Spss statistics 2017

Data were analysed using a statistics package (IBM SPSS Statistics 2017, Armonk, NY, USA). Data were described using means, standard deviations and/or confidence intervals. Graphs and linear regression plots were produced using spreadsheet software (Excel 2017, Microsoft, Redmond, WA, USA). Statistical differences between groups were analysed using ANOVA and Tukey's post hoc testing. All statistical tests were completed with a 95 % confidence interval. Intra-class Correlation Coefficient (ICC) was used to measure agreement between CLSM and CP methods. A Pearson Correlation test was also used to measure if there was a linear relationship between CLSM and CP step height measures. Both ICC and Pearson correlation are estimates of the magnitude of relationships between variables i.e. step height differences. However, ICC also includes measures of the reproducibility or reliability between CLSM and CP measures.

Scatterplots were drawn by SigmaPlot 12.5. Significant analysis was performed by IBM SPSS Statistics 2017 and Microsoft Excel 2016 software, and the difference between the two groups was determined by two-tailed t test. Partial least square discriminant analysis (PLS-DA) and random forest model analysis were performed at https://www.omicstudio.cn/tool using the OmicStudio tools. Heat map correlation analysis is performed on https://hiplot.com.cn/cloud-tool/drawing-tool/list. Pearson correlation was used to analyze the correlation between metabolites and chromaticity values in IBM SPSS Statistics 2017, and p < 0.05 was considered as significant.