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scatterplots and pearson correlation coefficients (r values) between proteins and mrna expression ratios  (GraphPad Software Inc)


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    GraphPad Software Inc scatterplots and pearson correlation coefficients (r values) between proteins and mrna expression ratios
    Schematic overview of proteomic and transcriptomic data generation and analysis workflow. Step 1: differential expression analysis of proteomic and Step 2: differential expression analysis of <t>mRNA-seq</t> data. Step 3: AS (alternative splicing) analysis using TopHat software and alternatively spliced differentially expressed genes (AS-DEGs) were obtained. Step 4: mapping of <t>all</t> <t>proteins</t> from the proteomic to mRNA-seq data and output of the two target groups. Among them, Group A contains translation repression and/or transcriptional degradation targets (contains differentially expressed genes and proteins whose levels were unchanged, both genes and proteins that were differentially expressed or no proteomic data). Group B contains potential targets of miRNA translational repression (differentially abundant proteins whose mRNA did not change or no mRNA data). Step 5: genome-wide analysis of differentially expressed conserved and novel miRNAs in both cotton genotypes under control and salt stress conditions. Step 6: psRNATarget analysis of both groups against miRNAs DE in the opposite direction combined with Group A and B data
    Scatterplots And Pearson Correlation Coefficients (R Values) Between Proteins And Mrna Expression Ratios, supplied by GraphPad Software 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/product/pearson+correlation+coefficient+calculation/pearson+correlation+coefficient/pmc06011603-100-10-19
    Average 90 stars, based on 1 article reviews
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    1) Product Images from "Integration of proteomic and transcriptomic profiles reveals multiple levels of genetic regulation of salt tolerance in cotton"

    Article Title: Integration of proteomic and transcriptomic profiles reveals multiple levels of genetic regulation of salt tolerance in cotton

    Journal: BMC Plant Biology

    doi: 10.1186/s12870-018-1350-1

    Schematic overview of proteomic and transcriptomic data generation and analysis workflow. Step 1: differential expression analysis of proteomic and Step 2: differential expression analysis of mRNA-seq data. Step 3: AS (alternative splicing) analysis using TopHat software and alternatively spliced differentially expressed genes (AS-DEGs) were obtained. Step 4: mapping of all proteins from the proteomic to mRNA-seq data and output of the two target groups. Among them, Group A contains translation repression and/or transcriptional degradation targets (contains differentially expressed genes and proteins whose levels were unchanged, both genes and proteins that were differentially expressed or no proteomic data). Group B contains potential targets of miRNA translational repression (differentially abundant proteins whose mRNA did not change or no mRNA data). Step 5: genome-wide analysis of differentially expressed conserved and novel miRNAs in both cotton genotypes under control and salt stress conditions. Step 6: psRNATarget analysis of both groups against miRNAs DE in the opposite direction combined with Group A and B data
    Figure Legend Snippet: Schematic overview of proteomic and transcriptomic data generation and analysis workflow. Step 1: differential expression analysis of proteomic and Step 2: differential expression analysis of mRNA-seq data. Step 3: AS (alternative splicing) analysis using TopHat software and alternatively spliced differentially expressed genes (AS-DEGs) were obtained. Step 4: mapping of all proteins from the proteomic to mRNA-seq data and output of the two target groups. Among them, Group A contains translation repression and/or transcriptional degradation targets (contains differentially expressed genes and proteins whose levels were unchanged, both genes and proteins that were differentially expressed or no proteomic data). Group B contains potential targets of miRNA translational repression (differentially abundant proteins whose mRNA did not change or no mRNA data). Step 5: genome-wide analysis of differentially expressed conserved and novel miRNAs in both cotton genotypes under control and salt stress conditions. Step 6: psRNATarget analysis of both groups against miRNAs DE in the opposite direction combined with Group A and B data

    Techniques Used: Quantitative Proteomics, Alternative Splicing, Software, Genome Wide, Control

    Hierarchical clustering analysis of mRNA-seq and proteomic data based on expression data. Heatmap of the proteins and mRNA expression ratios that have the same ( a , c ) or opposite ( b , d ) change tendencies after 4 h and 24 h of salt treatment, respectively. The expression profiles shown in the left and right panels are based on standardized log 2 ratio values. The minimum and maximum displayed log 2 ratios are ±3 for the transcriptomic data and ± 0.5 for the proteomics data. Black represents no significant change in expression. The KEGG pathway of genes/proteins showing significance are represented by various symbols behind the gene ID
    Figure Legend Snippet: Hierarchical clustering analysis of mRNA-seq and proteomic data based on expression data. Heatmap of the proteins and mRNA expression ratios that have the same ( a , c ) or opposite ( b , d ) change tendencies after 4 h and 24 h of salt treatment, respectively. The expression profiles shown in the left and right panels are based on standardized log 2 ratio values. The minimum and maximum displayed log 2 ratios are ±3 for the transcriptomic data and ± 0.5 for the proteomics data. Black represents no significant change in expression. The KEGG pathway of genes/proteins showing significance are represented by various symbols behind the gene ID

    Techniques Used: Expressing

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    Article Title: Ligand-activated EGFR/MAPK signaling but not PI3K, are key resistance mechanisms to EGFR-therapy in colorectal cancer

    doi: 10.1038/s41467-025-59588-3

    Figure Lengend Snippet: a , b Scatterplot showing the relationship between gene-level alteration frequencies in ctDNA and archival tissues from ( a ) 141 patients who received prior anti-VEGF therapies, and ( b )113 patients who received prior anti-EGFR therapies. The effect size was estimated using Pearson’s correlation coefficient(r) calculated in GraphPad Prism, and the coefficient of determination (r 2 ), representing the proportion of variance explained by the relationship, is displayed on the scatter plot. c Comparison of percentage of patients with emerging alterations between patients with prior anti-EGFR therapies and those with prior anti-VEGF therapies. Alterations that were absent from tumor tissue samples but detected in plasma samples were defined as emerging alterations. (All genes with at least one emerging alteration listed). Only known and likely oncogenic alterations in each gene were considered for the analysis. P- value was calculated using a two-tailed Fisher’s exact test to compare the percentage of patients with emerging alterations between those who received anti-EGFR therapies and those who received anti-VEGF therapies. Source data are provided as a Source Data file.

    Article Snippet: The effect size was estimated using Pearson’s correlation coefficient(r) calculated in GraphPad Prism, and the coefficient of determination (r 2 ), representing the proportion of variance explained by the relationship, is displayed on the scatter plot. c Comparison of percentage of patients with emerging alterations between patients with prior anti-EGFR therapies and those with prior anti-VEGF therapies.

    Techniques: Comparison, Clinical Proteomics, Two Tailed Test

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    Journal: Frontiers in Pharmacology

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    Figure Lengend Snippet: Correlation plots of equilibrium and kinetic parameters of cannabinoid compounds at CB1R and CB2R. Correlation between negative logarithmic transformation of affinities (−log K d ) and logarithmic (A) association rate (log k on ) and (B) dissociation rate (log k off ) for CB1R ligands. Correlation between negative logarithmic transformation of affinities (−log K d ) and logarithmic (C) association rates (log k on ) and (D) dissociation rates (log k off ) for CB2R ligands. Correlation analysis was carried out using a Pearson correlation analysis (two-tailed). Data shown are the mean and SEM of four independent experiments.

    Article Snippet: The correlation between datasets was determined by calculating the Pearson correlation coefficient (presented as the r 2 coefficient of determination, which shows percentage variation in y, which is explained by all the x variables together) in GraphPad Prism 9.2.

    Techniques: Transformation Assay, Two Tailed Test