The goal of ALS_clinical_trials is to …
library(tidyverse)
library(patchwork)This is a brief R tutorial on how to work with the data from our paper.
Read in the data (download from the data directory above, or from the supplement of our paper):
timepoints <- read_csv( "https://raw.githubusercontent.com/FelixTheStudent/ALS_clinical_trials/main/data/data_timepoints.csv")
patients <- read_csv( "https://raw.githubusercontent.com/FelixTheStudent/ALS_clinical_trials/main/data/data_patientInfo.csv" )To illustrate how disease progression is correlated with our biomarker (blood levels of neurofilament light chain, measured at time of diagnosis), we visualize four selected patients:
patient_colors <- c("#00b159", "#ffc425", "#f37735", "#d11141");
names(patient_colors) <- c("p32", "p715", "p20", "p33")
df <- timepoints %>% filter(patient %in% c("p20", "p32", "p33", "p715" ))
df %>% ggplot(aes(time, ALSFRSR, col=patient))+geom_point() + ylim(c(0, 60)) +
scale_color_manual(values = patient_colors) + xlab("Time since diagnosis [months]")+
theme_classic()+
df %>% ggplot(aes(time, nfl, col=patient)) +geom_point() + scale_y_log10(limits=c(1,1500)) +
scale_color_manual(values = patient_colors) + xlab("Time since diagnosis [months]")+
ylab("Neurofilaments in blood [pg/ml]")+ theme_classic() +
plot_layout(guides="collect")
#> Warning: Removed 24 rows containing missing values (geom_point).
This enables prediction of disease progression, increasing statistical power in clinical ALS trials. More details can be found in our manuscript:
Simon Witzel and Felix Frauhammer et al., 2021 (manuscript submitted)
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