added predictions for ps and lig and output to results
This commit is contained in:
parent
a77b472dfa
commit
8a6c7968f5
4 changed files with 842 additions and 255 deletions
|
|
@ -1,255 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: prediction
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts")
|
||||
getwd()
|
||||
|
||||
source("combining_dfs_plotting.R")
|
||||
#source("plotting/corr_data.R")
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions
|
||||
####################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
|
||||
table(merged_df2$mutation_info)
|
||||
merged_df2$mutation_info_labels = ifelse(merged_df2$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df2$mutation_info_labels)
|
||||
|
||||
|
||||
table(merged_df3$mutation_info)
|
||||
merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3$mutation_info_labels)
|
||||
|
||||
|
||||
# lig
|
||||
table(merged_df3$mutation_info)
|
||||
merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3$mutation_info_labels)
|
||||
|
||||
|
||||
table(merged_df3$mutation_info)
|
||||
merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3$mutation_info_labels)
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
###############################################################################
|
||||
model_ind = glm(mutation_info_labels ~ or_mychisq
|
||||
, data = merged_df2
|
||||
, family = "binomial")
|
||||
summary(model_ind)
|
||||
nobs(model_ind)
|
||||
|
||||
#=============
|
||||
# try loop
|
||||
#=============
|
||||
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af", "duet_stability_change", "duet_scaled")
|
||||
|
||||
|
||||
for( i in my_ivs){
|
||||
cat ("===============================\n")
|
||||
cat(i)
|
||||
cat ("\n===============================\n")
|
||||
print(summary(glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df2
|
||||
, family = "binomial")))
|
||||
}
|
||||
###############################################################################
|
||||
model1 = glm(mutation_info_labels ~ or_mychisq
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model1)
|
||||
|
||||
model2 = glm(mutation_info_labels ~ or_kin
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model2)
|
||||
|
||||
model3 = glm(mutation_info_labels ~ pval_fisher
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model3)
|
||||
|
||||
model4 = glm(mutation_info_labels ~ af
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model4)
|
||||
|
||||
model5 = glm(mutation_info_labels ~ duet_stability_change
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model5)
|
||||
|
||||
model6 = glm(mutation_info_labels ~ duet_scaled
|
||||
, data = merged_df2
|
||||
, family = "binomial"); summary(model6)
|
||||
###############################################################################
|
||||
#========================================
|
||||
# loop and output in df: individually
|
||||
#========================================
|
||||
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "duet_stability_change"
|
||||
, "duet_scaled"
|
||||
, "duet_outcome"
|
||||
, "ddg"
|
||||
, "foldx_scaled"
|
||||
, "foldx_outcome")
|
||||
|
||||
ps_logistic_df2 = data.frame()
|
||||
|
||||
for( i in my_ivs){
|
||||
print(i)
|
||||
|
||||
df = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
model = glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df2
|
||||
, family = "binomial")
|
||||
|
||||
var_name = i
|
||||
|
||||
number_samples = nobs(model)
|
||||
|
||||
beta_logistic = summary(model)$coefficients[2,1]; beta_logistic
|
||||
|
||||
or_logistic = exp(summary(model)$coefficients[2,1])
|
||||
|
||||
pval_logistic = summary(model)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
print(df)
|
||||
ps_logistic_df2 = rbind(ps_logistic_df2, df)
|
||||
|
||||
}
|
||||
#--------------------
|
||||
# formatting df
|
||||
#--------------------
|
||||
ps_logistic_df2$data_source = "df2"
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_df2$pvalue_signif = ps_logistic_df2$pvalue
|
||||
str(ps_logistic_df2$pvalue_signif)
|
||||
|
||||
ps_logistic_df2 = dplyr::mutate(ps_logistic_df2
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
###############################################################################
|
||||
#========================================
|
||||
# loop and output in df: adjusted
|
||||
#========================================
|
||||
my_ivs = c("or_mychisq"
|
||||
, "rsa", "rd_values", "kd_values"
|
||||
, "duet_stability_change", "duet_scaled", "duet_outcome"
|
||||
, "ddg", "foldx_scaled", "foldx_outcome")
|
||||
|
||||
model_adjusted = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
|
||||
duet_stability_change + duet_scaled + duet_outcome + ddg + foldx_scaled + foldx_outcome
|
||||
, data = merged_df2
|
||||
, family = "binomial")
|
||||
|
||||
var_names = model_adjusted$terms
|
||||
number_samples = nobs(model_adjusted)
|
||||
summary(model_adjusted)
|
||||
ci_mod = exp(confint(model_adjusted))
|
||||
|
||||
|
||||
my_ivs_adjusted = names(model_adjusted$coefficients)
|
||||
|
||||
ps_logistic_adjusted_df2 = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
|
||||
for (i in my_ivs_adjusted){
|
||||
print(i)
|
||||
|
||||
ps_logistic_adjusted_df2$var_name = i
|
||||
ps_logistic_adjusted_df2$number_samples = number_samples
|
||||
ps_logistic_adjusted_df2$beta = model_adjusted$coefficients[[i]]
|
||||
ps_logistic_adjusted_df2$odds_ratio = exp(model_adjusted$coefficients[[i]])
|
||||
ps_logistic_adjusted_df2$pvalue =
|
||||
ps_logistic_adjusted_df2$se =
|
||||
ps_logistic_adjusted_df2$zvalue =
|
||||
ps_logistic_adjusted_df2$ci_lower = ci_mod[[i]]
|
||||
ps_logistic_adjusted_df2$ci_upper =
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
pval_logistic = summary(model_adjusted)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model_adjusted)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model_adjusted)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model_adjusted))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
426
scripts/prediction_all.R
Normal file
426
scripts/prediction_all.R
Normal file
|
|
@ -0,0 +1,426 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: prediction
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
source("plotting/combining_dfs_plotting.R")
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions
|
||||
####################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# ps
|
||||
table(merged_df2$mutation_info)
|
||||
merged_df2$mutation_info_labels = ifelse(merged_df2$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df2$mutation_info_labels)
|
||||
|
||||
|
||||
table(merged_df3$mutation_info)
|
||||
merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3$mutation_info_labels)
|
||||
|
||||
|
||||
# lig
|
||||
table(merged_df2_lig$mutation_info)
|
||||
merged_df2_lig$mutation_info_labels = ifelse(merged_df2_lig$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df2_lig$mutation_info_labels)
|
||||
|
||||
|
||||
table(merged_df3_lig$mutation_info)
|
||||
merged_df3_lig$mutation_info_labels = ifelse(merged_df3_lig$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3_lig$mutation_info_labels)
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
###############################################################################
|
||||
model_ind = glm(mutation_info_labels ~ or_mychisq
|
||||
, data = merged_df2
|
||||
, family = "binomial")
|
||||
summary(model_ind)
|
||||
nobs(model_ind)
|
||||
|
||||
#=============
|
||||
# try loop
|
||||
#=============
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af", "duet_stability_change", "duet_scaled")
|
||||
|
||||
for( i in my_ivs){
|
||||
cat ("===============================\n")
|
||||
cat(i)
|
||||
cat ("\n===============================\n")
|
||||
print(summary(glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df2
|
||||
, family = "binomial")))
|
||||
}
|
||||
###############################################################################
|
||||
#========================================
|
||||
# merged_df2: UNadjusted,loop
|
||||
#========================================
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
|
||||
, "ligand_distance"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "duet_stability_change"
|
||||
, "duet_scaled"
|
||||
, "duet_outcome"
|
||||
, "ddg"
|
||||
, "foldx_scaled"
|
||||
, "foldx_outcome")
|
||||
|
||||
ps_logistic_df2 = data.frame()
|
||||
|
||||
for( i in my_ivs){
|
||||
print(i)
|
||||
|
||||
df = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
model = glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df2
|
||||
, family = "binomial")
|
||||
|
||||
var_name = i
|
||||
|
||||
number_samples = nobs(model)
|
||||
|
||||
beta_logistic = summary(model)$coefficients[2,1]; beta_logistic
|
||||
|
||||
or_logistic = exp(summary(model)$coefficients[2,1])
|
||||
|
||||
pval_logistic = summary(model)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
print(df)
|
||||
ps_logistic_df2 = rbind(ps_logistic_df2, df)
|
||||
|
||||
}
|
||||
#--------------------
|
||||
# formatting df
|
||||
#--------------------
|
||||
ps_logistic_df2$data_source = "df2"
|
||||
ps_logistic_df2$model = "unadjusted"
|
||||
|
||||
ps_logistic_df2$odds_ratio = round(ps_logistic_df2$odds_ratio, 2)
|
||||
ps_logistic_df2$ci_lower = round(ps_logistic_df2$ci_lower, 2)
|
||||
ps_logistic_df2$ci_upper = round(ps_logistic_df2$ci_upper, 2)
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_df2$pvalue_signif = ps_logistic_df2$pvalue
|
||||
str(ps_logistic_df2$pvalue_signif)
|
||||
|
||||
ps_logistic_df2 = dplyr::mutate(ps_logistic_df2
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
colnames(ps_logistic_df2)
|
||||
ps_logistic_df2_o = ps_logistic_df2 [c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
, "data_source")]
|
||||
|
||||
###############################################################################
|
||||
#========================================
|
||||
# merged_df2: adjusted, loop
|
||||
#========================================
|
||||
#model_adjusted = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
|
||||
# duet_stability_change + duet_scaled + duet_outcome + ddg + foldx_scaled + foldx_outcome
|
||||
# , data = merged_df2
|
||||
# , family = "binomial")
|
||||
|
||||
model_adjusted_df2 = glm(mutation_info_labels ~ or_mychisq + or_kin + rd_values + kd_values +
|
||||
ligand_distance + duet_stability_change
|
||||
, data = merged_df2
|
||||
, family = "binomial");summary(model_adjusted_df2)
|
||||
|
||||
var_names_df = as.data.frame(names(model_adjusted_df2$coefficients))
|
||||
names(var_names_df) = c("var_name")
|
||||
|
||||
ci_mod = exp(confint(model_adjusted_df2))
|
||||
ci_mod_df = as.data.frame(ci_mod)
|
||||
names(ci_mod_df) = c("ci_lower", "ci_upper")
|
||||
ci_mod_df$ci_lower = round(ci_mod_df$ci_lower, 2)
|
||||
ci_mod_df$ci_upper = round(ci_mod_df$ci_upper, 2)
|
||||
|
||||
|
||||
estimates_df = as.data.frame(summary(model_adjusted_df2)$coefficients)
|
||||
names(estimates_df) = c("beta", "se", "zvalue", "pvalue")
|
||||
estimates_df$odds_ratio = round(exp(estimates_df$beta), 2)
|
||||
number_samples = nobs(model_adjusted_df2)
|
||||
estimates_df$number_samples = number_samples
|
||||
estimates_df$data_source = "df2"
|
||||
estimates_df$model = "adjusted"
|
||||
|
||||
names(ps_logistic_adjusted_df2)
|
||||
|
||||
if ( all(rownames(estimates_df) == rownames(ci_mod_df)) ){
|
||||
cat("PASS: rownames match. Preparing to merge...")
|
||||
ps_logistic_adjusted_df2 = merge(estimates_df, ci_mod_df, by = "row.names", all = T)
|
||||
}
|
||||
|
||||
|
||||
colnames(ps_logistic_adjusted_df2)[1] <- c("var_name")
|
||||
d1 = which(ps_logistic_adjusted_df2$var_name == "(Intercept)")
|
||||
ps_logistic_adjusted_df2 = ps_logistic_adjusted_df2[-d1,]
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_adjusted_df2$pvalue_signif = ps_logistic_adjusted_df2$pvalue
|
||||
str(ps_logistic_adjusted_df2$pvalue_signif)
|
||||
|
||||
ps_logistic_adjusted_df2 = dplyr::mutate(ps_logistic_adjusted_df2
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
colnames(ps_logistic_adjusted_df2)
|
||||
ps_logistic_adjusted_df2_o = ps_logistic_adjusted_df2[c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
, "data_source")]
|
||||
|
||||
###############################################################################
|
||||
|
||||
###############################################################################
|
||||
#========================================
|
||||
# merged_df3: UNadjusted,loop
|
||||
#========================================
|
||||
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
|
||||
, "ligand_distance"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "duet_stability_change"
|
||||
, "duet_scaled"
|
||||
, "duet_outcome"
|
||||
, "ddg"
|
||||
, "foldx_scaled"
|
||||
, "foldx_outcome")
|
||||
|
||||
ps_logistic_df3 = data.frame()
|
||||
|
||||
for( i in my_ivs){
|
||||
print(i)
|
||||
|
||||
df = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
model = glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df3
|
||||
, family = "binomial")
|
||||
|
||||
var_name = i
|
||||
|
||||
number_samples = nobs(model)
|
||||
|
||||
beta_logistic = summary(model)$coefficients[2,1]; beta_logistic
|
||||
|
||||
or_logistic = exp(summary(model)$coefficients[2,1])
|
||||
|
||||
pval_logistic = summary(model)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
print(df)
|
||||
ps_logistic_df3 = rbind(ps_logistic_df3, df)
|
||||
|
||||
}
|
||||
#--------------------
|
||||
# formatting df
|
||||
#--------------------
|
||||
ps_logistic_df3$data_source = "df3"
|
||||
ps_logistic_df3$model = "unadjusted"
|
||||
|
||||
ps_logistic_df3$odds_ratio = round(ps_logistic_df3$odds_ratio, 2)
|
||||
ps_logistic_df3$ci_lower = round(ps_logistic_df3$ci_lower, 2)
|
||||
ps_logistic_df3$ci_upper = round(ps_logistic_df3$ci_upper, 2)
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_df3$pvalue_signif = ps_logistic_df3$pvalue
|
||||
str(ps_logistic_df3$pvalue_signif)
|
||||
|
||||
ps_logistic_df3 = dplyr::mutate(ps_logistic_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
ps_logistic_df3_o = ps_logistic_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
, "data_source")]
|
||||
|
||||
#========================================
|
||||
# merged_df3: adjusted, loop
|
||||
#========================================
|
||||
#model_adjusted_df3 = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
|
||||
# duet_stability_change + duet_scaled + duet_outcome + ddg + foldx_scaled + foldx_outcome
|
||||
# , data = merged_df3
|
||||
# , family = "binomial")
|
||||
|
||||
model_adjusted_df3 = glm(mutation_info_labels ~ rd_values +
|
||||
ligand_distance + duet_stability_change
|
||||
, data = merged_df3
|
||||
, family = "binomial");summary(model_adjusted_df3)
|
||||
|
||||
var_names_df = as.data.frame(names(model_adjusted_df3$coefficients))
|
||||
names(var_names_df) = c("var_name")
|
||||
|
||||
ci_mod = exp(confint(model_adjusted_df3))
|
||||
ci_mod_df = as.data.frame(ci_mod)
|
||||
names(ci_mod_df) = c("ci_lower", "ci_upper")
|
||||
ci_mod_df$ci_lower = round(ci_mod_df$ci_lower, 2)
|
||||
ci_mod_df$ci_upper = round(ci_mod_df$ci_upper, 2)
|
||||
|
||||
|
||||
estimates_df = as.data.frame(summary(model_adjusted_df3)$coefficients)
|
||||
names(estimates_df) = c("beta", "se", "zvalue", "pvalue")
|
||||
estimates_df$odds_ratio = round(exp(estimates_df$beta), 2)
|
||||
number_samples = nobs(model_adjusted_df3)
|
||||
estimates_df$number_samples = number_samples
|
||||
estimates_df$data_source = "df3"
|
||||
estimates_df$model = "adjusted"
|
||||
|
||||
names(ps_logistic_adjusted_df3)
|
||||
|
||||
if ( all(rownames(estimates_df) == rownames(ci_mod_df)) ){
|
||||
cat("PASS: rownames match. Preparing to merge...")
|
||||
ps_logistic_adjusted_df3 = merge(estimates_df, ci_mod_df, by = "row.names", all = T)
|
||||
}
|
||||
|
||||
colnames(ps_logistic_adjusted_df3)[1] <- c("var_name")
|
||||
d2 = which(ps_logistic_adjusted_df3$var_name == "(Intercept)")
|
||||
ps_logistic_adjusted_df3 = ps_logistic_adjusted_df3[-d2,]
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_adjusted_df3$pvalue_signif = ps_logistic_adjusted_df3$pvalue
|
||||
str(ps_logistic_adjusted_df3$pvalue_signif)
|
||||
|
||||
ps_logistic_adjusted_df3 = dplyr::mutate(ps_logistic_adjusted_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
colnames(ps_logistic_adjusted_df3)
|
||||
ps_logistic_adjusted_df3_o = ps_logistic_adjusted_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
,"data_source")]
|
||||
|
||||
#-------------
|
||||
# lm
|
||||
#-------------
|
||||
|
||||
model_lm = lm(or_kin ~ rsa + rd_values + duet_stability_change + ddg + mutation_info_labels
|
||||
, data = merged_df3)
|
||||
|
||||
summary(model_lm)
|
||||
|
||||
|
||||
model_lm1 = lm(or_mychisq ~ mutation_info_labels
|
||||
, data = merged_df2)
|
||||
|
||||
summary(model_lm1)
|
||||
203
scripts/prediction_lig.R
Normal file
203
scripts/prediction_lig.R
Normal file
|
|
@ -0,0 +1,203 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: prediction
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
source("plotting/combining_dfs_plotting.R")
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
lig_unadjusted = paste0(outdir, "/results/", tolower(gene), "_unadjusted_logistic_LIG.csv")
|
||||
lig_adjusted = paste0(outdir, "/results/", tolower(gene), "_adjusted_logistic_LIG.csv")
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions
|
||||
####################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# lig
|
||||
table(merged_df3_lig$mutation_info)
|
||||
merged_df3_lig$mutation_info_labels = ifelse(merged_df3_lig$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3_lig$mutation_info_labels)
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
###############################################################################
|
||||
|
||||
#========================================
|
||||
# merged_df3_lig: UNadjusted,loop
|
||||
#========================================
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
|
||||
#, "ligand_distance"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "ligand_affinity_change"
|
||||
, "affinity_scaled"
|
||||
, "ligand_outcome")
|
||||
|
||||
lig_logistic_df3 = data.frame()
|
||||
|
||||
for( i in my_ivs){
|
||||
print(i)
|
||||
|
||||
df = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
model_lig = glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df3_lig
|
||||
, family = "binomial")
|
||||
|
||||
var_name = i
|
||||
|
||||
number_samples = nobs(model_lig)
|
||||
|
||||
beta_logistic = summary(model_lig)$coefficients[2,1]; beta_logistic
|
||||
|
||||
or_logistic = exp(summary(model_lig)$coefficients[2,1])
|
||||
|
||||
pval_logistic = summary(model_lig)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model_lig)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model_lig)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model_lig))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
print(df)
|
||||
lig_logistic_df3 = rbind(lig_logistic_df3, df)
|
||||
|
||||
}
|
||||
#--------------------
|
||||
# formatting df
|
||||
#--------------------
|
||||
lig_logistic_df3$data_source = "df3_lig"
|
||||
lig_logistic_df3$model_lig = "unadjusted"
|
||||
|
||||
lig_logistic_df3$odds_ratio = round(lig_logistic_df3$odds_ratio, 2)
|
||||
lig_logistic_df3$ci_lower = round(lig_logistic_df3$ci_lower, 2)
|
||||
lig_logistic_df3$ci_upper = round(lig_logistic_df3$ci_upper, 2)
|
||||
|
||||
# adding pvalue_signif
|
||||
lig_logistic_df3$pvalue_signif = lig_logistic_df3$pvalue
|
||||
str(lig_logistic_df3$pvalue_signif)
|
||||
|
||||
lig_logistic_df3 = dplyr::mutate(lig_logistic_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
lig_logistic_df3_o = lig_logistic_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model_lig"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
, "data_source")]
|
||||
# writing file
|
||||
write.csv(lig_logistic_df3_o, lig_unadjusted, row.names = F)
|
||||
|
||||
#========================================
|
||||
# merged_df3_lig: adjusted, loop
|
||||
#========================================
|
||||
#model_lig_adjusted_df3 = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
|
||||
# ligand_affinity_change + affinity_scaled + ligand_outcome
|
||||
# , data = merged_df3_lig
|
||||
# , family = "binomial")
|
||||
|
||||
model_lig_adjusted_df3 = glm(mutation_info_labels ~ rd_values + ligand_affinity_change
|
||||
, data = merged_df3_lig
|
||||
, family = "binomial");summary(model_lig_adjusted_df3)
|
||||
|
||||
var_names_df = as.data.frame(names(model_lig_adjusted_df3$coefficients))
|
||||
names(var_names_df) = c("var_name")
|
||||
|
||||
ci_mod = exp(confint(model_lig_adjusted_df3))
|
||||
ci_mod_df = as.data.frame(ci_mod)
|
||||
names(ci_mod_df) = c("ci_lower", "ci_upper")
|
||||
ci_mod_df$ci_lower = round(ci_mod_df$ci_lower, 2)
|
||||
ci_mod_df$ci_upper = round(ci_mod_df$ci_upper, 2)
|
||||
|
||||
|
||||
estimates_df = as.data.frame(summary(model_lig_adjusted_df3)$coefficients)
|
||||
names(estimates_df) = c("beta", "se", "zvalue", "pvalue")
|
||||
estimates_df$odds_ratio = round(exp(estimates_df$beta), 2)
|
||||
number_samples = nobs(model_lig_adjusted_df3)
|
||||
estimates_df$number_samples = number_samples
|
||||
estimates_df$data_source = "df3_lig"
|
||||
estimates_df$model_lig = "adjusted"
|
||||
|
||||
names(lig_logistic_adjusted_df3)
|
||||
|
||||
if ( all(rownames(estimates_df) == rownames(ci_mod_df)) ){
|
||||
cat("PASS: rownames match. Preparing to merge...")
|
||||
lig_logistic_adjusted_df3 = merge(estimates_df, ci_mod_df, by = "row.names", all = T)
|
||||
}
|
||||
|
||||
colnames(lig_logistic_adjusted_df3)[1] <- c("var_name")
|
||||
d2 = which(lig_logistic_adjusted_df3$var_name == "(Intercept)")
|
||||
lig_logistic_adjusted_df3 = lig_logistic_adjusted_df3[-d2,]
|
||||
|
||||
# adding pvalue_signif
|
||||
lig_logistic_adjusted_df3$pvalue_signif = lig_logistic_adjusted_df3$pvalue
|
||||
str(lig_logistic_adjusted_df3$pvalue_signif)
|
||||
|
||||
lig_logistic_adjusted_df3 = dplyr::mutate(lig_logistic_adjusted_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
colnames(lig_logistic_adjusted_df3)
|
||||
lig_logistic_adjusted_df3_o = lig_logistic_adjusted_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model_lig"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
,"data_source")]
|
||||
# writing file
|
||||
write.csv(lig_logistic_adjusted_df3_o, lig_adjusted, row.names = F)
|
||||
213
scripts/prediction_ps.R
Normal file
213
scripts/prediction_ps.R
Normal file
|
|
@ -0,0 +1,213 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: prediction
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
source("plotting/combining_dfs_plotting.R")
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
ps_unadjusted = paste0(outdir, "/results/", tolower(gene), "_unadjusted_logistic_PS.csv")
|
||||
ps_adjusted = paste0(outdir, "/results/", tolower(gene), "_adjusted_logistic_PS.csv")
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions
|
||||
####################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# ps
|
||||
table(merged_df3$mutation_info)
|
||||
merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3$mutation_info_labels)
|
||||
|
||||
|
||||
# lig
|
||||
table(merged_df3_lig$mutation_info)
|
||||
merged_df3_lig$mutation_info_labels = ifelse(merged_df3_lig$mutation_info == dr_muts_col, 1, 0)
|
||||
table(merged_df3_lig$mutation_info_labels)
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
###############################################################################
|
||||
#========================================
|
||||
# merged_df3: UNadjusted,loop
|
||||
#========================================
|
||||
my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
|
||||
, "ligand_distance"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "duet_stability_change"
|
||||
, "duet_scaled"
|
||||
, "duet_outcome"
|
||||
, "ddg"
|
||||
, "foldx_scaled"
|
||||
, "foldx_outcome")
|
||||
|
||||
ps_logistic_df3 = data.frame()
|
||||
|
||||
for( i in my_ivs){
|
||||
print(i)
|
||||
|
||||
df = data.frame(var_name = NA
|
||||
, number_samples = NA
|
||||
, beta = NA
|
||||
, odds_ratio = NA
|
||||
, pvalue = NA
|
||||
, se = NA
|
||||
, zvalue = NA
|
||||
, ci_lower = NA
|
||||
, ci_upper = NA)
|
||||
|
||||
model = glm(mutation_info_labels ~ eval(parse(text=i))
|
||||
, data = merged_df3
|
||||
, family = "binomial")
|
||||
|
||||
var_name = i
|
||||
|
||||
number_samples = nobs(model)
|
||||
|
||||
beta_logistic = summary(model)$coefficients[2,1]; beta_logistic
|
||||
|
||||
or_logistic = exp(summary(model)$coefficients[2,1])
|
||||
|
||||
pval_logistic = summary(model)$coefficients[2,4]
|
||||
|
||||
se_logistic = summary(model)$coefficients[2,2]
|
||||
|
||||
zval_logistic = summary(model)$coefficients[2,3]
|
||||
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
|
||||
ci_lower_logistic = ci_mod[["2.5 %"]]
|
||||
ci_upper_logistic = ci_mod[["97.5 %"]]
|
||||
|
||||
print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
|
||||
|
||||
df$var_name = var_name
|
||||
df$number_samples = number_samples
|
||||
df$beta = beta_logistic
|
||||
df$odds_ratio = or_logistic
|
||||
df$pvalue = pval_logistic
|
||||
df$se = se_logistic
|
||||
df$zvalue = zval_logistic
|
||||
df$ci_lower = ci_lower_logistic
|
||||
df$ci_upper = ci_upper_logistic
|
||||
|
||||
print(df)
|
||||
ps_logistic_df3 = rbind(ps_logistic_df3, df)
|
||||
|
||||
}
|
||||
#--------------------
|
||||
# formatting df
|
||||
#--------------------
|
||||
ps_logistic_df3$data_source = "df3"
|
||||
ps_logistic_df3$model = "unadjusted"
|
||||
|
||||
ps_logistic_df3$odds_ratio = round(ps_logistic_df3$odds_ratio, 2)
|
||||
ps_logistic_df3$ci_lower = round(ps_logistic_df3$ci_lower, 2)
|
||||
ps_logistic_df3$ci_upper = round(ps_logistic_df3$ci_upper, 2)
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_df3$pvalue_signif = ps_logistic_df3$pvalue
|
||||
str(ps_logistic_df3$pvalue_signif)
|
||||
|
||||
ps_logistic_df3 = dplyr::mutate(ps_logistic_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
ps_logistic_df3_o = ps_logistic_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
, "data_source")]
|
||||
# writing file
|
||||
write.csv(ps_logistic_df3_o, ps_unadjusted, row.names = F)
|
||||
|
||||
#========================================
|
||||
# merged_df3: adjusted, loop
|
||||
#========================================
|
||||
#model_adjusted_df3 = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
|
||||
# duet_stability_change + duet_scaled + duet_outcome + ddg + foldx_scaled + foldx_outcome
|
||||
# , data = merged_df3
|
||||
# , family = "binomial")
|
||||
|
||||
model_adjusted_df3 = glm(mutation_info_labels ~ rd_values +
|
||||
ligand_distance + duet_stability_change
|
||||
, data = merged_df3
|
||||
, family = "binomial");summary(model_adjusted_df3)
|
||||
|
||||
var_names_df = as.data.frame(names(model_adjusted_df3$coefficients))
|
||||
names(var_names_df) = c("var_name")
|
||||
|
||||
ci_mod = exp(confint(model_adjusted_df3))
|
||||
ci_mod_df = as.data.frame(ci_mod)
|
||||
names(ci_mod_df) = c("ci_lower", "ci_upper")
|
||||
ci_mod_df$ci_lower = round(ci_mod_df$ci_lower, 2)
|
||||
ci_mod_df$ci_upper = round(ci_mod_df$ci_upper, 2)
|
||||
|
||||
|
||||
estimates_df = as.data.frame(summary(model_adjusted_df3)$coefficients)
|
||||
names(estimates_df) = c("beta", "se", "zvalue", "pvalue")
|
||||
estimates_df$odds_ratio = round(exp(estimates_df$beta), 2)
|
||||
number_samples = nobs(model_adjusted_df3)
|
||||
estimates_df$number_samples = number_samples
|
||||
estimates_df$data_source = "df3"
|
||||
estimates_df$model = "adjusted"
|
||||
|
||||
names(ps_logistic_adjusted_df3)
|
||||
|
||||
if ( all(rownames(estimates_df) == rownames(ci_mod_df)) ){
|
||||
cat("PASS: rownames match. Preparing to merge...")
|
||||
ps_logistic_adjusted_df3 = merge(estimates_df, ci_mod_df, by = "row.names", all = T)
|
||||
}
|
||||
|
||||
colnames(ps_logistic_adjusted_df3)[1] <- c("var_name")
|
||||
d2 = which(ps_logistic_adjusted_df3$var_name == "(Intercept)")
|
||||
ps_logistic_adjusted_df3 = ps_logistic_adjusted_df3[-d2,]
|
||||
|
||||
# adding pvalue_signif
|
||||
ps_logistic_adjusted_df3$pvalue_signif = ps_logistic_adjusted_df3$pvalue
|
||||
str(ps_logistic_adjusted_df3$pvalue_signif)
|
||||
|
||||
ps_logistic_adjusted_df3 = dplyr::mutate(ps_logistic_adjusted_df3
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# rearranging columns
|
||||
colnames(ps_logistic_adjusted_df3)
|
||||
ps_logistic_adjusted_df3_o = ps_logistic_adjusted_df3[c("var_name"
|
||||
, "number_samples"
|
||||
, "model"
|
||||
, "odds_ratio"
|
||||
, "pvalue"
|
||||
, "pvalue_signif"
|
||||
, "beta"
|
||||
, "se"
|
||||
, "zvalue"
|
||||
, "ci_lower"
|
||||
, "ci_upper"
|
||||
,"data_source")]
|
||||
# writing file
|
||||
write.csv(ps_logistic_adjusted_df3_o, ps_adjusted, row.names = F)
|
||||
###############################################################################
|
||||
Loading…
Add table
Add a link
Reference in a new issue