pearson’s chi-square test matlab’s function crosstab (MathWorks Inc)

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MathWorks Inc pearson’s chi-square test matlab’s function crosstab

pearson’s chi-square test matlab’s function crosstab - by Bioz Stars, 2026-10

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1) Product Images from "Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex"

Article Title: Distribution of spine classes shows intra-neuronal dendritic heterogeneity in mouse cortex

Journal: Neurophotonics

doi: 10.1117/1.NPh.12.1.015001

Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells. " title="... class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition ..." property="contentUrl" width="100%" height="100%"/>
Figure Legend Snippet: Spine classes are inhomogeneously distributed on the dendrites. (a) Spines were categorized into four classes by hierarchical clustering of shape and length: upper panels—individual diameter profiles (gray) and average profile of each class (green). The total lengths of the profiles were scaled to the average spine length of the respective class; lower panels—representative spine examples. Scale bars, 1 μ m . (b) Relative abundance of each spine class on the dendrites, the distribution differs among dendrites. Numbers 1 to 4 refer to the four neurons, letters to the dendrites (see Fig. 1 ). Color code as in panel (a). (c) Pie charts showing which fraction of a specific spine class is found on each dendrite. (d) Pairwise Pearson’s chi-square tests confirm significant differences among the repartition of the classes on the dendrites within three of the four neurons. p values color coded: p ≤ 0.05 red, p ≤ 0.01 yellow, p ≤ 0.001 green, and p > 0.05 blue. All tests have been corrected for multiple comparisons. (e) Pairwise Pearson’s chi-square tests for all dendrites of all cells.

Techniques Used:

other:

Article Title: Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim
Article Snippet: Critical value is obtained according to a chi-square distribution with the function ncx2inv with MATLAB.

Article Title: Hippocampal place cells encode global location but not connectivity in a complex space
Article Snippet: If these were equally represented we would expect 50% of this total to fall around each door type in each session, which we tested using a chi-square test of equal proportions (custom MATLAB function, p-values were corrected across sessions using the Holm-Bonferroni method).

Article Title: Spatially resolved epigenomic profiling of single cells in complex tissues.
Article Snippet: The significance of the enrichment was calculated using a chi-square test using Chi2test function in MATLAB (Version 2021a).

Article Title: Columnar processing of border ownership in primate visual cortex
Article Snippet: The independence between the fractions of, respectively, orientation-selective and border ownership-selective units ( ) was assessed using a Chi-square test (MATLAB function crosstab ).

Article Title: Multiple Temporal and Object-Based Strategies Across Learning for a Selective Detection Task in Mice
Article Snippet: Nonparametric (interquintile of maximum change in trajectory) data were analyzed using the chi-square (χ 2 ) Kruskal-Wallis rank sum test (MATLAB function kruskalwallis ) across all measures for all mice, male mice, and female mice.

Clinical Proteomics:

Article Title: Relationship of plasma biomarkers to digital cognitive tests in Alzheimer's disease.
Article Snippet: 2.8.3 Machine learning for group classification Additionally, machine learning was applied to predict group classification and plasma biomarkers levels, first using MATLAB-based algorithms for feature ranking, to estimate the absolute contribution of each variable. .. The fscchi2 function in MATLAB (univariate feature ranking for classification using chi-square tests) was used to predict group classification, while the fsrftest function (univariate feature ranking for regression using F tests) was used to predict continuous variables, that is, plasma biomarker levels. ..

Article Title: Relationship of plasma biomarkers to digital cognitive tests in Alzheimer's disease
Article Snippet: Additionally, machine learning was applied to predict group classification and plasma biomarkers levels, first using MATLAB‐based algorithms for feature ranking, to estimate the absolute contribution of each variable. .. The fscchi2 function in MATLAB (univariate feature ranking for classification using chi‐square tests) was used to predict group classification, while the fsrftest function (univariate feature ranking for regression using F tests) was used to predict continuous variables, that is, plasma biomarker levels. ..

Biomarker Discovery:

Transformation Assay:

Article Title: Autonomous residual monitoring of metallurgical digital twins
Article Snippet: .. The values were transformed into the upper tail probabilities of chi-square distribution by using the chi2cdf function in MATLAB®. ..

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