data Analysis Techniques

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Electrophysiological data were analyzed using pClamp 10.4 software (Molecular Devices), Axograph, or IGOR Pro v6.3 or v8 (WaveMetrics) and NeuroMatic (Rothman and Silver, 2018 (link)). Figures were made using IGOR Pro, Affinity Designer, and Adobe Illustrator. Statistics were performed in IGOR Pro, Axograph, Python, Microsoft Excel, or Prism (GraphPad). For statistical analysis, groups were compared with paired or unpaired t-test. Cluster analysis was performed using sklearn.cluster.KMeans in Python and figures were made using matplotlib.pyplot. Error bars are represented as mean ± SEM unless otherwise stated.
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4

Statistical Analysis of Seahorse and Animal Studies

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Statistical significance was determined by two-tailed unpaired Student’s t test and two-tailed heteroscedastic Student’s t test for Seahorse assay. One-sided t test was used for animal studies, in addition to log-rank test for the Kaplan-Meier plots. GraphPad Prism and Igor Pro were used for calculations. Differences were considered to be statistically significant if P < .05.
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5

AML Cell Combination Therapy Analysis

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Statistics were performed with Cell Quest Pro software (BD Bioscience), HEKA FitMaster v2x53 and IgorPro for patch-clamp transient currents and GraphPad Prism for analysis of dose-response curves and Student’s t-test, with P* < 0.05, P** < 0.01, and P*** < 0.001. The combination index (CI) and dose reduction index (DRI) were determined with CompuSyn1.0 software using the Chou-Talalay method [46 (link)]. For primary AML cells, the coefficient of drug interaction (CDI) was calculated to assess the synergistic inhibitory effect of the drug combination (CDI = AB/(A × B)) [47 (link)].
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6

Quantifying Phase-Locked Neural Spiking

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Data were analyzed using custom software written in Igor Pro (Wavemetrics, Lake Oswego, Oregon) and were plotted with Igor Pro and Prism 5 (Graphpad, La Jolla, California). Data sets were tested for normality using Kolmogorov-Smirnov normality tests. Statistical significance between conditions was determined using paired two-tailed Student's t tests. In order to evaluate the fidelity of phase locking for evoked spiking, we converted individual spike times, with time = 0 coinciding to stimulus onset, to unit vectors with appropriate phase angle for the given stimulus and then calculated vector strength (r) [31] (link).
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7

Electrophysiology of C. elegans Neuromuscular Junctions

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Electrophysiological recordings were obtained from the C. elegans neuromuscular junctions of immobilized and dissected adult worms as previously described (Richmond, 2009 (link)). Ventral body wall muscle recordings were acquired in whole-cell voltage-clamp mode (holding potential, −60 mV) using an EPC-10 amplifier, digitized at 1 kHz. Evoked responses were obtained using a 2 ms voltage pulse applied to a stimulating electrode positioned on the ventral nerve cord anterior to the recording site. For multiple stimulations, a five pulse train was delivered at 20 Hz. The 5 mM Ca2+ extracellular solution consisted of 150 mM NaCl, 5 mM KCl, 5 mM CaCl2, 4 mM MgCl2, 10 mM glucose, 5 mM sucrose, and 15 mM HEPES (pH 7.3,~340 mOsm). The patch pipette was filled with 120 mM KCl, 20 mM KOH, 4 mM MgCl2, 5 mM (N-tris[Hydroxymethyl] methyl-2-aminoethane-sulfonic acid), 0.25 mM CaCl2, 4 mM Na2ATP, 36 mM sucrose, and 5 mM EGTA (pH 7.2,~315 mOsm). Data were obtained using Pulse software (HEKA. Subsequent analysis and graphing was performed using mini analysis (Synaptosoft), Igor Pro and Prism (GraphPad).
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8

Statistical Analysis of Experimental Data

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Experimental data were analyzed and presented using Igor Pro and Prism (GraphPad Software, San Diego, CA). For statistical significance, we tested the normality of the data distribution with the Kolmogorov–Smirnov test with the Dallal-Wilkinson-Lillie correction for corrected p values using Prism 5. If a dataset passed the normality test, we used the unpaired Student’s t-test; for all other datasets we used the non-parametric Mann-Whiney U test. For statistical significance of multiple groups, we used a two-way ANOVA. For comparing slopes of lines, we used the linear regression test. Data collected as raw values are shown as mean ± S.E.M. or mean ± S.D. Details of statistical methods are reported in the text. For all analyses, p values < 0.05 were considered significant.
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9

Biostatistical Analysis Methods

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Any statistical packages can be employed for statistical tests. We have utilized commercially available packages including GraphPad Prism (RRID:SCR_002798), Igor Pro and MATLAB. R is widely used for statistical computing software (RRID:SCR_001905) as it is freely available, and it runs on Windows, MacOS and a wide variety of Unix platforms. We have developed a practical R script library for basic biostatistics: RStatisticalTests [16 (link)]. In this library, TwoSampleTest.R is used for parametric and non-parametric comparisons between two independent groups. OneWayANOVA.R is used for parametric and non-parametric comparisons between multiple groups, followed by post-hoc comparisons.
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10

Sheep Retinal Neuron Electrophysiology

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All figures presented were analyzed and graphed in GraphPad Prism 8 or IgorPro software unless stated otherwise. Data shown as mean ± SD. Two group comparisons were carried by Student’s unpaired t test, and percentage comparison was done by χ2 test; p < 0.05 was considered significant. N = 3 sheep (two to six weeks post-OD) were used for electrophysiology and Ca2+ imaging while another cohort of N = 3 sheep were used for immunohistochemistry.
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