Review



package bayesian optimized pla signal sorting (bopss)  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    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.
    Package Bayesian Optimized Pla Signal Sorting (Bopss), supplied by MathWorks Inc, 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/package bayesian optimized pla signal sorting (bopss)/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    package bayesian optimized pla signal sorting (bopss) - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Detecting G protein-coupled receptor complexes in postmortem human brain with proximity ligation assay and a Bayesian classifier"

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

    Journal: Biotechniques

    doi: 10.2144/btn-2019-0083

    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.
    Figure Legend 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.

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



    Similar Products

    package bayesian optimized pla signal sorting (bopss)  (MathWorks Inc)
    90
    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.
    Package Bayesian Optimized Pla Signal Sorting (Bopss), supplied by MathWorks Inc, 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/package bayesian optimized pla signal sorting (bopss)/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    package bayesian optimized pla signal sorting (bopss) - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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