DECONV DREAM Challenege 2020 (4)

library(gridExtra)
library(ggplot2)
a3$type<-"glmnet200"
a4$type<-"glmnet250"
a5$type<-"glmnet350"
a6$type<-"glmnet500"
x1$type<-"svr200"
x2$type<-"svr250"
x3$type<-"svr300"
x4$type<-"svr500"

z4$type<-"pls500"
z3$type<-"pls350"
z2$type<-"pls250"
z1$type<-"pls200"

heatmap <-rbind(a3,a4,a5,a6,
                
                x1,x2,x3,x4,
                z1,z2,z3,z4)

 

Heatmap per cell (Pearson)

#library(ggplotify)
#par(mfrow=c(2,1))
pattern="DS"

heatmap %>% group_by(type,cells) %>%  summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~cells) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")

Heatmap per cell rnaseq (Pearson)

heatmap %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,cells) %>%  summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~cells) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")

Heatmap per cell (Spearman)

heatmap %>% group_by(type,cells) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~cells) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")

Heatmap per cell rnaseq (Spearman)

heatmap  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,cells) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~cells) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")

Heatmap per dataset (Pearson)

#library(ggplotify)
#par(mfrow=c(2,1))
pattern="DS"

heatmap %>% group_by(type,datasets) %>%  summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")

Heatmap per dataset rnaseq (Pearson)

heatmap %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>%  summarise(pearson=mean(pearson)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Blues")

Heatmap per dataset (Spearman)

heatmap %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")

Heatmap per dataset rnaseq (Spearman)

heatmap  %>% filter(datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>% group_by(type,datasets) %>% summarise(spearman=mean(spearman)) %>% reshape2::acast(type ~datasets) %>% as.matrix() %>% d3heatmap::d3heatmap(colors = "Reds")

a3 [glmnet200] Results for glmnet 200 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_200_20000_newmix.csv"
readr::write_csv(RESULTS,path = filename)
a3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a3

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

a4 [glmnet250] Results for glmnet 250 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_250_20000_newmix.csv"
readr::write_csv(RESULTS,path = filename)
a4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a4

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

a5 [glmnet350] Results for glmnet 350 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_350_20000_all_newmix.csv"
readr::write_csv(RESULTS,path = filename)
a5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a5

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

a6 [glmnet500] Results for glmnet 500 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_500_20000_newmix.csv"
readr::write_csv(RESULTS,path = filename)
a6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a6

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

x1 [svr200] Results for svr radial noscale 200 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_200_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
x1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x1

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x2 [svr250] Results for svr radial noscale 250 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_250_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
x2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x2

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x3 [svr350] Results for svr radial noscale 350 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_350_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
x3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x3

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x4 [svr500] Results for svr radial noscale 500 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_500_20000_noscale_newmix.csv"
#readr::write_csv(RESULTS,path = filename)
x4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x4

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

z4 [pls500] Results for pls 500 features (RF)

filename<-"./devcon_challenge.pls_results_rf_selected_features_500_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
z4<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
z4

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

z3 [pls350] Results for pls 350 features (RF)

filename<-"./devcon_challenge.pls_results_rf_selected_features_350_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
z3<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
z3

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

z2 [pls250] Results for pls 250 features (RF)

filename<-"./devcon_challenge.pls_results_rf_selected_features_250_20000_noscale_newmix.csv"
readr::write_csv(RESULTS,path = filename)
z2<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
z2

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

z1 [pls200] Results for pls 200 features (RF)

filename<-"./devcon_challenge.pls_results_rf_selected_features_200_20000_noscale_newmix"
readr::write_csv(RESULTS,path = filename)
z1<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
z1

readr::read_csv(filename) %>% filter(!is.na(pearson)) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

readr::read_csv(filename) %>% filter(!is.na(pearson))  %>% filter( datasets %in% c("DS446395","DS500","CIAS4","CIVA3")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

load("results_glmnet_devcon_bestmodels_fgdata_cps_20000_2_selected_features_250_test_added.rdata")
glmnet250<-results_final_models

load("results_svr_radial_devcon_bestmodels_fgdata_cps_20000_2_noscale_selected_features_200_newgama_test_added.rdata")
svr200<-results_final_models

glmnet250[['cancer']]$nzero
svr200[['cancer']]$