DECONV DREAM Challenege 2020 (5)

library(gridExtra)
library(ggplot2)
a3$type<-"glmnet200"
a4$type<-"glmnet250"
a5$type<-"glmnet350"
a6$type<-"glmnet500"
a7$type<-"glmnet1000"
a8$type<-"glmnet2500"
a9$type<-"glmnet5000"
a10$type<-"glmnet10000"
a11$type<-"glmnet20000"

x1$type<-"svr200"
x2$type<-"svr250"
x3$type<-"svr300"
x4$type<-"svr500"
x5$type<-"svr750"
x6$type<-"svr1000"
x7$type<-"svr1250"
x8$type<-"svr2000"
x9$type<-"svr5000"



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

heatmap <-rbind(a3,a4,a5,a6,a7,a8,a9,a10,a11,
                
                x1,x2,x3,x4,x5,x6,x7,x8,x9,
                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","FIAS4")) %>% 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","FIAS4")) %>% 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","FIAS4")) %>% 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","FIAS4")) %>% 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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_newmix_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

a7 [glmnet1000] Results for glmnet 1000 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_1000_20000_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
a7<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a7

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

a8 [glmnet2500] Results for glmnet 2500 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_2500_20000_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
a8<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a8

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

a9 [glmnet5000] Results for glmnet 5000 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_5000_20000_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
a9<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a9

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

a10 [glmnet10000] Results for glmnet 10000 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_10000_20000_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
a10<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a10

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

NA

a11 [glmnet20000] Results for glmnet 20000 features over 20000 (RF2)

filename<-"./devcon_challenge.glmnet_results_noscale_rf_selected_features_20000_20000_newmix_spearson_finegrain.csv"
readr::write_csv(RESULTS,path = filename)
a11<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
a11

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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x5 [svr750] Results for svr radial noscale 750 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_750_20000_noscale_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
x5<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x5

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x6 [svr1000] Results for svr radial noscale 1000 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_1000_20000_noscale_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
x6<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x6

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x7 [svr1250] Results for svr radial noscale 1250 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_1250_20000_noscale_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
x7<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x7

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x8 [svr2000] Results for svr radial noscale 2000 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_2000_20000_noscale_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
x8<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x8

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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))

x9 [svr5000] Results for svr radial noscale 5000 features over 20000 (RF2)

filename<-"./devcon_challenge.svr_results_rf_selected_features_5000_20000_noscale_newmix_spearson_finegrain.csv"
#readr::write_csv(RESULTS,path = filename)
x9<-readr::read_csv(filename) %>% filter(!is.na(pearson)) 
x9

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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson_finegrain.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","FIAS4")) %>%  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_spearson.csv"
#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","FIAS4")) %>%  summarise(pearson_mean=mean(pearson),spearman_mean=mean(spearman))