Real-time PCR data were analyzed using the SPSS Statistics package 24.0 (SPSS Inc., Chicago, IL), as described previously (Wang, Diaz-Rosales, et al. 2011 (link)). A paired samples T test was applied to the expression levels between SOCS paralogue pairs (supplementary fig. S30 , Supplementary Material online). One way-analysis of variance (ANOVA) and the LSD post hoc test was used to analyze the ontogeny of expression data in figure 8 , with P ≤ 0.05 between groups considered significant.
Spss statistics package 24
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SPSS Statistics package 24.0 is a comprehensive software suite for statistical analysis. It provides a wide range of analytical tools and techniques for data management, analysis, and visualization. The core function of SPSS Statistics is to enable users to conduct statistical analyses, generate reports, and create graphical representations of their data.
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Lab products found in correlation
9 protocols using spss statistics package 24
1
Real-Time PCR Data Analysis
2
Analysis of Real-time PCR and Flow Cytometry Data
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The data were analysed statistically using the SPSS Statistics package 24.0 (SPSS Inc., Chicago, Illinois). Real-time PCR data were scaled and log 2 transformed before statistical analysis, as described previously (Wang et al., 2011 (link)). Flow cytometry data were converted to the percentage of IL-22+ cells, then ARCSINE transformed for statistical analysis using either a paired-sample T-test or independent-sample T-test, with P ≤ 0.05 considered significant.
3
Nerve Regeneration Metrics Analysis
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Data are presented as mean ± standard error of the mean (SEM). Differences in axon counts, regeneration distance, and transfection efficiency were analyzed with the Mann–Whitney U test using SPSS Statistics package 24.0 (SPSS, IBM, Chicago, IL). A p value of less than 0.05 was considered statistically significant.
4
Comparative statistical analysis of PCR and OD
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The data were analysed statistically using the SPSS Statistics package 24.0 (SPSS Inc., Chicago, Illinois). Real-time PCR data were scaled, log2 transformed and used for statistical analysis as described previously (Wang et al., 2011a) . The OD450 and percentage of cells were directly used for statistical analysis using a paired-sample T-test, with P <0.05 between groups considered significant.
5
Quantitative Gene Expression Analysis
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Real-time PCR data were analysed using the SPSS Statistics package 24.0 (SPSS Inc., Chicago, Illinois) as described previously (Wang et al., 2011a) . One way-analysis of variance (ANOVA) and the LSD post hoc test were used to analyze expression data in Figs. 8-10, with P <0.05 between treatment and control groups considered significant. Since tissue expression consisted of sample sets from six individual fish, a Paired-Sample T-test was applied (Fig. 6).
6
Quantitative PCR Data Analysis Protocol
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The data were statistically analyzed using the SPSS Statistics package 24 (SPSS Inc., Chicago, Illinois). The analysis of real-time PCR data was as described previously (47 (link), 48 (link)). To improve the normality of data, real-time quantitative PCR measurements were scaled, with the lowest expression level in a data set defined as 1, and log2 transformed. One way-analysis of variance (ANOVA) and the Bonferroni post-hoc test were used to analyse the gene expression data, with P < 0.05 between treatment and control groups considered significant. Genes significantly modulated by YRF in vivo were selected and their fold changes were log2 transformed and subjected to hierarchical clustering analysis using the Morpheus program at https://software.broadinstitute.org/morpheus .
7
In vivo gene expression analysis
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Relative in vivo gene expression values were normalized against EF-1α, scaled and log2 transformed prior to statistical analysis, as described previously [34 (link)]. The effects of selenium and/or infection in vivo were determined by 2-way ANOVA in the SPSS Statistics package 24 (SPSS Inc., Chicago, Illinois). The in vitro expression data was normally distributed, as tested by Levene´s test, and analysed directly by independent student´s t-tests on untransformed expression data. Significance levels were set at p≤0.05.
8
Analyzing Gene Expression Using SPSS
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The data were analyzed statistically using the SPSS Statistics package 24 (SPSS Inc., Chicago, IL, USA). The real-time PCR data were scaled and log2 transformed before statistical analysis as described previously (37 (link)). One-way analysis of variance and the LSD post hoc test were used to analyze expression data in MLR, with p ≤ 0.05 between mixed and control groups considered significant. For the tissue distribution of expression and other in vitro experiments that consisted of sample sets from individual fish, a paired samples T-test was applied.
9
Real-Time PCR Data Analysis
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The data were statistically analyzed using the SPSS Statistics package 24 (SPSS Inc., Chicago, Illinois). The analysis of real-time PCR data was as described previously (43) . To improve the normality of data, real-time
quantitative PCR measurements were scaled, with the lowest expression level in a data set defined as 1, and log2 transformed. One way-analysis of variance (ANOVA) and the LSD post hoc test were used to analyse the gene expression data, with P ≤ 0.05 between treatment and control groups considered significant.
quantitative PCR measurements were scaled, with the lowest expression level in a data set defined as 1, and log2 transformed. One way-analysis of variance (ANOVA) and the LSD post hoc test were used to analyse the gene expression data, with P ≤ 0.05 between treatment and control groups considered significant.
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