QuanTP: Association between abundance ratios of transcript and protein

Input data summary

Abbreviations used: PE (Proteome data) and TE (Transcriptome data)

Input Proteome data dimension (Row Column): 2817 x 5
Input Transcriptome data dimension (Row Column): 2817 x 5

SAMPLE DISTRIBUTION

Boxplot: Transcriptome data	Boxplot: Proteome data

Sample wise distribution (Box plot) after using mean on replicates

Boxplot: Transcriptome data	Boxplot: Proteome data

Distribution (Box plot) of log fold change

Boxplot: Transcriptome data	Boxplot: Proteome data

Download the complete fold change data here

Transcript Fold-Change	Protein Fold-Change

PCA plot: Transcriptome data	PCA plot: Proteome data

CORRELATION

Scatter plot between Proteome and Transcriptome Abundance

Parameter	Method 1	Method 2	Method 3
Correlation method	Pearson's product-moment correlation	Spearman's rank correlation rho	Kendall's rank correlation tau
Correlation coefficient	0.1173569	0.1608612	0.1093701

*Note that correlation is sensitive to outliers in the data. So it is important to analyze outliers/influential observations in the data.
Below we use Cook's distance based approach to identify such influential observations.

REGRESSION ANALYSIS

Linear Regression model fit between Proteome and Transcriptome data

Assuming a linear relationship between Proteome and Transcriptome data, we here fit a linear regression model.

Parameter	Value
Formula	PE_abundance~TE_abundance
Coefficients
(Intercept)	-0.06910598 (Pvalue: 1.220723e-05 )
TE_abundance	0.1712395 (Pvalue: 4.168015e-10 )
Model parameters
Residual standard error	0.8363295 ( 2815 degree of freedom)
F-statistic	39.31142 ( on 1 and 2815 degree of freedom)
R-squared	0.01377265
Adjusted R-squared	0.0134223

Regression and diagnostics plots

1) Residuals vs Fitted plot	2) Normal Q-Q plot of residuals

This plot checks for linear relationship assumptions. If a horizontal line is observed without any distinct patterns, it indicates a linear relationship.	This plot checks whether residuals are normally distributed or not. It is good if the residuals points follow the straight dashed line i.e., do not deviate much from dashed line.

Outliers based on the residuals from regression analysis

Residuals from Regression
Parameter	Value
Mean Residual value	1.942328e-17
Standard deviation (Residuals)	0.836181
Total outliers (Residual value > 2 standard deviation from the mean)	164 (Download these 164 data points with high residual values here)
(Download the complete residuals data here)

3) Residuals vs Leverage plot

This plot is useful to identify any influential cases, that is outliers or extreme values. They might influence the regression results upon inclusion or exclusion from the analysis.

INFLUENTIAL OBSERVATIONS

Cook's distance computes the influence of each data point/observation on the predicted outcome. i.e. this measures how much the observation is influencing the fitted values.
In general use, those observations that have a Cook's distance > than 4 times the mean may be classified as influential.

In the above plot, observations above red line ( 4 * mean Cook's distance) are influential. Genes that are outliers could be important. These observations influences the correlation values and regression coefficients

Parameter	Value
Mean Cook's distance	0.0004875011
Total influential observations (Cook's distance > 4 * mean Cook's distance)	115
Observations with Cook's distance < 4 * mean Cook's distance	2702

Scatterplot: Before removal

Scatterplot: After removal

Parameter	Method 1	Method 2	Method 3
Correlation method	Pearson's product-moment correlation	Spearman's rank correlation rho	Kendall's rank correlation tau
Correlation coefficient	0.1173569	0.1608612	0.1093701

Parameter	Method 1	Method 2	Method 3
Correlation method	Pearson's product-moment correlation	Spearman's rank correlation rho	Kendall's rank correlation tau
Correlation coefficient	0.1334038	0.1611936	0.1082761

Download the complete list of influential observations Download the complete list (After removing influential points)

Top 10 Influential observations (Cook's distance > 4 * mean Cook's distance)

Gene	Protein Log Fold-Change	Transcript Log Fold-Change	Cook's Distance
CATHL2	-1.960863	4.88565	0.1432189
CD177	-4.173263	2.057499	0.06826605
CATHL1	-0.9912973	4.835209	0.05767091
HP	2.570727	3.885549	0.04680496
AZU1	-2.226356	-5.561874	0.03737565
ELANE	-2.732479	-2.914936	0.03266198
PYGM	-0.06079228	6.071712	0.03242859
LTF	-2.4294	2.129742	0.02725017
ATP1A2	0.2871971	6.446299	0.01939256
C13H20orf194	-5.640732	-0.6697401	0.01852927

CLUSTER ANALYSIS

Heatmap of PE and TE abundance values (Hierarchical clustering)	Number of clusters to extract: 5

Download the hierarchical cluster list

K-mean clustering	Number of clusters: 4

Download the cluster list

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