linear mixed effects model matlab routine fitlme Search Results


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MathWorks Inc multivariate regression analysis
Identification of key transcription factors associated with reporter expression levels and noises using <t>multivariate</t> linear regression analysis. ( A ) View of the barcode 29 locus on chromosome 1. Genome coordinate of each barcode was obtained from conducting TRIP experiment. Six examples of normalized ChIP-seq signal profile of transcription factor surrounding the integration site of the barcode 29 were visualized for a domain of 50 kb. ( B ) The heatmap showing the correlation between the enrichment of transcription factors and reporter expression mean and noise. Transcription factors with Spearman's rank-order correlation coefficient of more than 0.2 were selected from over 200 tested transcription factors. ( C , D ) To understand the relationship between the enrichment of TF and the expression levels ( C ) and noise ( D ) of reporter in an integrative way, we selected transcription factors showing a significant correlation in ( B ) to fit multivariate linear regression model. Features with a significant level above the threshold or dashed line (p < 0.05) contributed significantly to the model. ( E ) A Venn diagram listing transcription factors contributing to reporter expression mean (green) and noise (red).
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MathWorks Inc linear regression models matlab 'fitlm
Identification of key transcription factors associated with reporter expression levels and noises using <t>multivariate</t> linear regression analysis. ( A ) View of the barcode 29 locus on chromosome 1. Genome coordinate of each barcode was obtained from conducting TRIP experiment. Six examples of normalized ChIP-seq signal profile of transcription factor surrounding the integration site of the barcode 29 were visualized for a domain of 50 kb. ( B ) The heatmap showing the correlation between the enrichment of transcription factors and reporter expression mean and noise. Transcription factors with Spearman's rank-order correlation coefficient of more than 0.2 were selected from over 200 tested transcription factors. ( C , D ) To understand the relationship between the enrichment of TF and the expression levels ( C ) and noise ( D ) of reporter in an integrative way, we selected transcription factors showing a significant correlation in ( B ) to fit multivariate linear regression model. Features with a significant level above the threshold or dashed line (p < 0.05) contributed significantly to the model. ( E ) A Venn diagram listing transcription factors contributing to reporter expression mean (green) and noise (red).
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Identification of key transcription factors associated with reporter expression levels and noises using multivariate linear regression analysis. ( A ) View of the barcode 29 locus on chromosome 1. Genome coordinate of each barcode was obtained from conducting TRIP experiment. Six examples of normalized ChIP-seq signal profile of transcription factor surrounding the integration site of the barcode 29 were visualized for a domain of 50 kb. ( B ) The heatmap showing the correlation between the enrichment of transcription factors and reporter expression mean and noise. Transcription factors with Spearman's rank-order correlation coefficient of more than 0.2 were selected from over 200 tested transcription factors. ( C , D ) To understand the relationship between the enrichment of TF and the expression levels ( C ) and noise ( D ) of reporter in an integrative way, we selected transcription factors showing a significant correlation in ( B ) to fit multivariate linear regression model. Features with a significant level above the threshold or dashed line (p < 0.05) contributed significantly to the model. ( E ) A Venn diagram listing transcription factors contributing to reporter expression mean (green) and noise (red).

Journal: Scientific Reports

Article Title: Identifying chromatin features that regulate gene expression distribution

doi: 10.1038/s41598-020-77638-2

Figure Lengend Snippet: Identification of key transcription factors associated with reporter expression levels and noises using multivariate linear regression analysis. ( A ) View of the barcode 29 locus on chromosome 1. Genome coordinate of each barcode was obtained from conducting TRIP experiment. Six examples of normalized ChIP-seq signal profile of transcription factor surrounding the integration site of the barcode 29 were visualized for a domain of 50 kb. ( B ) The heatmap showing the correlation between the enrichment of transcription factors and reporter expression mean and noise. Transcription factors with Spearman's rank-order correlation coefficient of more than 0.2 were selected from over 200 tested transcription factors. ( C , D ) To understand the relationship between the enrichment of TF and the expression levels ( C ) and noise ( D ) of reporter in an integrative way, we selected transcription factors showing a significant correlation in ( B ) to fit multivariate linear regression model. Features with a significant level above the threshold or dashed line (p < 0.05) contributed significantly to the model. ( E ) A Venn diagram listing transcription factors contributing to reporter expression mean (green) and noise (red).

Article Snippet: The analysis of statistical significance was done using standard multivariate regression analysis (using the Matlab command fitlm).

Techniques: Expressing, ChIP-sequencing

Distance to specific chromatin states influences expression mean and variance ( A ) Nearest distance from the barcode location to each chromatin state segmented by ChromHMM method was calculated. ( B ) The correlation plot between distance to specific chromatin state and reporter expression mean or noise. ( C ) A multivariate linear regression model was used to determine significant chromatin states influencing reporter expression mean and noise. Panels show the student t statistics and p-values of each coefficient in the model for expression mean (top) and noise (bottom). Chromatin states are color-coded.

Journal: Scientific Reports

Article Title: Identifying chromatin features that regulate gene expression distribution

doi: 10.1038/s41598-020-77638-2

Figure Lengend Snippet: Distance to specific chromatin states influences expression mean and variance ( A ) Nearest distance from the barcode location to each chromatin state segmented by ChromHMM method was calculated. ( B ) The correlation plot between distance to specific chromatin state and reporter expression mean or noise. ( C ) A multivariate linear regression model was used to determine significant chromatin states influencing reporter expression mean and noise. Panels show the student t statistics and p-values of each coefficient in the model for expression mean (top) and noise (bottom). Chromatin states are color-coded.

Article Snippet: The analysis of statistical significance was done using standard multivariate regression analysis (using the Matlab command fitlm).

Techniques: Expressing