Analyzing each dataset

library(DT)
library(UpSetR)

path_with_WMA <- '/Users/korshe/Documents/Data_Groningen/pbmc_5ds/'
dataset_ids <- c('stemi_v2','stemi_v3','v2_1m','v3_1m', 'ng')

tab_eqtls <- read.table(paste(path_with_WMA,'Sample.size._tab.tsv',sep=''), sep='\t', header=T)
tab <- tab_eqtls
for (dataset_id in dataset_ids){
#tab<- tab %>% group_by(bulk.feature_id) %>%   arrange(cFDR_empirical_feature_p_value)
tab <- tab[order(tab[,paste0('p_value_', dataset_id )], decreasing = F),]
tab_bh = multtest::mt.rawp2adjp(c("BH","Bonferroni"),rawp = tab[,paste0('p_value_', dataset_id )])$adjp
tab[,paste0('BH_', dataset_id )] <- tab_bh[,2]
tab[,paste0('Bonf_', dataset_id )] <- tab_bh[,3]
}


UpSetR::upset(fromList(list(NG= tab[tab$BH_ng <0.05, ]$snp_gene, 
                            STEMI_v2 = tab[tab$BH_stemi_v2 <0.05, ]$snp_gene,
                            STEMI_v3 = tab[tab$BH_stemi_v3 <0.05, ]$snp_gene,
                            V2_1M = tab[tab$BH_v2_1m <0.05, ]$snp_gene,
                            V3_1M = tab[tab$BH_v3_1m <0.05, ]$snp_gene
                            # ,
                            # WMA_SS = tab[tab$BH_ZW_ss <0.05, ]$snp_gene,
                            # WMA_SE = tab[tab$BH_ZW_SE <0.05, ]$snp_gene
                            )), 
              set_size.show = T, order.by = 'freq', nintersects = 60,nsets = 20,sets.x.label = 'BH eQTLs')

UpSetR::upset(fromList(list(NG= tab[tab$BH_ng <0.05, ]$feature_id, 
                            STEMI_v2 = tab[tab$BH_stemi_v2 <0.05, ]$feature_id,
                            STEMI_v3 = tab[tab$BH_stemi_v3 <0.05, ]$feature_id,
                            V2_1M = tab[tab$BH_v2_1m <0.05, ]$feature_id,
                            V3_1M = tab[tab$BH_v3_1m <0.05, ]$feature_id
                            # ,
                            # WMA_SS = tab[tab$BH_ZW_ss <0.05, ]$feature_id,
                            # WMA_SE = tab[tab$BH_ZW_SE <0.05, ]$feature_id
                            )), 
              set_size.show = T, order.by = 'freq', nintersects = 60,nsets = 20,sets.x.label = 'BH eGenes')

UpSetR::upset(fromList(list(NG= tab[tab$Bonf_ng <0.05, ]$snp_gene, 
                            STEMI_v2 = tab[tab$Bonf_stemi_v2 <0.05, ]$snp_gene,
                            STEMI_v3 = tab[tab$Bonf_stemi_v3 <0.05, ]$snp_gene,
                            V2_1M = tab[tab$Bonf_v2_1m <0.05, ]$snp_gene,
                            V3_1M = tab[tab$Bonf_v3_1m <0.05, ]$snp_gene
                            # ,
                            # WMA_SS = tab[tab$Bonf_ZW_ss <0.05, ]$snp_gene,
                            # WMA_SE = tab[tab$Bonf_ZW_SE <0.05, ]$snp_gene
                            )), 
              set_size.show = T, order.by = 'freq', nintersects = 60,nsets = 20,sets.x.label = 'Bonf eQTLs')

UpSetR::upset(fromList(list(NG= tab[tab$Bonf_ng <0.05, ]$feature_id, 
                            STEMI_v2 = tab[tab$Bonf_stemi_v2 <0.05, ]$feature_id,
                            STEMI_v3 = tab[tab$Bonf_stemi_v3 <0.05, ]$feature_id,
                            V2_1M = tab[tab$Bonf_v2_1m <0.05, ]$feature_id,
                            V3_1M = tab[tab$Bonf_v3_1m <0.05, ]$feature_id
                            # ,
                            # WMA_SS = tab[tab$Bonf_ZW_ss <0.05, ]$feature_id,
                            # WMA_SE = tab[tab$Bonf_ZW_SE <0.05, ]$feature_id
                            )), 
              set_size.show = T, order.by = 'freq', nintersects = 60,nsets = 20,sets.x.label = 'Bonf eGenes')

## Expression parameters

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(tidyr)
library(gridExtra)
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
param = 'mean.donor'
summary_stat_filteres <- tab
get_plot_for_values <- function(summary_stat_filteres,param){
  #summary_stat_filteres <- summary_stat_filteres[1:500,]
  param_cols <-c(paste0(param, '_stemi_v2'),
                 paste0(param, '_stemi_v3'),
                 paste0(param, '_v2_1m'),
                 paste0(param, '_v3_1m'),
                 paste0(param, '_ng'))

    summary_stat_filteres_par <- summary_stat_filteres %>%  select(one_of(param_cols), snp_gene)
  
  summary_stat_filteres_par <- summary_stat_filteres_par[sample(nrow(summary_stat_filteres_par), 1500), ] #sample(summary_stat_filteres_par, 1000)
  colnames(summary_stat_filteres_par) <-  gsub(param, "", colnames(summary_stat_filteres_par))
  colnames(summary_stat_filteres_par) <-  gsub("_", "", colnames(summary_stat_filteres_par))
  
  colnames(summary_stat_filteres_par)[ colnames(summary_stat_filteres_par) == 'stemiv2'] <- 'van_Blokland_v2'
  colnames(summary_stat_filteres_par)[ colnames(summary_stat_filteres_par) == 'stemiv3'] <- 'van_Blokland_v3'
  colnames(summary_stat_filteres_par)[ colnames(summary_stat_filteres_par) == 'ng'] <- 'van_der_Wijst_v2'
  colnames(summary_stat_filteres_par)[ colnames(summary_stat_filteres_par) == 'v21m'] <- 'Oelen_v2'
  colnames(summary_stat_filteres_par)[ colnames(summary_stat_filteres_par) == 'v31m'] <- 'Oelen_v3'
  
  summary_stat_filteres_par <- summary_stat_filteres_par[order(as.numeric(summary_stat_filteres_par$van_der_Wijst_v2)), ]
  summary_stat_filteres_par$gene <- 1:length(summary_stat_filteres_par$snpgene)
  data_long <- gather(summary_stat_filteres_par, condition, measurement, 1:5, factor_key=TRUE)
  data_long$measurement <- as.numeric(data_long$measurement)
  print(param)
  #data_long <- data_long[order(data_long$measurement),]
  plot1<- 
    ggplot(data = data_long, aes(x = gene, y = measurement, color = condition)) +  geom_line()  +theme_light()  + 
    scale_x_continuous(name="Gene id")
  
  
  plot2<-ggplot(data = data_long,
                mapping = aes(x = gene, y = measurement, color = condition)) +theme_light() +
    geom_line() +
    facet_grid(rows = vars(condition)) + ggtitle(param) 


  plot3 <- ggplot(data_long, aes(x = measurement, color = condition)) + theme_light() + geom_density(alpha=.3) + 
  scale_x_continuous(name=param, limits=c(0, 6))
  # grid.arrange(plot1,plot2, plot3, ncol = 3)
  # grid.arrange(plot1, plot3, ncol = 2)
  grid.arrange(plot3, plot2, ncol = 2)
  
  #grid.arrange(plot1p,plot2p)
}
get_plot_for_values(summary_stat_filteres,param='mean.donor')
## [1] "mean.donor"

get_plot_for_values(summary_stat_filteres,param='mean.cell')
## [1] "mean.cell"

get_plot_for_values(summary_stat_filteres,param='sd.donor')
## [1] "sd.donor"

get_plot_for_values(summary_stat_filteres,param='sd.cell')
## [1] "sd.cell"

get_plot_for_values(summary_stat_filteres,param='sd_prop')
## [1] "sd_prop"
## Warning: Removed 37 rows containing non-finite values (stat_density).

WMA efficiency estimate 5ds

weights <- read.table(paste(path_with_WMA,'list_of_weighting_ids.tsv',sep=''), sep='\t', header=T)
weights$x
##  [1] "mean.donor"                  "sd.donor"                   
##  [3] "variance.donor"              "cv.donor"                   
##  [5] "mean_log.donor"              "sd_log.donor"               
##  [7] "variance_log.donor"          "cv_log.donor"               
##  [9] "mean.cell"                   "sd.cell"                    
## [11] "variance.cell"               "cv.cell"                    
## [13] "zeros.cell"                  "mean_log.cell"              
## [15] "sd_log.cell"                 "variance_log.cell"          
## [17] "cv_log.cell"                 "zeros_log.cell"             
## [19] "avr_SD_donor"                "inv_variance.cell"          
## [21] "inv_variance.donor"          "mean_donor_L_varience_donor"
## [23] "mean_cell_L_varience_cell"   "mean_donor_L_variance_cell" 
## [25] "mean_cell_L_variance_donor"  "inv_sd.cell"                
## [27] "inv_sd.donor"                "mean_donor_L_sd_donor"      
## [29] "mean_cell_L_sd_cell"         "mean_donor_L_sd_cell"       
## [31] "mean_cell_L_sd_donor"        "sd_prop"                    
## [33] "mean_cell_sd_prop"           "mean_cell_L_sd_prop"        
## [35] "mean_donor_sd_prop"          "mean_donor_L_sd_prop"       
## [37] "sd_prop_L_mean_cell"
dataset_characteristics <- read.table(paste(path_with_WMA,'PBMC_dataset_specifications.csv',sep=''), sep=',', header=T)
datatable(dataset_characteristics,extensions = "Buttons", 
            options = list(paging = TRUE,
                           scrollX=TRUE, 
                           searching = TRUE,
                           ordering = TRUE,
                           dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf') ))

Testing if the differences between summary statistics is significant

t.test(tab$OverallZScore_stemi_v3,tab$OverallZScore_stemi_v2,alternative="two.sided", conf.level=0.95)
## 
##  Welch Two Sample t-test
## 
## data:  tab$OverallZScore_stemi_v3 and tab$OverallZScore_stemi_v2
## t = 6.4791, df = 468306, p-value = 9.235e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01462705 0.02731466
## sample estimates:
##    mean of x    mean of y 
##  0.019233233 -0.001737625
t.test(tab$OverallZScore_v2_1m,tab$OverallZScore_v3_1m,alternative="two.sided", conf.level=0.95)
## 
##  Welch Two Sample t-test
## 
## data:  tab$OverallZScore_v2_1m and tab$OverallZScore_v3_1m
## t = -0.026983, df = 468730, p-value = 0.9785
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.006931284  0.006743032
## sample estimates:
##    mean of x    mean of y 
## -0.007809981 -0.007715855
t.test(tab$OverallZScore_v2_1m,tab$ng,alternative="two.sided", conf.level=0.95)
## 
##  One Sample t-test
## 
## data:  tab$OverallZScore_v2_1m
## t = -3.1317, df = 234478, p-value = 0.001738
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.012697905 -0.002922057
## sample estimates:
##    mean of x 
## -0.007809981
t.test(tab$OverallZScore_v3_1m,tab$ng,alternative="two.sided", conf.level=0.95)
## 
##  One Sample t-test
## 
## data:  tab$OverallZScore_v3_1m
## t = -3.1633, df = 234483, p-value = 0.00156
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  -0.012496563 -0.002935147
## sample estimates:
##    mean of x 
## -0.007715855
t.test(tab$OverallZScore_v2_1m,tab$OverallZScore_stemi_v2,alternative="two.sided", conf.level=0.95)
## 
##  Welch Two Sample t-test
## 
## data:  tab$OverallZScore_v2_1m and tab$OverallZScore_stemi_v2
## t = -1.8087, df = 463958, p-value = 0.07049
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0126524541  0.0005077419
## sample estimates:
##    mean of x    mean of y 
## -0.007809981 -0.001737625
t.test(tab$OverallZScore_v3_1m,tab$OverallZScore_stemi_v3,alternative="two.sided", conf.level=0.95)
## 
##  Welch Two Sample t-test
## 
## data:  tab$OverallZScore_v3_1m and tab$OverallZScore_stemi_v3
## t = -7.9908, df = 467934, p-value = 1.344e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.03355915 -0.02033903
## sample estimates:
##    mean of x    mean of y 
## -0.007715855  0.019233233

Weights and weighting characteristics

Check what it the most optimal weighting using n eQTLs BH-corrected

library(DT)
path_with_WMA <- '/Users/korshe/Documents/Data_Groningen/pbmc_5ds/'

BH <- read.table(paste(path_with_WMA,'BH','.tsv',sep=''), sep='\t', header=T)
BH <- BH[order(BH$eQTLs),]
BH <- BH[,c(10,11,1:9)]
DT::datatable(BH,extensions = "Buttons", 
            options = list(paging = TRUE,
                           scrollX=TRUE, 
                           searching = TRUE,
                           ordering = TRUE,
                           dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf') ))

Check what it the most optimal weighting using n eQTLs Bonferroni-corrected

Bonf <- read.table(paste(path_with_WMA,'Bonf','.tsv',sep=''), sep='\t', header=T)
Bonf <- Bonf[order(Bonf$eQTLs),]
Bonf <- Bonf[,c(10,11,1:9)]
DT::datatable(Bonf,extensions = "Buttons", 
            options = list(paging = TRUE,
                           scrollX=TRUE, 
                           searching = TRUE,
                           ordering = TRUE,
                           dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf') ))
library(viridis)
## Loading required package: viridisLite
tab <- tab[complete.cases(tab),]
get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

tab$density <- get_density(tab$ZW_weight_ss, tab$OverallZScore.eqtlgen_ng, n = 105)

ggplot(tab) + geom_point(aes(tab$ZW_weight_ss, tab$OverallZScore.eqtlgen_ng, color = density))  + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank(),axis.line = element_line(colour = "grey")) +  labs(y = "ZScore from eQTLGen", x = "Weighted ZScore (SS)")  +  scale_color_viridis()
## Warning: Use of `tab$ZW_weight_ss` is discouraged. Use `ZW_weight_ss` instead.
## Warning: Use of `tab$OverallZScore.eqtlgen_ng` is discouraged. Use
## `OverallZScore.eqtlgen_ng` instead.

ggplot(tab) + geom_point(aes(tab$ZW_weight_SE, tab$OverallZScore.eqtlgen_ng, color = density))  + theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),panel.background = element_blank(),axis.line = element_line(colour = "grey")) +  labs(y = "ZScore from eQTLGen", x = "Weighted ZScore (SE)")  +  scale_color_viridis()
## Warning: Use of `tab$ZW_weight_SE` is discouraged. Use `ZW_weight_SE` instead.
## Use of `tab$OverallZScore.eqtlgen_ng` is discouraged. Use `OverallZScore.eqtlgen_ng` instead.

WMA estimate for pairwise weighting

path_wma_pairwise <- '/Users/korshe/Documents/Data_Groningen/pbmc_pairwise_2022/ALL/'
datset_combination = 'ng_vs_stemi_v2'

tab_prep <- function(path_wma_pairwise, datset_combination){
ng_stemi_v2 <- read.table(paste(path_wma_pairwise,'ALL_',datset_combination,'.tsv',sep=''), sep='\t', header=T)
ng_stemi_v2= ng_stemi_v2[order(ng_stemi_v2$Bonf_genes,ng_stemi_v2$Bonf_TP,decreasing = c(T,T)),]
ng_stemi_v2["rank_genes"] = c(1:nrow(ng_stemi_v2))
ng_stemi_v2$weight <- paste(ng_stemi_v2$ss, gsub('[0-9]+', '', rownames(ng_stemi_v2) ), sep='_')
return(ng_stemi_v2)
}

Between chemistries

ng_vs_stemi_v2 <- tab_prep(path_wma_pairwise, datset_combination)

datatable(ng_vs_stemi_v2[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))
ng_vs_v2_1m <- tab_prep(path_wma_pairwise, datset_combination='ng_vs_v2_1m')

datatable(ng_vs_v2_1m[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))
stemi_v3_vs_v3_1m <- tab_prep(path_wma_pairwise, datset_combination='stemi_v3_vs_v3_1m')

datatable(stemi_v3_vs_v3_1m[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))
#library(matrixStats)
inter_chemistries_agr_rank <- merge(ng_vs_stemi_v2[,c('weight','rank_genes') ],ng_vs_v2_1m[,c('weight','rank_genes')],by= 'weight')
inter_chemistries_agr_rank <- merge(inter_chemistries_agr_rank,stemi_v3_vs_v3_1m[,c('weight','rank_genes')],by= 'weight')
inter_chemistries_agr_rank$weight <- gsub('.tsv', '', inter_chemistries_agr_rank$weight )
rownames(inter_chemistries_agr_rank) <- inter_chemistries_agr_rank$weight
inter_chemistries_agr_rank$weight <- NULL
inter_chemistries_agr_rank$agregated_rank <- rowSums(inter_chemistries_agr_rank)
inter_chemistries_agr_rank <- inter_chemistries_agr_rank[order(inter_chemistries_agr_rank$agregated_rank),]

datatable(inter_chemistries_agr_rank,extensions = "Buttons", 
            options = list(paging = TRUE,
                           scrollX=TRUE, 
                           searching = TRUE,
                           ordering = TRUE,
                           dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf') ))

Between chemistries

stemi_v3_vs_stemi_v2 <- tab_prep(path_wma_pairwise, datset_combination='stemi_v3_vs_stemi_v2')

datatable(stemi_v3_vs_stemi_v2[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))
ng_vs_v3_1m <- tab_prep(path_wma_pairwise, datset_combination='ng_vs_v3_1m')

datatable(ng_vs_v3_1m[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))
v3_1m_vs_v2_1m <- tab_prep(path_wma_pairwise, datset_combination='v3_1m_vs_v2_1m')

datatable(v3_1m_vs_v2_1m[,c(1:2,4,6, 10,11)],extensions = "Buttons", 
            options = list(buttons = c('copy', 'csv', 'excel', 'pdf') ))

Getting aggregated rank

#library(matrixStats)
between_chemistries_agr_rank <- merge(v3_1m_vs_v2_1m[,c('weight','rank_genes') ],ng_vs_v3_1m[,c('weight','rank_genes')],by= 'weight')
between_chemistries_agr_rank <- merge(between_chemistries_agr_rank,stemi_v3_vs_stemi_v2[,c('weight','rank_genes')],by= 'weight')
between_chemistries_agr_rank$weight <- gsub('.tsv', '', between_chemistries_agr_rank$weight )
rownames(between_chemistries_agr_rank) <- between_chemistries_agr_rank$weight
between_chemistries_agr_rank$weight <- NULL
between_chemistries_agr_rank$agregated_rank <- rowSums(between_chemistries_agr_rank)
between_chemistries_agr_rank <- between_chemistries_agr_rank[order(between_chemistries_agr_rank$agregated_rank),]

datatable(between_chemistries_agr_rank,extensions = "Buttons", 
            options = list(paging = TRUE,
                           scrollX=TRUE, 
                           searching = TRUE,
                           ordering = TRUE,
                           dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel', 'pdf') ))

WMA on subset of genes

For genes with high GC top three optimal WMA weights are XXX, XXX, XXX.

path_summary <- '/Users/korshe/Documents/Data_Groningen/pbmc_pairwise_2022/subset_of_genes/'
dataset_combination <- 'v2_1m_vs_v3_1m'
parameter <- 'length'

top_param_high <- function(path_summary, dataset_combination, parameter){
  high_tab <- read.table(paste(path_summary,'1_Total_summary_',dataset_combination,'Sample.size.',parameter,'.donor_HIGHT.tsv',sep=''), sep='\t', header=T)
  high_tab_t <- as.data.frame(t(high_tab))
  colnames(high_tab_t) <- high_tab_t[1,]
  high_tab_t<- high_tab_t[-1,]
  
  high_tab_t <- high_tab_t[order(high_tab_t$BH_SS_eQTLs, decreasing = T),]
high_tab_t_3 <- high_tab_t[1:5,c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')]
high_tab_t_SS <- high_tab_t[rownames(high_tab_t)=="Sample.size.",c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')]
high_tab_t_SE <- high_tab_t[rownames(high_tab_t)=="Sample.size.",c('BH_SE_genes','BH_SE_eQTLs','cor_SE_all','cor_SE_sign')]
rownames(high_tab_t_SE) <- "SE"
colnames(high_tab_t_SE) <-c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')
high_tab_t_3 <- rbind(high_tab_t_3,high_tab_t_SS)
high_tab_t_3 <- rbind(high_tab_t_3,high_tab_t_SE)
knitr::kable(high_tab_t_3,  caption = paste(dataset_combination, 'WMA of subset of genes with high', parameter))
}
top_param_low <- function(path_summary, dataset_combination, parameter){
  high_tab <- read.table(paste(path_summary,'1_Total_summary_',dataset_combination,'Sample.size.',parameter,'.donor_LOW.tsv',sep=''), sep='\t', header=T)
  high_tab_t <- as.data.frame(t(high_tab))
  colnames(high_tab_t) <- high_tab_t[1,]
  high_tab_t<- high_tab_t[-1,]
  
  high_tab_t <- high_tab_t[order(high_tab_t$BH_SS_eQTLs, decreasing = T),]
high_tab_t_3 <- high_tab_t[1:5,c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')]
high_tab_t_SS <- high_tab_t[rownames(high_tab_t)=="Sample.size.",c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')]
high_tab_t_SE <- high_tab_t[rownames(high_tab_t)=="Sample.size.",c('BH_SE_genes','BH_SE_eQTLs','cor_SE_all','cor_SE_sign')]
rownames(high_tab_t_SE) <- "SE"
colnames(high_tab_t_SE) <-c('BH_SS_genes','BH_SS_eQTLs','cor_SS_all','cor_SS_sign')
high_tab_t_3 <- rbind(high_tab_t_3,high_tab_t_SS)
high_tab_t_3 <- rbind(high_tab_t_3,high_tab_t_SE)
knitr::kable(high_tab_t_3,  caption = paste(dataset_combination, 'WMA of subset of genes with low', parameter))
}

1M v2 & v3

top_param_high(path_summary, dataset_combination, parameter='length')
v2_1m_vs_v3_1m WMA of subset of genes with high length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_sd_donor 12.0000000 105.0000000 0.3830421 0.5124326
mean_cell_L_sd_donor 12.0000000 105.0000000 0.3827140 0.5114275
mean_cell_L_sd_prop 12.0000000 105.0000000 0.3827140 0.5114275
mean_donor_L_sd_prop 12.0000000 105.0000000 0.3830421 0.5124326
sd_prop 13.0000000 102.0000000 0.3849117 0.5409461
Sample.size. 13.0000000 101.0000000 0.3912639 0.5482947
SE 12.0000000 102.0000000 0.3884130 0.5626165 
top_param_low(path_summary, dataset_combination, parameter='length')
v2_1m_vs_v3_1m WMA of subset of genes with low length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_cell_L_sd_cell 17.0000000 182.0000000 0.3446227 0.8886137
mean_donor_L_sd_cell 17.0000000 181.0000000 0.3461420 0.8884718
sd_log.cell 17.0000000 165.0000000 0.3336581 0.8836933
sd.cell 16.0000000 164.0000000 0.3303284 0.8789485
avr_SD_donor 16.0000000 161.0000000 0.3419979 0.8676182
Sample.size. 15.0000000 141.0000000 0.3636316 0.9043358
SE 13.0000000 131.0000000 0.3693670 0.9068768 
top_param_high(path_summary, dataset_combination, parameter='gc')
v2_1m_vs_v3_1m WMA of subset of genes with high gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
sd_prop 9.0000000 40.0000000 0.3189122 0.9176581
zeros_log.cell 8.0000000 39.0000000 0.3247360 0.9213615
Sample.size. 7.0000000 37.0000000 0.3268219 0.9501444
mean_donor_L_varience_donor 7.0000000 35.0000000 0.2969538 0.8942915
mean_cell_L_variance_donor 7.0000000 35.0000000 0.2967622 0.8946662
Sample.size.1 7.0000000 37.0000000 0.3268219 0.9501444
SE 3.0000000 5.0000000 0.3269741 0.9481789 
top_param_low(path_summary, dataset_combination, parameter='gc')
v2_1m_vs_v3_1m WMA of subset of genes with low gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
cv_log.cell 48.0000000 1418.0000000 0.5107185 0.8048854
mean_cell_L_varience_cell 47.0000000 1415.0000000 0.5125610 0.8121256
mean_donor_L_variance_cell 47.0000000 1399.0000000 0.5122781 0.8159857
cv.cell 48.0000000 1360.0000000 0.5036711 0.8115379
cv_log.donor 47.0000000 1250.0000000 0.4996034 0.8399226
Sample.size. 49.0000000 1238.0000000 0.5167595 0.8462178
SE 48.0000000 1385.0000000 0.5138727 0.8058381 
top_param_high(path_summary, dataset_combination, parameter='mean')
v2_1m_vs_v3_1m WMA of subset of genes with high mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
variance.cell 26.0000000 489.0000000 0.2588198 0.4702910
variance_log.cell 24.0000000 481.0000000 0.2563483 0.4639215
mean_log.cell 23.0000000 474.0000000 0.2598463 0.4724762
mean_log.donor 23.0000000 472.0000000 0.2598666 0.4717395
mean.donor 23.0000000 461.0000000 0.2653893 0.4956494
Sample.size. 21.0000000 415.0000000 0.2412562 0.4468668
SE 23.0000000 429.0000000 0.2338139 0.4278087 
top_param_low(path_summary, dataset_combination, parameter='mean')
v2_1m_vs_v3_1m WMA of subset of genes with low mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
cv_log.donor 4.0000000 19.0000000 0.2364163 0.9426668
mean_cell_L_varience_cell 4.0000000 19.0000000 0.2682027 0.9398309
cv_log.cell 4.0000000 16.0000000 0.2670292 0.8773096
Sample.size. 3.0000000 15.0000000 0.2939085 0.7949185
zeros_log.cell 3.0000000 15.0000000 0.2878997 0.7890365
Sample.size.1 3.0000000 15.0000000 0.2939085 0.7949185
SE 3.0000000 11.0000000 0.2792001 0.8228778 
top_param_high(path_summary, dataset_combination, parameter='sd')
v2_1m_vs_v3_1m WMA of subset of genes with high sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_sd_donor 21.0000000 396.0000000 0.3469961 0.6797311
mean_cell_L_sd_donor 21.0000000 396.0000000 0.3468789 0.6798414
mean_cell_L_sd_prop 21.0000000 396.0000000 0.3468789 0.6798414
mean_donor_L_sd_prop 21.0000000 396.0000000 0.3469961 0.6797311
mean_log.donor 18.0000000 394.0000000 0.3392858 0.6630351
Sample.size. 19.0000000 367.0000000 0.3383879 0.6794109
SE 22.0000000 373.0000000 0.3315468 0.6615580 
top_param_low(path_summary, dataset_combination, parameter='sd')
v2_1m_vs_v3_1m WMA of subset of genes with low sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 0.0000000 0.0000000 0.2464812 0.0000000
mean.donor 0.0000000 0.0000000 0.3289821 0.0000000
sd.donor 0.0000000 0.0000000 0.3271387 0.0000000
variance.donor 0.0000000 0.0000000 0.3314561 0.0000000
cv.donor 0.0000000 0.0000000 0.1657964 0.0000000
Sample.size.1 0.0000000 0.0000000 0.2464812 0.0000000
SE 0.0000000 0.0000000 0.2271203 0.0000000

NG v2 & 1M v3

dataset_combination ='ng_vs_v3_1m'
top_param_high(path_summary, dataset_combination, parameter='length')
ng_vs_v3_1m WMA of subset of genes with high length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 9.0000000 48.0000000 0.3317558 0.9455553
zeros.cell 9.0000000 48.0000000 0.3339620 0.9428702
zeros_log.cell 9.0000000 48.0000000 0.3322678 0.9454301
sd_prop 9.0000000 47.0000000 0.3267121 0.9465538
mean_donor_L_variance_cell 9.0000000 46.0000000 0.3240715 0.9469643
Sample.size.1 9.0000000 48.0000000 0.3317558 0.9455553
SE 9.0000000 47.0000000 0.3287993 0.9491593 
top_param_low(path_summary, dataset_combination, parameter='length')
ng_vs_v3_1m WMA of subset of genes with low length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_log.donor 11.0000000 79.0000000 0.2617560 0.8558849
sd.cell 12.0000000 78.0000000 0.2748296 0.8664076
mean_log.cell 11.0000000 77.0000000 0.2614956 0.8463045
sd_log.cell 12.0000000 77.0000000 0.2771293 0.8672397
zeros.cell 12.0000000 76.0000000 0.2855283 0.8775576
Sample.size. 10.0000000 47.0000000 0.2994051 0.8635707
SE 11.0000000 54.0000000 0.3064150 0.8584953 
top_param_high(path_summary, dataset_combination, parameter='gc')
ng_vs_v3_1m WMA of subset of genes with high gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 4.0000000 4.0000000 0.3413259 0.0000000
mean.donor 4.0000000 4.0000000 0.3107390 0.0000000
sd.donor 4.0000000 4.0000000 0.3205555 0.0000000
cv.donor 4.0000000 4.0000000 0.3308767 0.0000000
mean_log.donor 4.0000000 4.0000000 0.3113296 0.0000000
Sample.size.1 4.0000000 4.0000000 0.3413259 0.0000000
SE 4.0000000 4.0000000 0.3472766 0.0000000 
top_param_low(path_summary, dataset_combination, parameter='gc')
ng_vs_v3_1m WMA of subset of genes with low gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_sd_donor 35.0000000 949.0000000 0.4795445 0.7801512
mean_donor_L_sd_prop 35.0000000 949.0000000 0.4795445 0.7801512
mean_cell_L_sd_donor 35.0000000 946.0000000 0.4788480 0.7796632
mean_cell_L_sd_prop 35.0000000 946.0000000 0.4788480 0.7796632
zeros_log.cell 34.0000000 945.0000000 0.4729856 0.7750755
Sample.size. 33.0000000 944.0000000 0.4735239 0.7756094
SE 38.0000000 967.0000000 0.4708052 0.7702254 
top_param_high(path_summary, dataset_combination, parameter='mean')
ng_vs_v3_1m WMA of subset of genes with high mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
inv_variance.cell 20.0000000 564.0000000 0.1882468 0.5218753
inv_sd.donor 20.0000000 525.0000000 0.1932009 0.5618353
mean_donor_L_varience_donor 19.0000000 519.0000000 0.1953610 0.5596007
mean_cell_L_variance_donor 18.0000000 505.0000000 0.1956957 0.5670303
cv.cell 19.0000000 490.0000000 0.1920465 0.5370476
Sample.size. 16.0000000 470.0000000 0.2157812 0.5490202
SE 18.0000000 472.0000000 0.2196725 0.5730657 
top_param_low(path_summary, dataset_combination, parameter='mean')
ng_vs_v3_1m WMA of subset of genes with low mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean.donor 3.0000000 12.0000000 0.3216376 0.9309261
variance.donor 3.0000000 12.0000000 0.3284211 0.9298793
mean_log.donor 3.0000000 12.0000000 0.3216493 0.9309324
variance_log.donor 3.0000000 12.0000000 0.3284211 0.9298793
variance.cell 3.0000000 12.0000000 0.3151593 0.9303253
Sample.size. 2.0000000 6.0000000 0.3416197 0.9999045
SE 1.0000000 1.0000000 0.3438839 0.0000000 
top_param_high(path_summary, dataset_combination, parameter='sd')
ng_vs_v3_1m WMA of subset of genes with high sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
cv_log.cell 24.0000000 684.0000000 0.3470991 0.7124770
cv.cell 26.0000000 678.0000000 0.3398562 0.7139733
inv_sd.cell 25.0000000 673.0000000 0.3427132 0.7215641
Sample.size. 25.0000000 672.0000000 0.3640568 0.7235983
mean_donor_L_varience_donor 26.0000000 666.0000000 0.3325521 0.7317342
Sample.size.1 25.0000000 672.0000000 0.3640568 0.7235983
SE 23.0000000 659.0000000 0.3659004 0.7381223 
top_param_low(path_summary, dataset_combination, parameter='sd')
ng_vs_v3_1m WMA of subset of genes with low sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean.donor 3.0000000 12.0000000 0.3201567 0.9309261
variance.donor 3.0000000 12.0000000 0.3233939 0.9298793
mean_log.donor 3.0000000 12.0000000 0.3201662 0.9309324
variance_log.donor 3.0000000 12.0000000 0.3233939 0.9298793
variance.cell 3.0000000 12.0000000 0.3158997 0.9303253
Sample.size. 2.0000000 6.0000000 0.3263891 0.9999045
SE 1.0000000 1.0000000 0.3302035 0.0000000 
#parameters <- c('gc','length','mean','sd')

NG v2 & STEMI v2

dataset_combination ='ng_vs_stemi_v2'
top_param_high(path_summary, dataset_combination, parameter='length')
ng_vs_stemi_v2 WMA of subset of genes with high length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
variance.donor 5.0000000 26.0000000 0.2432222 0.7875336
variance_log.donor 5.0000000 26.0000000 0.2432313 0.7875200
zeros.cell 5.0000000 26.0000000 0.2589024 0.8261392
cv_log.cell 5.0000000 26.0000000 0.2630279 0.9357856
mean_cell_L_varience_cell 5.0000000 26.0000000 0.2501131 0.8327964
Sample.size. 4.0000000 25.0000000 0.2625132 0.9304219
SE 5.0000000 26.0000000 0.2597212 0.8299394 
top_param_low(path_summary, dataset_combination, parameter='length')
ng_vs_stemi_v2 WMA of subset of genes with low length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_varience_donor 3.0000000 8.0000000 0.2934226 0.9656829
mean_donor_L_sd_donor 3.0000000 8.0000000 0.3109583 0.9671971
mean_donor_L_sd_prop 3.0000000 8.0000000 0.3109583 0.9671971
cv.cell 2.0000000 6.0000000 0.3258072 0.9950748
cv_log.cell 2.0000000 6.0000000 0.3271891 0.9951192
Sample.size. 1.0000000 2.0000000 0.3230321 0.0000000
SE 2.0000000 3.0000000 0.3015067 0.0000000 
top_param_high(path_summary, dataset_combination, parameter='gc')
ng_vs_stemi_v2 WMA of subset of genes with high gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
cv.cell 1.0000000 1.0000000 0.2527622 0.0000000
zeros.cell 1.0000000 1.0000000 0.2685889 0.0000000
zeros_log.cell 1.0000000 1.0000000 0.2703194 0.0000000
inv_variance.cell 1.0000000 1.0000000 0.2362931 0.0000000
inv_variance.donor 1.0000000 1.0000000 0.2219016 0.0000000
Sample.size. 0.0000000 0.0000000 0.2702047 0.0000000
SE 1.0000000 1.0000000 0.2627731 0.0000000 
top_param_low(path_summary, dataset_combination, parameter='gc')
ng_vs_stemi_v2 WMA of subset of genes with low gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
inv_sd.donor 9.0000000 373.0000000 0.3511033 0.9427655
cv.cell 9.0000000 370.0000000 0.3426099 0.9429228
cv_log.donor 11.0000000 363.0000000 0.3321501 0.9400661
sd.cell 8.0000000 348.0000000 0.3302741 0.9543884
sd_log.cell 8.0000000 348.0000000 0.3323872 0.9538529
Sample.size. 8.0000000 315.0000000 0.3535123 0.9504660
SE 2.0000000 21.0000000 0.3570769 0.8130366 
top_param_high(path_summary, dataset_combination, parameter='mean')
ng_vs_stemi_v2 WMA of subset of genes with high mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
sd_prop 26.0000000 566.0000000 0.1551807 0.4704803
inv_sd.donor 24.0000000 565.0000000 0.1546213 0.4687628
mean_cell_L_varience_cell 27.0000000 561.0000000 0.1615674 0.4762640
inv_sd.cell 25.0000000 561.0000000 0.1612236 0.4760984
avr_SD_donor 23.0000000 558.0000000 0.1525568 0.4704280
Sample.size. 22.0000000 472.0000000 0.1640899 0.5071582
SE 29.0000000 577.0000000 0.1601348 0.4726863 
top_param_low(path_summary, dataset_combination, parameter='mean')
ng_vs_stemi_v2 WMA of subset of genes with low mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
variance.cell 0.000000e+00 0.000000e+00 9.385707e-02 0.000000e+00
variance_log.cell 0.000000e+00 0.000000e+00 9.387245e-02 0.000000e+00
mean_cell_L_sd_donor 1.0000000 3.0000000 0.1221018 0.0000000
mean_cell_L_sd_prop 1.0000000 3.0000000 0.1221018 0.0000000
Sample.size. 0.0000000 0.0000000 0.1566476 0.0000000
Sample.size.1 0.0000000 0.0000000 0.1566476 0.0000000
SE 0.0000000 0.0000000 0.1696128 0.0000000 
top_param_high(path_summary, dataset_combination, parameter='sd')
ng_vs_stemi_v2 WMA of subset of genes with high sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_sd_donor 26.0000000 589.0000000 0.2857967 0.7401483
mean_donor_L_sd_prop 26.0000000 589.0000000 0.2857967 0.7401483
inv_sd.donor 24.0000000 588.0000000 0.2889890 0.7478492
inv_variance.donor 24.0000000 586.0000000 0.2826482 0.7318523
mean_donor_L_varience_donor 25.0000000 585.0000000 0.2770441 0.7296780
Sample.size. 21.0000000 492.0000000 0.3036785 0.7773670
SE 27.0000000 581.0000000 0.2943974 0.7225017 
top_param_low(path_summary, dataset_combination, parameter='sd')
ng_vs_stemi_v2 WMA of subset of genes with low sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_cell_L_sd_donor 1.0000000 3.0000000 0.1298505 0.0000000
mean_cell_L_sd_prop 1.0000000 3.0000000 0.1298505 0.0000000
Sample.size. 0.0000000 0.0000000 0.1505765 0.0000000
mean.donor 0.0000000 0.0000000 0.1595669 0.0000000
sd.donor 0.0000000 0.0000000 0.1633831 0.0000000
Sample.size.1 0.0000000 0.0000000 0.1505765 0.0000000
SE 0.0000000 0.0000000 0.1610945 0.0000000

NG v2 & 1M v2

dataset_combination ='ng_vs_v2_1m'
top_param_high(path_summary, dataset_combination, parameter='length')
ng_vs_v2_1m WMA of subset of genes with high length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
variance.cell 1.000000e+01 9.800000e+01 3.189260e-01 6.691013e-01
variance_log.cell 1.000000e+01 9.800000e+01 3.191818e-01 6.689081e-01
mean_cell_L_variance_donor 11.0000000 120.0000000 0.3419234 0.6858609
sd_log.cell 11.00000000 119.00000000 0.32801015 0.66921562
mean_donor_L_sd_donor 11.00000000 119.00000000 0.33417925 0.67343220
Sample.size. 9.00000000 115.00000000 0.33444227 0.67846350
SE 13.00000000 93.00000000 0.34774581 0.40433683 
top_param_low(path_summary, dataset_combination, parameter='length')
ng_vs_v2_1m WMA of subset of genes with low length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean.donor 8.0000000 33.0000000 0.3498154 0.9096587
sd.donor 8.0000000 33.0000000 0.3437112 0.9094820
variance.donor 8.0000000 33.0000000 0.3156178 0.9196858
mean_log.donor 8.0000000 33.0000000 0.3501954 0.9095520
sd_log.donor 8.0000000 33.0000000 0.3438656 0.9094445
Sample.size. 6.0000000 15.0000000 0.3725321 0.9362740
SE 7.0000000 36.0000000 0.3832980 0.9399126 
top_param_high(path_summary, dataset_combination, parameter='gc')
ng_vs_v2_1m WMA of subset of genes with high gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 1.0000000 1.0000000 0.2958102 0.0000000
mean.donor 1.0000000 1.0000000 0.2929579 0.0000000
sd.donor 1.0000000 1.0000000 0.2881051 0.0000000
variance.donor 1.0000000 1.0000000 0.2715610 0.0000000
cv.donor 1.0000000 1.0000000 0.2884989 0.0000000
Sample.size.1 1.0000000 1.0000000 0.2958102 0.0000000
SE 1.0000000 1.0000000 0.2870843 0.0000000 
top_param_low(path_summary, dataset_combination, parameter='gc')
ng_vs_v2_1m WMA of subset of genes with low gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
inv_variance.donor 28.0000000 632.0000000 0.4560842 0.8463953
inv_variance.cell 27.00000000 591.00000000 0.46198021 0.90272872
inv_sd.donor 29.00000000 589.00000000 0.46309785 0.86479291
cv.cell 28.00000000 586.00000000 0.46548434 0.86423005
inv_sd.cell 28.00000000 586.00000000 0.46435219 0.86425753
Sample.size. 22.0000000 521.0000000 0.4627987 0.8568584
SE 27.0000000 611.0000000 0.4701973 0.8768237 
top_param_high(path_summary, dataset_combination, parameter='mean')
ng_vs_v2_1m WMA of subset of genes with high mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
cv.donor 25.0000000 472.0000000 0.1670159 0.4316799
cv_log.donor 25.0000000 472.0000000 0.1685668 0.4325850
cv.cell 24.0000000 472.0000000 0.1732926 0.4391895
sd_prop_L_mean_cell 25.0000000 472.0000000 0.1671250 0.4314487
inv_variance.cell 25.0000000 471.0000000 0.1753585 0.4618821
Sample.size. 21.0000000 457.0000000 0.1792590 0.4667053
SE 24.0000000 518.0000000 0.1870202 0.4226345 
top_param_low(path_summary, dataset_combination, parameter='mean')
ng_vs_v2_1m WMA of subset of genes with low mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 1.0000000 2.0000000 0.2993830 0.0000000
mean.donor 1.0000000 2.0000000 0.2958014 0.0000000
sd.donor 1.0000000 2.0000000 0.2963371 0.0000000
variance.donor 1.0000000 2.0000000 0.2887124 0.0000000
mean_log.donor 1.0000000 2.0000000 0.2958042 0.0000000
Sample.size.1 1.0000000 2.0000000 0.2993830 0.0000000
SE 1.0000000 2.0000000 0.3167615 0.0000000 
top_param_high(path_summary, dataset_combination, parameter='sd')
ng_vs_v2_1m WMA of subset of genes with high sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
sd_prop_L_mean_cell 25.0000000 459.0000000 0.3381884 0.6322244
cv_log.donor 25.0000000 456.0000000 0.3397424 0.6358152
cv.donor 25.0000000 455.0000000 0.3381732 0.6379984
inv_variance.cell 24.0000000 450.0000000 0.3464770 0.6608746
inv_variance.donor 26.0000000 450.0000000 0.3475537 0.6586948
Sample.size. 22.0000000 443.0000000 0.3493605 0.6513082
SE 26.0000000 509.0000000 0.3638315 0.6446366 
top_param_low(path_summary, dataset_combination, parameter='sd')
ng_vs_v2_1m WMA of subset of genes with low sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 1.0000000 2.0000000 0.2834610 0.0000000
mean.donor 1.0000000 2.0000000 0.2855699 0.0000000
sd.donor 1.0000000 2.0000000 0.2843085 0.0000000
variance.donor 1.0000000 2.0000000 0.2785106 0.0000000
mean_log.donor 1.0000000 2.0000000 0.2855714 0.0000000
Sample.size.1 1.0000000 2.0000000 0.2834610 0.0000000
SE 1.0000000 2.0000000 0.3063411 0.0000000

STEMI v3 & 1M v3

dataset_combination ='stemi_v3_vs_v3_1m'
top_param_high(path_summary, dataset_combination, parameter='length')
stemi_v3_vs_v3_1m WMA of subset of genes with high length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_L_variance_cell 5.0000000 26.0000000 0.3166268 0.6195197
mean_donor_L_sd_cell 5.0000000 26.0000000 0.3087805 0.7052360
zeros_log.cell 4.0000000 25.0000000 0.2868697 0.3418463
avr_SD_donor 4.0000000 25.0000000 0.2789740 0.3607516
mean_cell_L_varience_cell 4.0000000 25.0000000 0.2995581 0.3817209
Sample.size. 4.0000000 23.0000000 0.2805184 0.3170239
SE 2.0000000 12.0000000 0.3168434 -0.3777836 
top_param_low(path_summary, dataset_combination, parameter='length')
stemi_v3_vs_v3_1m WMA of subset of genes with low length
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
zeros.cell 8.0000000 85.0000000 0.2474593 0.7900599
variance.donor 6.0000000 55.0000000 0.2689472 0.8955783
variance_log.donor 6.0000000 55.0000000 0.2689796 0.8955982
sd.donor 6.0000000 52.0000000 0.2643972 0.8907365
sd_log.donor 6.0000000 46.0000000 0.2643250 0.8825832
Sample.size. 1.0000000 5.0000000 0.2403568 0.7811554
SE 5.0000000 49.0000000 0.2304147 0.9725329 
top_param_high(path_summary, dataset_combination, parameter='gc')
stemi_v3_vs_v3_1m WMA of subset of genes with high gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_sd_prop 6.0000000 44.0000000 0.2653018 0.9232801
Sample.size. 4.0000000 41.0000000 0.2154790 0.9158282
mean.cell 4.0000000 41.0000000 0.2154350 0.9214766
sd.cell 4.0000000 41.0000000 0.2080846 0.9150221
variance.cell 4.0000000 41.0000000 0.2004603 0.9141595
Sample.size.1 4.0000000 41.0000000 0.2154790 0.9158282
SE 0.0000000 0.0000000 0.2729563 0.0000000 
top_param_low(path_summary, dataset_combination, parameter='gc')
stemi_v3_vs_v3_1m WMA of subset of genes with low gc
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
sd_prop_L_mean_cell 25.0000000 795.0000000 0.4212361 0.8525919
inv_sd.cell 24.0000000 776.0000000 0.4453924 0.8451554
inv_variance.cell 24.0000000 773.0000000 0.4460672 0.8399818
mean_cell_L_varience_cell 25.0000000 735.0000000 0.4555651 0.8645287
mean_donor_L_variance_cell 25.0000000 721.0000000 0.4759422 0.8568491
Sample.size. 12.0000000 166.0000000 0.4419067 0.8739260
SE 24.0000000 734.0000000 0.4678853 0.8054725 
top_param_high(path_summary, dataset_combination, parameter='mean')
stemi_v3_vs_v3_1m WMA of subset of genes with high mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
variance.donor 2.000000e+00 3.000000e+01 2.247827e-01 9.809085e-02
zeros.cell 8.0000000 142.0000000 0.2767575 0.5049588
mean_donor_L_sd_donor 8.0000000 131.0000000 0.2909270 0.4776824
mean_donor_L_sd_prop 8.0000000 131.0000000 0.2909270 0.4776824
mean_donor_L_variance_cell 8.0000000 129.0000000 0.2784009 0.4515029
Sample.size. 1.0000000 29.0000000 0.2698677 -0.3928951
SE 5.0000000 101.0000000 0.2494954 0.3133391 
top_param_low(path_summary, dataset_combination, parameter='mean')
stemi_v3_vs_v3_1m WMA of subset of genes with low mean
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
mean_donor_sd_prop 1.0000000 9.0000000 0.2772362 0.6512968
mean.donor 1.0000000 4.0000000 0.2671629 0.0000000
sd.donor 1.0000000 4.0000000 0.2611781 0.0000000
variance.donor 1.0000000 4.0000000 0.2785881 0.0000000
mean_log.donor 1.0000000 4.0000000 0.2671462 0.0000000
Sample.size. 0.0000000 0.0000000 0.2148669 0.0000000
SE 1.0000000 9.0000000 0.2422744 0.6512968 
top_param_high(path_summary, dataset_combination, parameter='sd')
stemi_v3_vs_v3_1m WMA of subset of genes with high sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
zeros.cell 8.0000000 88.0000000 0.3626497 0.9362700
inv_variance.cell 7.0000000 79.0000000 0.3426494 0.9268460
mean_donor_L_variance_cell 8.0000000 78.0000000 0.3656688 0.9437620
mean_donor_L_sd_cell 7.0000000 74.0000000 0.3643666 0.9248328
mean_donor_L_varience_donor 6.0000000 72.0000000 0.3884618 0.9312092
Sample.size. 1.0000000 10.0000000 0.3403024 0.9992667
SE 7.0000000 82.0000000 0.3436007 0.9381732 
top_param_low(path_summary, dataset_combination, parameter='sd')
stemi_v3_vs_v3_1m WMA of subset of genes with low sd
BH_SS_genes BH_SS_eQTLs cor_SS_all cor_SS_sign
Sample.size. 0.0000000 0.0000000 0.1644582 0.0000000
mean.donor 0.0000000 0.0000000 0.1866382 0.0000000
sd.donor 0.0000000 0.0000000 0.1918760 0.0000000
variance.donor 0.0000000 0.0000000 0.1953173 0.0000000
cv.donor 0.0000000 0.0000000 0.1573711 0.0000000
Sample.size.1 0.0000000 0.0000000 0.1644582 0.0000000
SE 0.0000000 0.0000000 0.1309707 0.0000000