Marker genes (or DEGs between two groups) were identified using the Seurat FindAllMarkers (or FindMarkers) function, and P values were calculated with the Wilcoxon rank sum test. For pathway enrichment analysis, P values were computed with the hypergeometric test and adjusted in a Benjamini‒Hochberg procedure for multiple hypothesis correction. The data from the pathology verification and RT‒qPCR assays are presented as the means ± standard deviations (SDs) and were analyzed by Student’s t test or one-way ANOVA (>2 groups) for P value determination. Significance is indicated by *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. All statistical analyses were performed with R (https://www.r-project.org/ ), python (python.org/">https://www.python.org/ ) or PRISM (GraphPad Software Inc).
Python
Manufactured by GraphPad
Sourced in United States
Python is a high-level programming language designed for general-purpose programming. It emphasizes code readability and supports multiple programming paradigms, including object-oriented, procedural, and functional programming. Python is commonly used for data analysis, scientific computing, web development, and automation tasks.
Automatically generated - may contain errors
Lab products found in correlation
Axograph, by Wavemetrics (2 mentions)
Igor pro v6.3 or v8, by Wavemetrics (2 mentions)
Affinity designer, by Adobe (2 mentions)
Axograph, by GraphPad (2 mentions)
Igor pro, by Adobe (2 mentions)
Igor pro, by GraphPad (2 mentions)
Microsoft excel, by GraphPad (2 mentions)
Pclamp 10, by Molecular Devices (2 mentions)
Prism 9, by GraphPad (1 mentions)
Prism 10, by GraphPad (1 mentions)
Matlab, by GraphPad (1 mentions)
Prism 5, by GraphPad (1 mentions)
13 protocols using python
1
Statistical Analysis of Differentially Expressed Genes
2
Quantitative Analysis of Stem Cell Organoids
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Graphs were generated and statistical analysis was performed on R Studio, Python, and Prism (version 8, GraphPad). Samples were tested for normality before analysis. Analysis of RT‐PCR (Fig 1B ) results for the different groups was performed using one‐way ANOVA and post hoc Tukey’s comparison of means. For quantification of marker co‐expression of DLX‐i56 GFP+ cells in dorsal and ventral regions (Fig 1G ), an unpaired two‐tailed Student’s t‐test was performed. For quantification of rosette sizes of the organoids grown from the different protocols (Fig EV1E ), statistical analysis was performed using one‐way ANOVA and post hoc Tukey’s comparison of means. For analysis for the 48 parameters of the tracking analysis (Figs 5C and EV4 ), first a Kruskal–Wallis test was performed to determine the variance between the different groups. Then, using non‐parametric Mann−Whitney U‐test with Bonferroni correction to correct for type 1 errors, statistical significances were examined. All results are noted in the corresponding figures or their legends. No statistical methods were used to pre‐determine sample size, which was estimated based on previous experience with similar setups. Experiments were not randomized. Investigators were blinded for immunohistochemistry analysis and not blinded for live‐imaging analysis.
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.
4
Statistical Analyses of Experimental Data
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Shapiro-Wilk test was applied to normality test. The Mann-Whitney test was used to compare two groups of data that did not subject to normal distribution and Student’s t-test was employed to compare two groups of data that did. And the ANOVA test was employed to conduct multiple comparisons. The log-rank test was used to determine the overall survival outcome, and the Spearman test was used to analyze the correlations between two sets of data. Analyses were conducted using R (version 4.1.3), Python (version 3.8.8), and GraphPad Prism (version 8.3.0).
5
Multi-Omics Data Integration Pipeline
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All analysis and visualizations were processed with R (Version 4.1.2 for scRNA-Seq and Version 4.2.1 for CyTOF and Bulk RNA-Seq), Python (Version 3.10.9) and GraphPad (Version 9.3). Wilcoxon rank sum tests were employed for continuous variables between two groups; Chi-Square tests were performed for the comparisons of categorical variables. Correlation analyses (Pearson) were based on Single-Cell Variational Inference (scVI-tools, Version 0.20.2) imputed expression data to avoid sparsity influence [58 (link)]. Data visualizations were performed with ggplot2 (Version 3.4.1), genekitr (Version 1.0.5), ggVennDiagram (Version 1.2.2) and ComplexHeatmap (Version 2.10.0) R packages.
6
Statistical Analysis Reporting Guidelines
7
Comprehensive Breast Cancer Imaging Analysis
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The Kolmogorov–Smirnov test was initially used to analyze for normal distributions of variables and then the Levene test to examine the homogeneity of variance. Wilcoxon signed-rank test and χ2 test were used to evaluate continuous and categorical variables, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performances of the imaging parameters for diagnosing BC and predicting histopathological findings, with excellent diagnostic ability defined as an area under the ROC curve (AUC) >0.8. Spearman correlation analysis was used to evaluate the associations between the imaging parameters and the BC prognostic factors and molecular subtypes. Correlations were classified based on the correlation coefficient as excellent (≥0.75), moderate to good (0.50–0.74), fair (0.25–0.49), and small (≤0.24).
All statistical analyses were performed using IBM SPSS (version 25; IBM Corp., Armonk, NY), MedCalc (version 15.6.1; Ostend, Belgium), GraphPad Prism (version 7, GraphPad, USA), and python (version 3.8.8). Differences were considered statistically significant at p-values <0.05.
All statistical analyses were performed using IBM SPSS (version 25; IBM Corp., Armonk, NY), MedCalc (version 15.6.1; Ostend, Belgium), GraphPad Prism (version 7, GraphPad, USA), and python (version 3.8.8). Differences were considered statistically significant at p-values <0.05.
8
Electrophysiology Data Analysis Protocols
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Electrophysiological data were analyzed using pClamp 10.4 software (Molecular Devices), Axograph, or IGOR Pro v6.3 or v8 (WaveMetrics, Lake Oswego, OR) 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 9 (GraphPad, San Diego, CA). Student’s t-test and ANOVA were used to compare the means when datasets were normally distributed. Otherwise, non-parametric tests were employed. Error bars are represented as mean ± SEM unless otherwise stated.
9
Single-Cell and Bulk RNA-Seq Analysis Workflow
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All analysis and visualizations were processed with R (Version 4.1.2 for scRNA-Seq and Version 4.2.1 for CyTOF and Bulk RNA-Seq), Python (Version 3.10.9) and GraphPad (Version 9.3). Wilcoxon rank sum tests were employed for continuous variables between two groups; Chi-Square tests were performed for the comparisons of categorical variables. Correlation analyses (Pearson) were based on Single-Cell Variational Inference (scVI-tools, Version 0.20.2) imputed expression data to avoid sparsity influence59 (link). Data visualizations were performed with ggplot2 (Version 3.4.1), genekitr (Version 1.0.5), ggVennDiagram (Version 1.2.2) and ComplexHeatmap (Version 2.10.0) R packages.
10
Statistical Analyses of Biological Assays
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Statistical analyses for immunofluorescence images, immunoblots, and behavioral assays were performed in GraphPad Prism10 (RRID:SCR_002798). No power analyses were performed to predetermine the sample sizes, but our sample sizes are comparable to those previously reported 14 (link) . For histological analyses, at least three age- and gender-matched littermates were selected per genotype. If there were fewer than three pairs, mice from another litter with similar birth date were added for statistical analyses. For electrophysiological analyses, traces from 8 optic nerves per genotype were analyzed.
The proper statistical tests were carefully selected based on normality and consideration of multiple comparisons. Data were analyzed using Microsoft Excel, python, and GraphPad Prism. The following tests were employed where appropriate: unpaired Student’s t-test, Mann-Whitney U test, one-way ANOVA, two-way ANOVA with multiple comparisons, Chi-square test, and Kolmogorov–Smirnov test. All error bars are ± SEM unless otherwise indicated. Each data point represents an individual animal unless otherwise stated.
The proper statistical tests were carefully selected based on normality and consideration of multiple comparisons. Data were analyzed using Microsoft Excel, python, and GraphPad Prism. The following tests were employed where appropriate: unpaired Student’s t-test, Mann-Whitney U test, one-way ANOVA, two-way ANOVA with multiple comparisons, Chi-square test, and Kolmogorov–Smirnov test. All error bars are ± SEM unless otherwise indicated. Each data point represents an individual animal unless otherwise stated.
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