multivariate statistical analysis (pca, cluster analysis, heatmaps plotting, correlation analysis)  (RStudio)

Journal: Nature Communications

Article Title: Natural variation of an E3 ubiquitin ligase encoding gene Chalk9 regulates grain chalkiness in rice

doi: 10.1038/s41467-025-61683-4

Figure Lengend Snippet: a The genome-wide association signals for chalky grain rate (CGR) and degree of chalkiness (DC) in the region at 18–21 Mb on chromosome 9 ( x -axis) across two years. Negative log 10 -transformed P values from the linear mixed model are plotted on the y -axis. The horizontal dashed line indicates the genome-wide significance threshold ( P = 1×10 –6 ). P values were determined using a two-sided Wald test and assessed after Bonferroni correction for multiple comparisons. b Linkage disequilibrium (LD) heatmap of the Chalk9 locus region. Pairwise linkage disequilibrium was determined by calculating r 2 (the square of the correlation coefficient between SNPs). c Relative expression level of the 12 candidate genes in the endosperm of eight high-chalky and eight low-chalky varieties at 20 days after flowering (DAF). The 12 predicted genes in the Chalk9 locus region are labeled by I to XII. Data show means ± SD ( n = 8 varieties). P values were calculated for comparisons between high-chalky and low-chalky groups, with each group comprising 8 varieties. d Relative expression level of the candidate gene III ( Chalk9 ) in the endosperm from the selected varieties at 20 DAF. The P value was calculated for the comparison between high-chalky and low-chalky groups, with each group comprising 8 varieties. Data show means ± SD ( n = 3 biological replicates). e Relative expression level of the 12 candidate genes in the leaves of eight high-chalky and eight low-chalky varieties. Data show means ± SD ( n = 8 varieties). In c – e , statistical analysis between high-chalky and low-chalky groups was performed by two-tailed Student’s t -test. Source data are provided as a Source Data file.

Article Snippet: Correlation analysis, heatmap plotting, and volcano plot analysis were performed using BMKCloud ( www.biocloud.net ).

Techniques: GWAS, Transformation Assay, Genome Wide, Expressing, Labeling, Comparison, Two Tailed Test

Journal: Journal of Agricultural and Food Chemistry

Article Title: Antibiotic Resistance Genes and Microbiota in Brassica oleracea var. acephala Cultivated in South Korea: Potential for Resistance Transmission

doi: 10.1021/acs.jafc.4c11161

Figure Lengend Snippet: Taxonomic profiling and quantification of potential foodborne pathogens. (A) Pie chart showing the proportion of kale microbiota composition at the phylum, class, and family levels. (B) Each column of the heatmap at the genus level shows normalized relative abundance, allowing for a comparison of regional differences between samples. (C) A LEfSe analysis of the kale microbiota identified the most differentially abundant taxa distinguishing the sampling sites. The bar graphs show LDA scores (>3.0). The color of each bar is identical to Figure . (D) Potential foodborne pathogens in the kale microbiota were quantified by measuring the gene copies of each specific pathogen relative to the total 16S rRNA gene copies using qRT-PCR, with three technical replicates. The foodborne pathogens targeted for quantitative experiments were A. lwoffii , B. cereus , Enteroaggregative E. coli (EAEC), Enterohemorrhagic E. coli (EHEC), Enterotoxigenic E. coli (ETEC), Enteropathogenic E. coli (EPEC), K. pneumoniae , P. aeruginosa , Pa. agglomerans , S. aureus , and Se. marcescens . Error bars indicate the standard deviation.

Article Snippet: Heatmap analysis was generated using RStudio, and LEfSe analysis was conducted at https://huttenhower.sph.harvard.edu/lefse/ ( p < 0.05 and LDA score >2.0).

Techniques: Comparison, Sampling, Quantitative RT-PCR, Standard Deviation

Journal: Journal of Agricultural and Food Chemistry

Article Title: Antibiotic Resistance Genes and Microbiota in Brassica oleracea var. acephala Cultivated in South Korea: Potential for Resistance Transmission

doi: 10.1021/acs.jafc.4c11161

Figure Lengend Snippet: Relative abundances of microbial species and overview of antimicrobial resistance (AMR) in kale microbiome. (A) The heatmap compares the dominant taxa at the species level, annotated from shotgun metagenomic sequencing, between the two regions. (B) Relative abundances of bacterial species and subspecies harboring ARGs were determined. (C) The distribution of coliform and noncoliform bacteria harboring antibiotic resistance genes (ARGs) and the classes of antibiotic resistance were compared in kale samples. Values are formulated from the number of reads that aligned to ARGs and normalized to bacterial abundance characterized by alignments to 16S rRNA gene sequences. (For detailed data, please refer to Table S2 ).

Article Snippet: Heatmap analysis was generated using RStudio, and LEfSe analysis was conducted at https://huttenhower.sph.harvard.edu/lefse/ ( p < 0.05 and LDA score >2.0).

Techniques: Sequencing, Bacteria