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binary logistic regression panel data analysis  (STATA Corporation)


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    STATA Corporation binary logistic regression panel data analysis
    Binary Logistic Regression Panel Data Analysis, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/binary logistic regression panel data analysis/product/STATA Corporation
    Average 90 stars, based on 1 article reviews
    binary logistic regression panel data analysis - by Bioz Stars, 2026-06
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    Validation of the RNA-Seq results was performed by comparing the significant ( p < 0.050) DEGs in the transcriptome analysis with those that were significant in a Qiagen targeted-RNA sequencing panel (491 immune response genes) in a different cohort of Kenyan children [SMA ( n = 21) and non-SMA ( n = 23). a Heatmap illustrating the comparison of significant ( p < 0.050) DEGs between the two datasets. The Y-axis depicts the gene pairs, and the X-axis represents the assay type. The color scale depicts fold regulation (Log 2 ). Statistical significance determined using a generalized linear model with a negative binomial distribution, p < 0.050. b Correlation scatter plot demonstrating the relationship between significantly expressed genes in targeted QIAseq analysis (Log 2 FoldChange, Y-axis) versus transcriptome data (Log 2 FoldChange, X-axis). A strong positive correlation of DEGs using two-tailed Spearman’s test ( r = 0.612; 95% confidence interval, 0.496-0.706; p = 1.842E−16) validates concordance between the two sequencing methods.

    Journal: Nature Communications

    Article Title: Entire expressed peripheral blood transcriptome in pediatric severe malarial anemia

    doi: 10.1038/s41467-024-48259-4

    Figure Lengend Snippet: Validation of the RNA-Seq results was performed by comparing the significant ( p < 0.050) DEGs in the transcriptome analysis with those that were significant in a Qiagen targeted-RNA sequencing panel (491 immune response genes) in a different cohort of Kenyan children [SMA ( n = 21) and non-SMA ( n = 23). a Heatmap illustrating the comparison of significant ( p < 0.050) DEGs between the two datasets. The Y-axis depicts the gene pairs, and the X-axis represents the assay type. The color scale depicts fold regulation (Log 2 ). Statistical significance determined using a generalized linear model with a negative binomial distribution, p < 0.050. b Correlation scatter plot demonstrating the relationship between significantly expressed genes in targeted QIAseq analysis (Log 2 FoldChange, Y-axis) versus transcriptome data (Log 2 FoldChange, X-axis). A strong positive correlation of DEGs using two-tailed Spearman’s test ( r = 0.612; 95% confidence interval, 0.496-0.706; p = 1.842E−16) validates concordance between the two sequencing methods.

    Article Snippet: Expression analysis using molecular tag counts from RNA-Seq file was done using Targeted RNA Panel Secondary Data Analysis web portal (v1.0, Qiagen).

    Techniques: Biomarker Discovery, RNA Sequencing, Comparison, Two Tailed Test, Sequencing