package bayesian optimized pla signal sorting (bopss) Search Results


package bayesian optimized pla signal sorting (bopss)  (MathWorks Inc)
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MathWorks Inc package bayesian optimized pla signal sorting (bopss)
Three counting images of the single (A) , dual (B) and negative <t>PLA</t> (C) from subject PI12277 were used to test puncta detection and quantification approaches with selected parameters: Particle Analysis (Image J) with auto threshold (Image J_Auto, D-F), Spot Detector (ICY) with parameters favoring detection of either dual and negative (ICY_D, G–I) or single (ICY_S, J–L) PLA signals, and <t>BOPSS</t> (BOPSS, M–O). Image J and ICY quantified the puncta in the transformed and contrast enhanced images (D–L) as described in Supplementary data. The counted puncta were marked in red dots (D-F & M-O) or labeled in red numbers (G-L) in the analyzed images. Single PLA puncta density results were analyzed with repeated one-way ANOVA; there was a significant effect of quantification method (p = 0.014) (P). Dual PLA and its negative control were analyzed by repeated two-way ANOVA, as they shared the same PLA conditions except for the omission of one of the two primary antibodies; the interaction between quantification method and PLA signal was not significant (accounts for 3.73% for the total variance, p = 0.21); both the quantification method (accounts for 56.9% of the total variance, p = 0.005) and the PLA condition (accounts for 26.22% of the total variance, p = 0.009) had significant effects on the variance (Q). Bonferroni's multiple comparisons were performed for both sets of analyses: to compare BOPSS and the other quantification methods: *Multiplicity-adjusted p < 0.05 (P & Q) ; to compare the results of dual PLA and negative PLA results: # Multiplicity-adjusted p < 0.05 (Q). Data were plotted as mean ± SEM. PLA: Proximity ligation assay.
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Three counting images of the single (A) , dual (B) and negative PLA (C) from subject PI12277 were used to test puncta detection and quantification approaches with selected parameters: Particle Analysis (Image J) with auto threshold (Image J_Auto, D-F), Spot Detector (ICY) with parameters favoring detection of either dual and negative (ICY_D, G–I) or single (ICY_S, J–L) PLA signals, and BOPSS (BOPSS, M–O). Image J and ICY quantified the puncta in the transformed and contrast enhanced images (D–L) as described in Supplementary data. The counted puncta were marked in red dots (D-F & M-O) or labeled in red numbers (G-L) in the analyzed images. Single PLA puncta density results were analyzed with repeated one-way ANOVA; there was a significant effect of quantification method (p = 0.014) (P). Dual PLA and its negative control were analyzed by repeated two-way ANOVA, as they shared the same PLA conditions except for the omission of one of the two primary antibodies; the interaction between quantification method and PLA signal was not significant (accounts for 3.73% for the total variance, p = 0.21); both the quantification method (accounts for 56.9% of the total variance, p = 0.005) and the PLA condition (accounts for 26.22% of the total variance, p = 0.009) had significant effects on the variance (Q). Bonferroni's multiple comparisons were performed for both sets of analyses: to compare BOPSS and the other quantification methods: *Multiplicity-adjusted p < 0.05 (P & Q) ; to compare the results of dual PLA and negative PLA results: # Multiplicity-adjusted p < 0.05 (Q). Data were plotted as mean ± SEM. PLA: Proximity ligation assay.

Journal: Biotechniques

Article Title: Detecting G protein-coupled receptor complexes in postmortem human brain with proximity ligation assay and a Bayesian classifier

doi: 10.2144/btn-2019-0083

Figure Lengend Snippet: Three counting images of the single (A) , dual (B) and negative PLA (C) from subject PI12277 were used to test puncta detection and quantification approaches with selected parameters: Particle Analysis (Image J) with auto threshold (Image J_Auto, D-F), Spot Detector (ICY) with parameters favoring detection of either dual and negative (ICY_D, G–I) or single (ICY_S, J–L) PLA signals, and BOPSS (BOPSS, M–O). Image J and ICY quantified the puncta in the transformed and contrast enhanced images (D–L) as described in Supplementary data. The counted puncta were marked in red dots (D-F & M-O) or labeled in red numbers (G-L) in the analyzed images. Single PLA puncta density results were analyzed with repeated one-way ANOVA; there was a significant effect of quantification method (p = 0.014) (P). Dual PLA and its negative control were analyzed by repeated two-way ANOVA, as they shared the same PLA conditions except for the omission of one of the two primary antibodies; the interaction between quantification method and PLA signal was not significant (accounts for 3.73% for the total variance, p = 0.21); both the quantification method (accounts for 56.9% of the total variance, p = 0.005) and the PLA condition (accounts for 26.22% of the total variance, p = 0.009) had significant effects on the variance (Q). Bonferroni's multiple comparisons were performed for both sets of analyses: to compare BOPSS and the other quantification methods: *Multiplicity-adjusted p < 0.05 (P & Q) ; to compare the results of dual PLA and negative PLA results: # Multiplicity-adjusted p < 0.05 (Q). Data were plotted as mean ± SEM. PLA: Proximity ligation assay.

Article Snippet: We designed a custom MATLAB package Bayesian optimized PLA signal sorting (BOPSS) to analyze PLA signal images [ ].

Techniques: Particle Size Analysis, Transformation Assay, Labeling, Negative Control, Proximity Ligation Assay