Aaron Meyer
June 28, 2016
Note that text describes the figure following it.
- TCPS: tissue culture plastic
- gel: stiff 2D PEG-PC gel
- single: single cells in 3D PEG gel
- sph: spheroids in 3D PEG gel
- serum: spheroids in 3D PEG gel plus serum
- DMSO
- lap: lapatinib
- tems: temsirolimus
- soraf: sorafenib
2 biological replicates, each with 2 technical replicates DMSO control can be used for all 3 drugs (equalized DMSO concentration across all)
This plots the drug response in each matrix condition separately, against pMek. From this it's possible to see a relationship between viability and pMek that exists across matrix conditions.
ggplot(MB231, aes(x = MEK1, y = prolifTwo, color = drug, shape = matrix)) +
geom_point() + facet_wrap( ~ matrix) +
coord_trans(x = "sqrt", y = "sqrt") + theme_bw() + ylab("Viability (RU)") + xlab("pMek")
![](Figures_files/figure-html/mek plot matrix-1.png)
A linear regression model clearly shows that pMek significantly explains some of the variance in viability. Note that the model explains about 30% of the variance, so there certainly are changes in drug response not completely captured by the model still.
# Scaling the phospho-measurements by z-score
MB231d <- dplyr::select(MB231, prolifTwo, CREB, EGFR, NFkB, p38, AKT, JNK, MEK1, ERK1.2) %>%
scale(center = TRUE, scale = TRUE) %>% data.frame
# Applying the linear model
model <- lm(prolifTwo ~ . + 0, data = MB231d)
# Summarize the model
model.sum <- (summary(model))
# Print the model summary
print(model.sum)
##
## Call:
## lm(formula = prolifTwo ~ . + 0, data = MB231d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5457 -0.4924 -0.1094 0.4007 3.1405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## CREB 0.08839 0.19838 0.446 0.6573
## EGFR -0.52229 0.22045 -2.369 0.0205 *
## NFkB 0.10901 0.18457 0.591 0.5566
## p38 0.08970 0.21109 0.425 0.6722
## AKT -0.38011 0.15950 -2.383 0.0198 *
## JNK 0.28277 0.16948 1.669 0.0996 .
## MEK1 0.94257 0.21496 4.385 3.89e-05 ***
## ERK1.2 -0.31953 0.18466 -1.730 0.0878 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8413 on 72 degrees of freedom
## Multiple R-squared: 0.3549, Adjusted R-squared: 0.2832
## F-statistic: 4.952 on 8 and 72 DF, p-value: 6.646e-05
Plot of MB231 model parameters.
# Extract the model coefficients for plotting
plotD <- data.frame(model.sum$coefficients)
ggplot(data = plotD, aes(x = Estimate, y = log10(Pr...t..), label = rownames(plotD))) +
geom_point() + theme_bw() + geom_text(nudge_y = 0.06) + scale_y_reverse() + xlim(-1.2, 1.2) +
ylab("Log10 P-Value") + ggtitle("MB231 Viability Linear Model") +
geom_errorbarh(aes(xmin = Estimate - Std..Error, xmax = Estimate + Std..Error))
I've plotted the SKBR3 data with respect to pMek just for consistency with the MB231 data. There are considerable matrix-dependent differences in drug response, but they aren't really explained by pMek.
ggplot(SKBR3, aes(x = MEK1, y = prolifTwo, color = drug, shape = matrix)) + geom_point() + facet_wrap( ~ matrix) +
coord_trans(x = "identity", y = "identity") + theme_bw() + ylab("Viability (RU)") + xlab("pMek")
![](Figures_files/figure-html/mek plot matrix skbr-1.png)
A linear model with the SKBR3 measurements doesn't really pick up much.
# Scaling the phospho-measurements by z-score
SKBR3d <- dplyr::select(SKBR3, prolifTwo, CREB, EGFR, NFkB, p38, AKT, JNK, MEK1, ERK1.2, STAT5, p70s6k) %>%
scale(center = TRUE, scale = TRUE) %>% data.frame
# Applying the linear model
model <- lm(prolifTwo ~ . + 0, data = SKBR3d)
# Summarize the model
model.sum <- (summary(model))
# Print the model summary
print(model.sum)
##
## Call:
## lm(formula = prolifTwo ~ . + 0, data = SKBR3d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.58014 -0.59450 0.04673 0.65844 2.31188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## CREB 0.03236 0.29938 0.108 0.9142
## EGFR 0.54024 0.34725 1.556 0.1243
## NFkB -0.14597 0.19756 -0.739 0.4625
## p38 -0.16422 0.22120 -0.742 0.4603
## AKT 0.10487 0.38100 0.275 0.7839
## JNK 0.09835 0.35510 0.277 0.7826
## MEK1 -0.77432 0.41560 -1.863 0.0666 .
## ERK1.2 0.20660 0.29737 0.695 0.4895
## STAT5 -0.22199 0.15770 -1.408 0.1636
## p70s6k -0.12830 0.14208 -0.903 0.3696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9656 on 70 degrees of freedom
## Multiple R-squared: 0.1739, Adjusted R-squared: 0.05588
## F-statistic: 1.473 on 10 and 70 DF, p-value: 0.1679
Plot of SKBR3 model parameters.
# Extract the model coefficients for plotting
plotD <- data.frame(model.sum$coefficients)
ggplot(data = plotD, aes(x = Estimate, y = log10(Pr...t..), label = rownames(plotD))) +
geom_point() + theme_bw() + geom_text(nudge_y = 0.03) + scale_y_reverse() +
ylab("Log10 P-Value") + ggtitle("SKBR3 Viability Linear Model") +
geom_errorbarh(aes(xmin = Estimate - Std..Error, xmax = Estimate + Std..Error))