Statistical analyses were conducted using JMP software from SAS (Statistical Analysis System, Institute, Cary, NC, USA). Two-tailed t-test, chi-square test and exact test were used to compare, respectively, continuous and categorical demographic and clinical characteristics between subjects with MDD and controls. To account for the unequal sample size and variance of the MDD and HC groups, we performed statistical analyses, including Welch’s t-Test as needed. Within-group Pearson correlations were conducted to examine the relationship between in-vivo exosomal molecular measures and severity of depressive symptoms. Multiple regression analysis was used to control for other clinical characteristics.
Jmp software
Manufactured by SAS Institute
Sourced in United States, Japan, Cameroon, Germany, United Kingdom, Belgium
JMP software is a data analysis and visualization tool developed by SAS Institute. It provides users with interactive and intuitive tools for exploring, analyzing, and presenting data. JMP software offers a range of features for data management, statistical analysis, and visual representation.
Automatically generated - may contain errors
Lab products found in correlation
Prism software, by GraphPad (9 mentions)
Prism 6, by GraphPad (8 mentions)
Expert statview, by SAS Institute (6 mentions)
Prism 5, by GraphPad (5 mentions)
Prism 9, by GraphPad (4 mentions)
Sas 9, by SAS Institute (4 mentions)
Iq5 real time pcr machine, by Bio-Rad (3 mentions)
Superscript 3, by Thermo Fisher Scientific (3 mentions)
Iq sybr green supermix, by Bio-Rad (3 mentions)
R software, by R Project for Statistical Computing (3 mentions)
Prism 8, by GraphPad (3 mentions)
Spss statistic, by IBM (3 mentions)
Prism 7, by GraphPad (3 mentions)
Statview, by Abacus (3 mentions)
Prism software v7, by GraphPad (2 mentions)
Circwave software, by SAS Institute (2 mentions)
Spss statistics software, by IBM (2 mentions)
Sas software, by SAS Institute (2 mentions)
Winrhizo software, by Regent Instruments (1 mentions)
Quick taq hs dyemix, by Toyobo (1 mentions)
1 534 protocols using jmp software
1
Exosomal Biomarkers for Major Depressive Disorder
2
Exosomal Biomarkers for Major Depressive Disorder
3
Evaluating Classifier Performance with ROC
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Receiver operator characteristic (ROC) curve was calculated using SPSS Statistics 17.0 and JMP software (SAS Institute, Cary, NC, USA). ROC plots graphically illustrate the performance of binary classifier system, that is, a parameter that distinguished between two groups of samples, like normal/tumor or prostate cancer/non-prostate cancer. The area under the curve (AUC) is a measure of how well the parameter distinguish between the two groups, with AUC values near 1 indicating good discriminating power of the variable and values near 0 no or limited power. T-test to obtain metagenes was performed with MultiExperiment Viewer (MeV). Times series analysis and DFA were done using JMP software (SAS Institute, Cary, NC, USA). GenePattern webserver was used to obtain ssGSEA scores for custom metagenes, as well as standard GSEA.
4
Clustering Genotypes and Phenotypes in Malaria
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For clustering multiple-SNP average genotype, a per-chromosome average genotype was first calculated by encoding the SNPs into a numeric scale (0 = YM genotype, 2 = N67 genotype, 1 = missing genotype call). A matrix consisting of the mean genotypes, 14 chromosomes by 45 parasites, was clustered using Ward’s method in JMP software (SAS Institute, Cary, NC). The cluster order of parasites was extracted and used to organize the heatmap of average genotype.
For clustering parasites based on phenotype (parasitemia and mortality), a matrix of daily parasitemia levels for 43 progenies and two parental strains for 25 days was used as the basis for ordering the parasites according to similarities in time course of parasiteima. Missing values (post-mortem time points) were imputed with final measured parasitemia level before clustering using Ward’s method in JMP software (SAS Institute). A sensored version of the parasitemia heatmap was created using the order of parasites generated in the clustering.
For clustering parasites based on phenotype (parasitemia and mortality), a matrix of daily parasitemia levels for 43 progenies and two parental strains for 25 days was used as the basis for ordering the parasites according to similarities in time course of parasiteima. Missing values (post-mortem time points) were imputed with final measured parasitemia level before clustering using Ward’s method in JMP software (SAS Institute). A sensored version of the parasitemia heatmap was created using the order of parasites generated in the clustering.
5
Pasture and Animal Performance Analysis via ANOVA
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Pasture analyses were performed using the one‐way ANOVA using the procedure of the JMP software (SAS Institute, NC, USA, 2000) according to this model: where Yij = mean of response variable, µ = population mean, Ti = effect of sampling time (i = 5; April, May, June, July, August) and εi = experimental error.
Body weight, milk and blood analyses were performed using the two‐way ANOVA for repeated measures over time using the procedure of the JMP software (SAS Institute, NC, USA, 2000) according to this model: where Yijk = mean of response variable, µ = population mean, Gi = effect of the group (i = 2; CTR and ECO), Tj = effect of sampling time (j = 5; April, May, June, July, August), (G × T) ijk = fixed effect of interaction between dietary treatment and sampling time, and εijk = experimental error.
The comparison among the mean values was performed by using Tukey's test. Differences were considered statistically significant at p < 0.05.
Body weight, milk and blood analyses were performed using the two‐way ANOVA for repeated measures over time using the procedure of the JMP software (SAS Institute, NC, USA, 2000) according to this model: where Yijk = mean of response variable, µ = population mean, Gi = effect of the group (i = 2; CTR and ECO), Tj = effect of sampling time (j = 5; April, May, June, July, August), (G × T) ijk = fixed effect of interaction between dietary treatment and sampling time, and εijk = experimental error.
The comparison among the mean values was performed by using Tukey's test. Differences were considered statistically significant at p < 0.05.
6
Statistical Analysis of Experimental Data
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Data are reported as Mean ± SEM or as percentage of totals for distributions. Comparisons were made using one-tailed ANOVA tests followed by Tukey’s HSD test for normal distributions in JMP software (SAS, Cary, NC, USA), or Student’s t-tests in Igor Pro (Wavemetrics, Portland, OR, USA). For non-normal distributions, we used Wilcoxon/Kruskal–Wallis multiple comparison tests followed by the Benjamini–Hochberg procedure using a false discovery rate (FDR) of 0.05 in JMP software (SAS, Cary, NC, USA) or a Mann–Whitney U test in Igor Pro. Multiple-comparison corrections were performed with Excel scripts. P-values of <0.05 were considered significant.
7
Dose-response and Biological Activity of Ligand-Hecate
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The dose-response data on Ligand-Hecate and Hecate against E. coli, P. termitis, and K. lactis was subjected to probit analysis and the values obtained for mean lethal dose (LD50) were compared within each microorganism using t-test (JMP software, SAS Institute). The data on: (1) the number alive protozoa T. pyriformis in biological activity assay using the culture supernatants of yeast strains, (2) the number of yeast cells (CFU) per termite gut in yeast feeding assays, and (3) diet consumption in yeast feeding assay were analyzed using analysis of variance. Then, Tukey's honestly significant difference (HSD) test with a significance level of α = 0.05 was used for post hoc means separation (JMP software, SAS Institute).
8
ANOVA Analysis of Experimental Results
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Statistical tests were performed by one-way ANOVA using JMP Software (version 16, SAS Institute, NC, USA). Unless otherwise stated, p < 0.05 was taken as the significance threshold.
9
Statistical Analysis of Allele Associations
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The chi-square test or Fisher's exact test was used to assess baseline differences between binary variables. The nonparametric Mann–Whitney U test was adopted for statistical analysis of the associations of total alleles in plasma with clinicopathological factors. Differences were considered significant when a value of P < .05 was obtained. All statistical analyses were two-sided and were performed using JMP software version 10.0.1 for Windows (SAS Institute Japan, Tokyo, Japan).
10
Metabolic Profiling and Root Traits Analysis
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To test for metabolite differences between treatments, metabolite abundances were Hellinger‐transformed (Ramette, 2007 ) and principal component analysis (PCA) was performed with PC‐ORD v6.08 (McCune & Mefford, 1999 ). Data on plant biomass, root system architecture (primary seminal root length, total root length, root surface area), ion content, lipid and metabolite abundance were subjected to statistical analysis by one‐way ANOVA, using JMP software (SAS institute Inc., Cary, NC). Significance was defined as a probability level of the student's t test at p ≤ .05. Total root length and root surface area were performed using WinRHIZO software (Arsenault, Poulcur, Messier, & Guay, 1995 ) based on scanned root images using standard parameters (Regent Instruments Inc., Ontario, CA).
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