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positional_modeling_SGE1_v6_20171013_noggplot.Rmd
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positional_modeling_SGE1_v6_20171013_noggplot.Rmd
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---
title: "Positional modeling"
output: html_document
---
Notes for changes to implement in v6
-- rename everything with 'r' (then, 'r'...)
-- model fit for replicates separately
-- model fit for replicate averages (compare w. above)
```{r}
#requires exon snv dataframes to be present
#a function to extend the lowess fit to points missing on either end of the sequence (must be used on an ordered sequence vector, as supplied here. Will only fill in missing points on the ends)
extend_fit <- function(fit_vector) {
if (length(which(is.na(fit_vector))) == 0) {
out_vector <- fit_vector
}
else {
min_na <- min(which(is.na(fit_vector) == TRUE))
min_point <- min(which(is.na(fit_vector) == FALSE))
min_point_val <- fit_vector[min_point]
max_point <- max(which(is.na(fit_vector) == FALSE))
max_point_val <- fit_vector[max_point]
max_na <- max(which(is.na(fit_vector) == TRUE))
out_vector <- fit_vector
if (min_na == 1) {
out_vector[min_na:min_point] <- min_point_val
}
if (max_na == length(fit_vector)) {
out_vector[(max_point+1):length(fit_vector)] <- max_point_val
}
}
return(out_vector)
}
#X2r --
#X2r1
X2_posrange <- range(as.numeric(as.character(X2r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X2_posseq <- seq(from=X2_posrange[1], to=X2_posrange[2])
X2r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X2r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X2r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X2r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X2r1_tHDR_pre_lib_pred <- predict(X2r1_tHDR_pre_lib_ratio.lo, newdata = X2_posseq, se=TRUE)
X2r1_tHDR_pre_lib_pos_effects <- X2r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X2r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X2r1_tHDR_pre_lib_pos_effects)
X2r1_tHDR_pre_lib_pos_effects_ci <- X2r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X2r1_tHDR_pre_lib_pred$df)
X2r1_tHDR_pre_lib_pos_effects_ci_ymin = X2r1_tHDR_pre_lib_pos_effects - X2r1_tHDR_pre_lib_pos_effects_ci
X2r1_tHDR_pre_lib_pos_effects_ci_ymax = X2r1_tHDR_pre_lib_pos_effects + X2r1_tHDR_pre_lib_pos_effects_ci
X2r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X2_posseq, X2r1_tHDR_pre_lib_pos_effects, X2r1_tHDR_pre_lib_pos_effects.x, X2r1_tHDR_pre_lib_pos_effects_ci_ymin, X2r1_tHDR_pre_lib_pos_effects_ci_ymax, X2r1_tHDR_pre_lib_pred_se = X2r1_tHDR_pre_lib_pred$se.fit)
#X2r2
X2r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X2r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X2r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X2r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X2r2_tHDR_pre_lib_pred <- predict(X2r2_tHDR_pre_lib_ratio.lo, newdata = X2_posseq, se=TRUE)
X2r2_tHDR_pre_lib_pos_effects <- X2r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X2r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X2r2_tHDR_pre_lib_pos_effects)
X2r2_tHDR_pre_lib_pos_effects_ci <- X2r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X2r2_tHDR_pre_lib_pred$df)
X2r2_tHDR_pre_lib_pos_effects_ci_ymin = X2r2_tHDR_pre_lib_pos_effects - X2r2_tHDR_pre_lib_pos_effects_ci
X2r2_tHDR_pre_lib_pos_effects_ci_ymax = X2r2_tHDR_pre_lib_pos_effects + X2r2_tHDR_pre_lib_pos_effects_ci
X2r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X2_posseq, X2r2_tHDR_pre_lib_pos_effects, X2r2_tHDR_pre_lib_pos_effects.x, X2r2_tHDR_pre_lib_pos_effects_ci_ymin, X2r2_tHDR_pre_lib_pos_effects_ci_ymax, X2r2_tHDR_pre_lib_pred_se = X2r2_tHDR_pre_lib_pred$se.fit)
#X2r1r2
X2r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X2r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X2r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X2r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X2r1r2_tHDR_pre_lib_pred <- predict(X2r1r2_tHDR_pre_lib_ratio.lo, newdata = X2_posseq, se=TRUE)
X2r1r2_tHDR_pre_lib_pos_effects <- X2r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X2r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X2r1r2_tHDR_pre_lib_pos_effects)
X2r1r2_tHDR_pre_lib_pos_effects_ci <- X2r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X2r1r2_tHDR_pre_lib_pred$df)
X2r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X2r1r2_tHDR_pre_lib_pos_effects - X2r1r2_tHDR_pre_lib_pos_effects_ci
X2r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X2r1r2_tHDR_pre_lib_pos_effects + X2r1r2_tHDR_pre_lib_pos_effects_ci
X2r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X2_posseq, X2r1r2_tHDR_pre_lib_pos_effects, X2r1r2_tHDR_pre_lib_pos_effects.x, X2r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X2r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X2r1r2_tHDR_pre_lib_pred_se = X2r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X2r_tHDR_pos_merge_df <- merge(X2r1_prog_map_thresh5_df_r2_int_ord_df, X2r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X2r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X2r_tHDR_pos_merge_df <- merge(X2r_tHDR_pos_merge_df, X2r2_tHDR_pre_lib_pos_effects.df[, c("pos","X2r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X2r_tHDR_pos_merge_df <- merge(X2r_tHDR_pos_merge_df, X2r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X2r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X2r_tHDR_pos_merge_df$X2r1_tHDR_post_lib_loess_r1 <- log2(X2r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r1_tHDR_post_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r2_tHDR_post_lib_loess_r2 <- log2(X2r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r2_tHDR_post_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r1r2_tHDR_post_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X2r_tHDR_pos_merge_df$X2r1_tHDR_pre_lib_loess_r1 <- log2(X2r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r1_tHDR_pre_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r2_tHDR_pre_lib_loess_r2 <- log2(X2r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r2_tHDR_pre_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X2r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X2r_tHDR_pos_merge_df$X2r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X2r_tHDR_post_lib_loess_median_syn_r1 <- median(X2r_tHDR_pos_merge_df[which(X2r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X2r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X2r_tHDR_post_lib_loess_median_syn_r2 <- median(X2r_tHDR_pos_merge_df[which(X2r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X2r2_tHDR_post_lib_loess_r2')])
#defines A
X2r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X2r_tHDR_post_lib_loess_median_syn_r1,X2r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X2r_tHDR_post_lib_loess_median_ns_r1 <- median(X2r_tHDR_pos_merge_df[which(X2r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X2r1_tHDR_post_lib_loess_r1')])
#define c for r2
X2r_tHDR_post_lib_loess_median_ns_r2 <- median(X2r_tHDR_pos_merge_df[which(X2r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X2r2_tHDR_post_lib_loess_r2')])
#defines C
X2r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X2r_tHDR_post_lib_loess_median_ns_r1,X2r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X2r_tHDR_pos_merge_df$X2r1_tHDR_post_lib_loess_r1_e.sns <- (X2r_tHDR_pos_merge_df$X2r1_tHDR_post_lib_loess_r1/(X2r_tHDR_post_lib_loess_median_syn_r1-X2r_tHDR_post_lib_loess_median_ns_r1))*(X2r_tHDR_post_lib_loess_median_syn_r1r2-X2r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X2r_tHDR_pos_merge_df$X2r2_tHDR_post_lib_loess_r2_e.sns <- (X2r_tHDR_pos_merge_df$X2r2_tHDR_post_lib_loess_r2/(X2r_tHDR_post_lib_loess_median_syn_r2-X2r_tHDR_post_lib_loess_median_ns_r2))*(X2r_tHDR_post_lib_loess_median_syn_r1r2-X2r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X2r_tHDR_pos_merge_df$X2r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X2r_tHDR_pos_merge_df$X2r1_tHDR_post_lib_loess_r1_e.sns+X2r_tHDR_pos_merge_df$X2r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X2r_tHDR_pos_merge_df)[names(X2r_tHDR_pos_merge_df) == "X2r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X2r_tHDR_pos_merge_df$exon = 'X2'
X2r_tHDR_pos_merge_df$rep_type = 'r'
#X3r --
#X3r1
X3_posrange <- range(as.numeric(as.character(X3r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X3_posseq <- seq(from=X3_posrange[1], to=X3_posrange[2])
X3r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X3r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X3r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X3r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X3r1_tHDR_pre_lib_pred <- predict(X3r1_tHDR_pre_lib_ratio.lo, newdata = X3_posseq, se=TRUE)
X3r1_tHDR_pre_lib_pos_effects <- X3r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X3r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X3r1_tHDR_pre_lib_pos_effects)
X3r1_tHDR_pre_lib_pos_effects_ci <- X3r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X3r1_tHDR_pre_lib_pred$df)
X3r1_tHDR_pre_lib_pos_effects_ci_ymin = X3r1_tHDR_pre_lib_pos_effects - X3r1_tHDR_pre_lib_pos_effects_ci
X3r1_tHDR_pre_lib_pos_effects_ci_ymax = X3r1_tHDR_pre_lib_pos_effects + X3r1_tHDR_pre_lib_pos_effects_ci
X3r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X3_posseq, X3r1_tHDR_pre_lib_pos_effects, X3r1_tHDR_pre_lib_pos_effects.x, X3r1_tHDR_pre_lib_pos_effects_ci_ymin, X3r1_tHDR_pre_lib_pos_effects_ci_ymax, X3r1_tHDR_pre_lib_pred_se = X3r1_tHDR_pre_lib_pred$se.fit)
#X3r2
X3r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X3r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X3r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X3r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X3r2_tHDR_pre_lib_pred <- predict(X3r2_tHDR_pre_lib_ratio.lo, newdata = X3_posseq, se=TRUE)
X3r2_tHDR_pre_lib_pos_effects <- X3r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X3r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X3r2_tHDR_pre_lib_pos_effects)
X3r2_tHDR_pre_lib_pos_effects_ci <- X3r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X3r2_tHDR_pre_lib_pred$df)
X3r2_tHDR_pre_lib_pos_effects_ci_ymin = X3r2_tHDR_pre_lib_pos_effects - X3r2_tHDR_pre_lib_pos_effects_ci
X3r2_tHDR_pre_lib_pos_effects_ci_ymax = X3r2_tHDR_pre_lib_pos_effects + X3r2_tHDR_pre_lib_pos_effects_ci
X3r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X3_posseq, X3r2_tHDR_pre_lib_pos_effects, X3r2_tHDR_pre_lib_pos_effects.x, X3r2_tHDR_pre_lib_pos_effects_ci_ymin, X3r2_tHDR_pre_lib_pos_effects_ci_ymax, X3r2_tHDR_pre_lib_pred_se = X3r2_tHDR_pre_lib_pred$se.fit)
#X3r1r2
X3r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X3r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X3r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X3r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X3r1r2_tHDR_pre_lib_pred <- predict(X3r1r2_tHDR_pre_lib_ratio.lo, newdata = X3_posseq, se=TRUE)
X3r1r2_tHDR_pre_lib_pos_effects <- X3r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X3r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X3r1r2_tHDR_pre_lib_pos_effects)
X3r1r2_tHDR_pre_lib_pos_effects_ci <- X3r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X3r1r2_tHDR_pre_lib_pred$df)
X3r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X3r1r2_tHDR_pre_lib_pos_effects - X3r1r2_tHDR_pre_lib_pos_effects_ci
X3r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X3r1r2_tHDR_pre_lib_pos_effects + X3r1r2_tHDR_pre_lib_pos_effects_ci
X3r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X3_posseq, X3r1r2_tHDR_pre_lib_pos_effects, X3r1r2_tHDR_pre_lib_pos_effects.x, X3r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X3r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X3r1r2_tHDR_pre_lib_pred_se = X3r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X3r_tHDR_pos_merge_df <- merge(X3r1_prog_map_thresh5_df_r2_int_ord_df, X3r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X3r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X3r_tHDR_pos_merge_df <- merge(X3r_tHDR_pos_merge_df, X3r2_tHDR_pre_lib_pos_effects.df[, c("pos","X3r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X3r_tHDR_pos_merge_df <- merge(X3r_tHDR_pos_merge_df, X3r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X3r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X3r_tHDR_pos_merge_df$X3r1_tHDR_post_lib_loess_r1 <- log2(X3r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r1_tHDR_post_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r2_tHDR_post_lib_loess_r2 <- log2(X3r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r2_tHDR_post_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r1r2_tHDR_post_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X3r_tHDR_pos_merge_df$X3r1_tHDR_pre_lib_loess_r1 <- log2(X3r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r1_tHDR_pre_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r2_tHDR_pre_lib_loess_r2 <- log2(X3r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r2_tHDR_pre_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X3r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X3r_tHDR_pos_merge_df$X3r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X3r_tHDR_post_lib_loess_median_syn_r1 <- median(X3r_tHDR_pos_merge_df[which(X3r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X3r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X3r_tHDR_post_lib_loess_median_syn_r2 <- median(X3r_tHDR_pos_merge_df[which(X3r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X3r2_tHDR_post_lib_loess_r2')])
#defines A
X3r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X3r_tHDR_post_lib_loess_median_syn_r1,X3r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X3r_tHDR_post_lib_loess_median_ns_r1 <- median(X3r_tHDR_pos_merge_df[which(X3r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X3r1_tHDR_post_lib_loess_r1')])
#define c for r2
X3r_tHDR_post_lib_loess_median_ns_r2 <- median(X3r_tHDR_pos_merge_df[which(X3r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X3r2_tHDR_post_lib_loess_r2')])
#defines C
X3r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X3r_tHDR_post_lib_loess_median_ns_r1,X3r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X3r_tHDR_pos_merge_df$X3r1_tHDR_post_lib_loess_r1_e.sns <- (X3r_tHDR_pos_merge_df$X3r1_tHDR_post_lib_loess_r1/(X3r_tHDR_post_lib_loess_median_syn_r1-X3r_tHDR_post_lib_loess_median_ns_r1))*(X3r_tHDR_post_lib_loess_median_syn_r1r2-X3r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X3r_tHDR_pos_merge_df$X3r2_tHDR_post_lib_loess_r2_e.sns <- (X3r_tHDR_pos_merge_df$X3r2_tHDR_post_lib_loess_r2/(X3r_tHDR_post_lib_loess_median_syn_r2-X3r_tHDR_post_lib_loess_median_ns_r2))*(X3r_tHDR_post_lib_loess_median_syn_r1r2-X3r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X3r_tHDR_pos_merge_df$X3r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X3r_tHDR_pos_merge_df$X3r1_tHDR_post_lib_loess_r1_e.sns+X3r_tHDR_pos_merge_df$X3r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X3r_tHDR_pos_merge_df)[names(X3r_tHDR_pos_merge_df) == "X3r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X3r_tHDR_pos_merge_df$exon = 'X3'
X3r_tHDR_pos_merge_df$rep_type = 'r'
#X4r --
#X4r1
X4_posrange <- range(as.numeric(as.character(X4r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X4_posseq <- seq(from=X4_posrange[1], to=X4_posrange[2])
X4r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X4r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X4r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X4r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X4r1_tHDR_pre_lib_pred <- predict(X4r1_tHDR_pre_lib_ratio.lo, newdata = X4_posseq, se=TRUE)
X4r1_tHDR_pre_lib_pos_effects <- X4r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X4r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X4r1_tHDR_pre_lib_pos_effects)
X4r1_tHDR_pre_lib_pos_effects_ci <- X4r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X4r1_tHDR_pre_lib_pred$df)
X4r1_tHDR_pre_lib_pos_effects_ci_ymin = X4r1_tHDR_pre_lib_pos_effects - X4r1_tHDR_pre_lib_pos_effects_ci
X4r1_tHDR_pre_lib_pos_effects_ci_ymax = X4r1_tHDR_pre_lib_pos_effects + X4r1_tHDR_pre_lib_pos_effects_ci
X4r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X4_posseq, X4r1_tHDR_pre_lib_pos_effects, X4r1_tHDR_pre_lib_pos_effects.x, X4r1_tHDR_pre_lib_pos_effects_ci_ymin, X4r1_tHDR_pre_lib_pos_effects_ci_ymax, X4r1_tHDR_pre_lib_pred_se = X4r1_tHDR_pre_lib_pred$se.fit)
#X4r2
X4r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X4r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X4r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X4r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X4r2_tHDR_pre_lib_pred <- predict(X4r2_tHDR_pre_lib_ratio.lo, newdata = X4_posseq, se=TRUE)
X4r2_tHDR_pre_lib_pos_effects <- X4r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X4r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X4r2_tHDR_pre_lib_pos_effects)
X4r2_tHDR_pre_lib_pos_effects_ci <- X4r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X4r2_tHDR_pre_lib_pred$df)
X4r2_tHDR_pre_lib_pos_effects_ci_ymin = X4r2_tHDR_pre_lib_pos_effects - X4r2_tHDR_pre_lib_pos_effects_ci
X4r2_tHDR_pre_lib_pos_effects_ci_ymax = X4r2_tHDR_pre_lib_pos_effects + X4r2_tHDR_pre_lib_pos_effects_ci
X4r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X4_posseq, X4r2_tHDR_pre_lib_pos_effects, X4r2_tHDR_pre_lib_pos_effects.x, X4r2_tHDR_pre_lib_pos_effects_ci_ymin, X4r2_tHDR_pre_lib_pos_effects_ci_ymax, X4r2_tHDR_pre_lib_pred_se = X4r2_tHDR_pre_lib_pred$se.fit)
#X4r1r2
X4r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X4r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X4r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X4r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X4r1r2_tHDR_pre_lib_pred <- predict(X4r1r2_tHDR_pre_lib_ratio.lo, newdata = X4_posseq, se=TRUE)
X4r1r2_tHDR_pre_lib_pos_effects <- X4r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X4r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X4r1r2_tHDR_pre_lib_pos_effects)
X4r1r2_tHDR_pre_lib_pos_effects_ci <- X4r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X4r1r2_tHDR_pre_lib_pred$df)
X4r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X4r1r2_tHDR_pre_lib_pos_effects - X4r1r2_tHDR_pre_lib_pos_effects_ci
X4r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X4r1r2_tHDR_pre_lib_pos_effects + X4r1r2_tHDR_pre_lib_pos_effects_ci
X4r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X4_posseq, X4r1r2_tHDR_pre_lib_pos_effects, X4r1r2_tHDR_pre_lib_pos_effects.x, X4r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X4r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X4r1r2_tHDR_pre_lib_pred_se = X4r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X4r_tHDR_pos_merge_df <- merge(X4r1_prog_map_thresh5_df_r2_int_ord_df, X4r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X4r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X4r_tHDR_pos_merge_df <- merge(X4r_tHDR_pos_merge_df, X4r2_tHDR_pre_lib_pos_effects.df[, c("pos","X4r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X4r_tHDR_pos_merge_df <- merge(X4r_tHDR_pos_merge_df, X4r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X4r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X4r_tHDR_pos_merge_df$X4r1_tHDR_post_lib_loess_r1 <- log2(X4r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r1_tHDR_post_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r2_tHDR_post_lib_loess_r2 <- log2(X4r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r2_tHDR_post_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r1r2_tHDR_post_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X4r_tHDR_pos_merge_df$X4r1_tHDR_pre_lib_loess_r1 <- log2(X4r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r1_tHDR_pre_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r2_tHDR_pre_lib_loess_r2 <- log2(X4r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r2_tHDR_pre_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X4r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X4r_tHDR_pos_merge_df$X4r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X4r_tHDR_post_lib_loess_median_syn_r1 <- median(X4r_tHDR_pos_merge_df[which(X4r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X4r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X4r_tHDR_post_lib_loess_median_syn_r2 <- median(X4r_tHDR_pos_merge_df[which(X4r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X4r2_tHDR_post_lib_loess_r2')])
#defines A
X4r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X4r_tHDR_post_lib_loess_median_syn_r1,X4r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X4r_tHDR_post_lib_loess_median_ns_r1 <- median(X4r_tHDR_pos_merge_df[which(X4r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X4r1_tHDR_post_lib_loess_r1')])
#define c for r2
X4r_tHDR_post_lib_loess_median_ns_r2 <- median(X4r_tHDR_pos_merge_df[which(X4r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X4r2_tHDR_post_lib_loess_r2')])
#defines C
X4r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X4r_tHDR_post_lib_loess_median_ns_r1,X4r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X4r_tHDR_pos_merge_df$X4r1_tHDR_post_lib_loess_r1_e.sns <- (X4r_tHDR_pos_merge_df$X4r1_tHDR_post_lib_loess_r1/(X4r_tHDR_post_lib_loess_median_syn_r1-X4r_tHDR_post_lib_loess_median_ns_r1))*(X4r_tHDR_post_lib_loess_median_syn_r1r2-X4r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X4r_tHDR_pos_merge_df$X4r2_tHDR_post_lib_loess_r2_e.sns <- (X4r_tHDR_pos_merge_df$X4r2_tHDR_post_lib_loess_r2/(X4r_tHDR_post_lib_loess_median_syn_r2-X4r_tHDR_post_lib_loess_median_ns_r2))*(X4r_tHDR_post_lib_loess_median_syn_r1r2-X4r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X4r_tHDR_pos_merge_df$X4r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X4r_tHDR_pos_merge_df$X4r1_tHDR_post_lib_loess_r1_e.sns+X4r_tHDR_pos_merge_df$X4r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X4r_tHDR_pos_merge_df)[names(X4r_tHDR_pos_merge_df) == "X4r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X4r_tHDR_pos_merge_df$exon = 'X4'
X4r_tHDR_pos_merge_df$rep_type = 'r'
#X5r --
#X5r1
X5_posrange <- range(as.numeric(as.character(X5r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X5_posseq <- seq(from=X5_posrange[1], to=X5_posrange[2])
X5r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X5r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X5r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X5r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X5r1_tHDR_pre_lib_pred <- predict(X5r1_tHDR_pre_lib_ratio.lo, newdata = X5_posseq, se=TRUE)
X5r1_tHDR_pre_lib_pos_effects <- X5r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X5r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X5r1_tHDR_pre_lib_pos_effects)
X5r1_tHDR_pre_lib_pos_effects_ci <- X5r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X5r1_tHDR_pre_lib_pred$df)
X5r1_tHDR_pre_lib_pos_effects_ci_ymin = X5r1_tHDR_pre_lib_pos_effects - X5r1_tHDR_pre_lib_pos_effects_ci
X5r1_tHDR_pre_lib_pos_effects_ci_ymax = X5r1_tHDR_pre_lib_pos_effects + X5r1_tHDR_pre_lib_pos_effects_ci
X5r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X5_posseq, X5r1_tHDR_pre_lib_pos_effects, X5r1_tHDR_pre_lib_pos_effects.x, X5r1_tHDR_pre_lib_pos_effects_ci_ymin, X5r1_tHDR_pre_lib_pos_effects_ci_ymax, X5r1_tHDR_pre_lib_pred_se = X5r1_tHDR_pre_lib_pred$se.fit)
#X5r2
X5r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X5r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X5r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X5r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X5r2_tHDR_pre_lib_pred <- predict(X5r2_tHDR_pre_lib_ratio.lo, newdata = X5_posseq, se=TRUE)
X5r2_tHDR_pre_lib_pos_effects <- X5r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X5r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X5r2_tHDR_pre_lib_pos_effects)
X5r2_tHDR_pre_lib_pos_effects_ci <- X5r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X5r2_tHDR_pre_lib_pred$df)
X5r2_tHDR_pre_lib_pos_effects_ci_ymin = X5r2_tHDR_pre_lib_pos_effects - X5r2_tHDR_pre_lib_pos_effects_ci
X5r2_tHDR_pre_lib_pos_effects_ci_ymax = X5r2_tHDR_pre_lib_pos_effects + X5r2_tHDR_pre_lib_pos_effects_ci
X5r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X5_posseq, X5r2_tHDR_pre_lib_pos_effects, X5r2_tHDR_pre_lib_pos_effects.x, X5r2_tHDR_pre_lib_pos_effects_ci_ymin, X5r2_tHDR_pre_lib_pos_effects_ci_ymax, X5r2_tHDR_pre_lib_pred_se = X5r2_tHDR_pre_lib_pred$se.fit)
#X5r1r2
X5r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X5r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X5r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X5r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X5r1r2_tHDR_pre_lib_pred <- predict(X5r1r2_tHDR_pre_lib_ratio.lo, newdata = X5_posseq, se=TRUE)
X5r1r2_tHDR_pre_lib_pos_effects <- X5r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X5r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X5r1r2_tHDR_pre_lib_pos_effects)
X5r1r2_tHDR_pre_lib_pos_effects_ci <- X5r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X5r1r2_tHDR_pre_lib_pred$df)
X5r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X5r1r2_tHDR_pre_lib_pos_effects - X5r1r2_tHDR_pre_lib_pos_effects_ci
X5r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X5r1r2_tHDR_pre_lib_pos_effects + X5r1r2_tHDR_pre_lib_pos_effects_ci
X5r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X5_posseq, X5r1r2_tHDR_pre_lib_pos_effects, X5r1r2_tHDR_pre_lib_pos_effects.x, X5r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X5r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X5r1r2_tHDR_pre_lib_pred_se = X5r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X5r_tHDR_pos_merge_df <- merge(X5r1_prog_map_thresh5_df_r2_int_ord_df, X5r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X5r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X5r_tHDR_pos_merge_df <- merge(X5r_tHDR_pos_merge_df, X5r2_tHDR_pre_lib_pos_effects.df[, c("pos","X5r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X5r_tHDR_pos_merge_df <- merge(X5r_tHDR_pos_merge_df, X5r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X5r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X5r_tHDR_pos_merge_df$X5r1_tHDR_post_lib_loess_r1 <- log2(X5r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r1_tHDR_post_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r2_tHDR_post_lib_loess_r2 <- log2(X5r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r2_tHDR_post_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r1r2_tHDR_post_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X5r_tHDR_pos_merge_df$X5r1_tHDR_pre_lib_loess_r1 <- log2(X5r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r1_tHDR_pre_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r2_tHDR_pre_lib_loess_r2 <- log2(X5r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r2_tHDR_pre_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X5r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X5r_tHDR_pos_merge_df$X5r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X5r_tHDR_post_lib_loess_median_syn_r1 <- median(X5r_tHDR_pos_merge_df[which(X5r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X5r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X5r_tHDR_post_lib_loess_median_syn_r2 <- median(X5r_tHDR_pos_merge_df[which(X5r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X5r2_tHDR_post_lib_loess_r2')])
#defines A
X5r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X5r_tHDR_post_lib_loess_median_syn_r1,X5r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X5r_tHDR_post_lib_loess_median_ns_r1 <- median(X5r_tHDR_pos_merge_df[which(X5r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X5r1_tHDR_post_lib_loess_r1')])
#define c for r2
X5r_tHDR_post_lib_loess_median_ns_r2 <- median(X5r_tHDR_pos_merge_df[which(X5r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X5r2_tHDR_post_lib_loess_r2')])
#defines C
X5r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X5r_tHDR_post_lib_loess_median_ns_r1,X5r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X5r_tHDR_pos_merge_df$X5r1_tHDR_post_lib_loess_r1_e.sns <- (X5r_tHDR_pos_merge_df$X5r1_tHDR_post_lib_loess_r1/(X5r_tHDR_post_lib_loess_median_syn_r1-X5r_tHDR_post_lib_loess_median_ns_r1))*(X5r_tHDR_post_lib_loess_median_syn_r1r2-X5r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X5r_tHDR_pos_merge_df$X5r2_tHDR_post_lib_loess_r2_e.sns <- (X5r_tHDR_pos_merge_df$X5r2_tHDR_post_lib_loess_r2/(X5r_tHDR_post_lib_loess_median_syn_r2-X5r_tHDR_post_lib_loess_median_ns_r2))*(X5r_tHDR_post_lib_loess_median_syn_r1r2-X5r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X5r_tHDR_pos_merge_df$X5r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X5r_tHDR_pos_merge_df$X5r1_tHDR_post_lib_loess_r1_e.sns+X5r_tHDR_pos_merge_df$X5r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X5r_tHDR_pos_merge_df)[names(X5r_tHDR_pos_merge_df) == "X5r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X5r_tHDR_pos_merge_df$exon = 'X5'
X5r_tHDR_pos_merge_df$rep_type = 'r'
#X15r --
#X15r1
X15_posrange <- range(as.numeric(as.character(X15r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X15_posseq <- seq(from=X15_posrange[1], to=X15_posrange[2])
X15r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X15r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X15r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X15r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X15r1_tHDR_pre_lib_pred <- predict(X15r1_tHDR_pre_lib_ratio.lo, newdata = X15_posseq, se=TRUE)
X15r1_tHDR_pre_lib_pos_effects <- X15r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X15r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X15r1_tHDR_pre_lib_pos_effects)
X15r1_tHDR_pre_lib_pos_effects_ci <- X15r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X15r1_tHDR_pre_lib_pred$df)
X15r1_tHDR_pre_lib_pos_effects_ci_ymin = X15r1_tHDR_pre_lib_pos_effects - X15r1_tHDR_pre_lib_pos_effects_ci
X15r1_tHDR_pre_lib_pos_effects_ci_ymax = X15r1_tHDR_pre_lib_pos_effects + X15r1_tHDR_pre_lib_pos_effects_ci
X15r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X15_posseq, X15r1_tHDR_pre_lib_pos_effects, X15r1_tHDR_pre_lib_pos_effects.x, X15r1_tHDR_pre_lib_pos_effects_ci_ymin, X15r1_tHDR_pre_lib_pos_effects_ci_ymax, X15r1_tHDR_pre_lib_pred_se = X15r1_tHDR_pre_lib_pred$se.fit)
#X15r2
X15r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X15r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X15r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X15r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X15r2_tHDR_pre_lib_pred <- predict(X15r2_tHDR_pre_lib_ratio.lo, newdata = X15_posseq, se=TRUE)
X15r2_tHDR_pre_lib_pos_effects <- X15r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X15r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X15r2_tHDR_pre_lib_pos_effects)
X15r2_tHDR_pre_lib_pos_effects_ci <- X15r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X15r2_tHDR_pre_lib_pred$df)
X15r2_tHDR_pre_lib_pos_effects_ci_ymin = X15r2_tHDR_pre_lib_pos_effects - X15r2_tHDR_pre_lib_pos_effects_ci
X15r2_tHDR_pre_lib_pos_effects_ci_ymax = X15r2_tHDR_pre_lib_pos_effects + X15r2_tHDR_pre_lib_pos_effects_ci
X15r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X15_posseq, X15r2_tHDR_pre_lib_pos_effects, X15r2_tHDR_pre_lib_pos_effects.x, X15r2_tHDR_pre_lib_pos_effects_ci_ymin, X15r2_tHDR_pre_lib_pos_effects_ci_ymax, X15r2_tHDR_pre_lib_pred_se = X15r2_tHDR_pre_lib_pred$se.fit)
#X15r1r2
X15r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X15r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X15r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X15r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X15r1r2_tHDR_pre_lib_pred <- predict(X15r1r2_tHDR_pre_lib_ratio.lo, newdata = X15_posseq, se=TRUE)
X15r1r2_tHDR_pre_lib_pos_effects <- X15r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X15r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X15r1r2_tHDR_pre_lib_pos_effects)
X15r1r2_tHDR_pre_lib_pos_effects_ci <- X15r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X15r1r2_tHDR_pre_lib_pred$df)
X15r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X15r1r2_tHDR_pre_lib_pos_effects - X15r1r2_tHDR_pre_lib_pos_effects_ci
X15r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X15r1r2_tHDR_pre_lib_pos_effects + X15r1r2_tHDR_pre_lib_pos_effects_ci
X15r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X15_posseq, X15r1r2_tHDR_pre_lib_pos_effects, X15r1r2_tHDR_pre_lib_pos_effects.x, X15r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X15r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X15r1r2_tHDR_pre_lib_pred_se = X15r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X15r_tHDR_pos_merge_df <- merge(X15r1_prog_map_thresh5_df_r2_int_ord_df, X15r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X15r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X15r_tHDR_pos_merge_df <- merge(X15r_tHDR_pos_merge_df, X15r2_tHDR_pre_lib_pos_effects.df[, c("pos","X15r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X15r_tHDR_pos_merge_df <- merge(X15r_tHDR_pos_merge_df, X15r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X15r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X15r_tHDR_pos_merge_df$X15r1_tHDR_post_lib_loess_r1 <- log2(X15r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r1_tHDR_post_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r2_tHDR_post_lib_loess_r2 <- log2(X15r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r2_tHDR_post_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r1r2_tHDR_post_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X15r_tHDR_pos_merge_df$X15r1_tHDR_pre_lib_loess_r1 <- log2(X15r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r1_tHDR_pre_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r2_tHDR_pre_lib_loess_r2 <- log2(X15r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r2_tHDR_pre_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X15r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X15r_tHDR_pos_merge_df$X15r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X15r_tHDR_post_lib_loess_median_syn_r1 <- median(X15r_tHDR_pos_merge_df[which(X15r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X15r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X15r_tHDR_post_lib_loess_median_syn_r2 <- median(X15r_tHDR_pos_merge_df[which(X15r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X15r2_tHDR_post_lib_loess_r2')])
#defines A
X15r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X15r_tHDR_post_lib_loess_median_syn_r1,X15r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X15r_tHDR_post_lib_loess_median_ns_r1 <- median(X15r_tHDR_pos_merge_df[which(X15r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X15r1_tHDR_post_lib_loess_r1')])
#define c for r2
X15r_tHDR_post_lib_loess_median_ns_r2 <- median(X15r_tHDR_pos_merge_df[which(X15r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X15r2_tHDR_post_lib_loess_r2')])
#defines C
X15r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X15r_tHDR_post_lib_loess_median_ns_r1,X15r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X15r_tHDR_pos_merge_df$X15r1_tHDR_post_lib_loess_r1_e.sns <- (X15r_tHDR_pos_merge_df$X15r1_tHDR_post_lib_loess_r1/(X15r_tHDR_post_lib_loess_median_syn_r1-X15r_tHDR_post_lib_loess_median_ns_r1))*(X15r_tHDR_post_lib_loess_median_syn_r1r2-X15r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X15r_tHDR_pos_merge_df$X15r2_tHDR_post_lib_loess_r2_e.sns <- (X15r_tHDR_pos_merge_df$X15r2_tHDR_post_lib_loess_r2/(X15r_tHDR_post_lib_loess_median_syn_r2-X15r_tHDR_post_lib_loess_median_ns_r2))*(X15r_tHDR_post_lib_loess_median_syn_r1r2-X15r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X15r_tHDR_pos_merge_df$X15r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X15r_tHDR_pos_merge_df$X15r1_tHDR_post_lib_loess_r1_e.sns+X15r_tHDR_pos_merge_df$X15r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X15r_tHDR_pos_merge_df)[names(X15r_tHDR_pos_merge_df) == "X15r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X15r_tHDR_pos_merge_df$exon = 'X15'
X15r_tHDR_pos_merge_df$rep_type = 'r'
#X16r --
#X16r1
X16_posrange <- range(as.numeric(as.character(X16r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X16_posseq <- seq(from=X16_posrange[1], to=X16_posrange[2])
X16r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X16r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X16r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X16r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X16r1_tHDR_pre_lib_pred <- predict(X16r1_tHDR_pre_lib_ratio.lo, newdata = X16_posseq, se=TRUE)
X16r1_tHDR_pre_lib_pos_effects <- X16r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X16r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X16r1_tHDR_pre_lib_pos_effects)
X16r1_tHDR_pre_lib_pos_effects_ci <- X16r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X16r1_tHDR_pre_lib_pred$df)
X16r1_tHDR_pre_lib_pos_effects_ci_ymin = X16r1_tHDR_pre_lib_pos_effects - X16r1_tHDR_pre_lib_pos_effects_ci
X16r1_tHDR_pre_lib_pos_effects_ci_ymax = X16r1_tHDR_pre_lib_pos_effects + X16r1_tHDR_pre_lib_pos_effects_ci
X16r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X16_posseq, X16r1_tHDR_pre_lib_pos_effects, X16r1_tHDR_pre_lib_pos_effects.x, X16r1_tHDR_pre_lib_pos_effects_ci_ymin, X16r1_tHDR_pre_lib_pos_effects_ci_ymax, X16r1_tHDR_pre_lib_pred_se = X16r1_tHDR_pre_lib_pred$se.fit)
#X16r2
X16r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X16r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X16r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X16r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X16r2_tHDR_pre_lib_pred <- predict(X16r2_tHDR_pre_lib_ratio.lo, newdata = X16_posseq, se=TRUE)
X16r2_tHDR_pre_lib_pos_effects <- X16r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X16r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X16r2_tHDR_pre_lib_pos_effects)
X16r2_tHDR_pre_lib_pos_effects_ci <- X16r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X16r2_tHDR_pre_lib_pred$df)
X16r2_tHDR_pre_lib_pos_effects_ci_ymin = X16r2_tHDR_pre_lib_pos_effects - X16r2_tHDR_pre_lib_pos_effects_ci
X16r2_tHDR_pre_lib_pos_effects_ci_ymax = X16r2_tHDR_pre_lib_pos_effects + X16r2_tHDR_pre_lib_pos_effects_ci
X16r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X16_posseq, X16r2_tHDR_pre_lib_pos_effects, X16r2_tHDR_pre_lib_pos_effects.x, X16r2_tHDR_pre_lib_pos_effects_ci_ymin, X16r2_tHDR_pre_lib_pos_effects_ci_ymax, X16r2_tHDR_pre_lib_pred_se = X16r2_tHDR_pre_lib_pred$se.fit)
#X16r1r2
X16r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X16r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X16r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X16r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X16r1r2_tHDR_pre_lib_pred <- predict(X16r1r2_tHDR_pre_lib_ratio.lo, newdata = X16_posseq, se=TRUE)
X16r1r2_tHDR_pre_lib_pos_effects <- X16r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X16r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X16r1r2_tHDR_pre_lib_pos_effects)
X16r1r2_tHDR_pre_lib_pos_effects_ci <- X16r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X16r1r2_tHDR_pre_lib_pred$df)
X16r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X16r1r2_tHDR_pre_lib_pos_effects - X16r1r2_tHDR_pre_lib_pos_effects_ci
X16r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X16r1r2_tHDR_pre_lib_pos_effects + X16r1r2_tHDR_pre_lib_pos_effects_ci
X16r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X16_posseq, X16r1r2_tHDR_pre_lib_pos_effects, X16r1r2_tHDR_pre_lib_pos_effects.x, X16r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X16r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X16r1r2_tHDR_pre_lib_pred_se = X16r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X16r_tHDR_pos_merge_df <- merge(X16r1_prog_map_thresh5_df_r2_int_ord_df, X16r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X16r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X16r_tHDR_pos_merge_df <- merge(X16r_tHDR_pos_merge_df, X16r2_tHDR_pre_lib_pos_effects.df[, c("pos","X16r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X16r_tHDR_pos_merge_df <- merge(X16r_tHDR_pos_merge_df, X16r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X16r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X16r_tHDR_pos_merge_df$X16r1_tHDR_post_lib_loess_r1 <- log2(X16r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r1_tHDR_post_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r2_tHDR_post_lib_loess_r2 <- log2(X16r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r2_tHDR_post_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r1r2_tHDR_post_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X16r_tHDR_pos_merge_df$X16r1_tHDR_pre_lib_loess_r1 <- log2(X16r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r1_tHDR_pre_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r2_tHDR_pre_lib_loess_r2 <- log2(X16r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r2_tHDR_pre_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X16r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X16r_tHDR_pos_merge_df$X16r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X16r_tHDR_post_lib_loess_median_syn_r1 <- median(X16r_tHDR_pos_merge_df[which(X16r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X16r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X16r_tHDR_post_lib_loess_median_syn_r2 <- median(X16r_tHDR_pos_merge_df[which(X16r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X16r2_tHDR_post_lib_loess_r2')])
#defines A
X16r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X16r_tHDR_post_lib_loess_median_syn_r1,X16r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X16r_tHDR_post_lib_loess_median_ns_r1 <- median(X16r_tHDR_pos_merge_df[which(X16r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X16r1_tHDR_post_lib_loess_r1')])
#define c for r2
X16r_tHDR_post_lib_loess_median_ns_r2 <- median(X16r_tHDR_pos_merge_df[which(X16r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X16r2_tHDR_post_lib_loess_r2')])
#defines C
X16r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X16r_tHDR_post_lib_loess_median_ns_r1,X16r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X16r_tHDR_pos_merge_df$X16r1_tHDR_post_lib_loess_r1_e.sns <- (X16r_tHDR_pos_merge_df$X16r1_tHDR_post_lib_loess_r1/(X16r_tHDR_post_lib_loess_median_syn_r1-X16r_tHDR_post_lib_loess_median_ns_r1))*(X16r_tHDR_post_lib_loess_median_syn_r1r2-X16r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X16r_tHDR_pos_merge_df$X16r2_tHDR_post_lib_loess_r2_e.sns <- (X16r_tHDR_pos_merge_df$X16r2_tHDR_post_lib_loess_r2/(X16r_tHDR_post_lib_loess_median_syn_r2-X16r_tHDR_post_lib_loess_median_ns_r2))*(X16r_tHDR_post_lib_loess_median_syn_r1r2-X16r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X16r_tHDR_pos_merge_df$X16r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X16r_tHDR_pos_merge_df$X16r1_tHDR_post_lib_loess_r1_e.sns+X16r_tHDR_pos_merge_df$X16r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X16r_tHDR_pos_merge_df)[names(X16r_tHDR_pos_merge_df) == "X16r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X16r_tHDR_pos_merge_df$exon = 'X16'
X16r_tHDR_pos_merge_df$rep_type = 'r'
#X17r --
#X17r1
X17_posrange <- range(as.numeric(as.character(X17r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X17_posseq <- seq(from=X17_posrange[1], to=X17_posrange[2])
X17r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X17r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X17r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X17r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X17r1_tHDR_pre_lib_pred <- predict(X17r1_tHDR_pre_lib_ratio.lo, newdata = X17_posseq, se=TRUE)
X17r1_tHDR_pre_lib_pos_effects <- X17r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X17r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X17r1_tHDR_pre_lib_pos_effects)
X17r1_tHDR_pre_lib_pos_effects_ci <- X17r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X17r1_tHDR_pre_lib_pred$df)
X17r1_tHDR_pre_lib_pos_effects_ci_ymin = X17r1_tHDR_pre_lib_pos_effects - X17r1_tHDR_pre_lib_pos_effects_ci
X17r1_tHDR_pre_lib_pos_effects_ci_ymax = X17r1_tHDR_pre_lib_pos_effects + X17r1_tHDR_pre_lib_pos_effects_ci
X17r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X17_posseq, X17r1_tHDR_pre_lib_pos_effects, X17r1_tHDR_pre_lib_pos_effects.x, X17r1_tHDR_pre_lib_pos_effects_ci_ymin, X17r1_tHDR_pre_lib_pos_effects_ci_ymax, X17r1_tHDR_pre_lib_pred_se = X17r1_tHDR_pre_lib_pred$se.fit)
#X17r2
X17r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X17r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X17r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X17r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X17r2_tHDR_pre_lib_pred <- predict(X17r2_tHDR_pre_lib_ratio.lo, newdata = X17_posseq, se=TRUE)
X17r2_tHDR_pre_lib_pos_effects <- X17r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X17r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X17r2_tHDR_pre_lib_pos_effects)
X17r2_tHDR_pre_lib_pos_effects_ci <- X17r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X17r2_tHDR_pre_lib_pred$df)
X17r2_tHDR_pre_lib_pos_effects_ci_ymin = X17r2_tHDR_pre_lib_pos_effects - X17r2_tHDR_pre_lib_pos_effects_ci
X17r2_tHDR_pre_lib_pos_effects_ci_ymax = X17r2_tHDR_pre_lib_pos_effects + X17r2_tHDR_pre_lib_pos_effects_ci
X17r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X17_posseq, X17r2_tHDR_pre_lib_pos_effects, X17r2_tHDR_pre_lib_pos_effects.x, X17r2_tHDR_pre_lib_pos_effects_ci_ymin, X17r2_tHDR_pre_lib_pos_effects_ci_ymax, X17r2_tHDR_pre_lib_pred_se = X17r2_tHDR_pre_lib_pred$se.fit)
#X17r1r2
X17r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X17r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X17r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X17r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X17r1r2_tHDR_pre_lib_pred <- predict(X17r1r2_tHDR_pre_lib_ratio.lo, newdata = X17_posseq, se=TRUE)
X17r1r2_tHDR_pre_lib_pos_effects <- X17r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X17r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X17r1r2_tHDR_pre_lib_pos_effects)
X17r1r2_tHDR_pre_lib_pos_effects_ci <- X17r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X17r1r2_tHDR_pre_lib_pred$df)
X17r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X17r1r2_tHDR_pre_lib_pos_effects - X17r1r2_tHDR_pre_lib_pos_effects_ci
X17r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X17r1r2_tHDR_pre_lib_pos_effects + X17r1r2_tHDR_pre_lib_pos_effects_ci
X17r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X17_posseq, X17r1r2_tHDR_pre_lib_pos_effects, X17r1r2_tHDR_pre_lib_pos_effects.x, X17r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X17r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X17r1r2_tHDR_pre_lib_pred_se = X17r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X17r_tHDR_pos_merge_df <- merge(X17r1_prog_map_thresh5_df_r2_int_ord_df, X17r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X17r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X17r_tHDR_pos_merge_df <- merge(X17r_tHDR_pos_merge_df, X17r2_tHDR_pre_lib_pos_effects.df[, c("pos","X17r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X17r_tHDR_pos_merge_df <- merge(X17r_tHDR_pos_merge_df, X17r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X17r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X17r_tHDR_pos_merge_df$X17r1_tHDR_post_lib_loess_r1 <- log2(X17r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r1_tHDR_post_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r2_tHDR_post_lib_loess_r2 <- log2(X17r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r2_tHDR_post_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$r2_tHDR_post_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r1r2_tHDR_post_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$tHDR_post_lib_ratio_r1r2_mean_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
###same for pre-lib
X17r_tHDR_pos_merge_df$X17r1_tHDR_pre_lib_loess_r1 <- log2(X17r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r1_tHDR_pre_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r2_tHDR_pre_lib_loess_r2 <- log2(X17r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r2_tHDR_pre_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$r2_tHDR_pre_lib_ratio_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_loess_r1r2 <- log2(X17r_tHDR_pos_merge_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm) - X17r_tHDR_pos_merge_df$X17r1r2_tHDR_pre_lib_pos_effects.x
#these plots have set cartesians for comparing on same axes -- plot to illustrate how pos-correction works.
#plotting r1: original data, fit 1, and fit 2, for tHDR_pre_lib
#redo same plot but with r2:
#plot of the averaged data with all 3 fit lines (SYN is average, two indiv. reps in gray), and then corrected according to averaged fit (two plots)
#plotting original post-lib data (mean), w/ all 3 fits, and subtracted post-lib data using the averages fit.
#plot the two independently processed replicates against each other:
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#test bimodality with a histogram (using each rep's post-lib w. the mean fit):
#mean histogram using mean fit
#include a within-replicate sns normalization (e.sns) next
#general formula: b/(a-c)*(A-C) where b is snv, a is exon med syn, c is exon med nonsense, A is global med syn, C is global med nonsense.
#defines a for r1
X17r_tHDR_post_lib_loess_median_syn_r1 <- median(X17r_tHDR_pos_merge_df[which(X17r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X17r1_tHDR_post_lib_loess_r1')])
#defines a for r2
X17r_tHDR_post_lib_loess_median_syn_r2 <- median(X17r_tHDR_pos_merge_df[which(X17r_tHDR_pos_merge_df$conseq == 'SYNONYMOUS'),c('X17r2_tHDR_post_lib_loess_r2')])
#defines A
X17r_tHDR_post_lib_loess_median_syn_r1r2 <- mean(c(X17r_tHDR_post_lib_loess_median_syn_r1,X17r_tHDR_post_lib_loess_median_syn_r2))
#define c for r1
X17r_tHDR_post_lib_loess_median_ns_r1 <- median(X17r_tHDR_pos_merge_df[which(X17r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X17r1_tHDR_post_lib_loess_r1')])
#define c for r2
X17r_tHDR_post_lib_loess_median_ns_r2 <- median(X17r_tHDR_pos_merge_df[which(X17r_tHDR_pos_merge_df$conseq == 'STOP_GAINED'),c('X17r2_tHDR_post_lib_loess_r2')])
#defines C
X17r_tHDR_post_lib_loess_median_ns_r1r2 <- mean(c(X17r_tHDR_post_lib_loess_median_ns_r1,X17r_tHDR_post_lib_loess_median_ns_r2))
#calculate new b values for r1 'e.sns' = exon syn/ns norm.
X17r_tHDR_pos_merge_df$X17r1_tHDR_post_lib_loess_r1_e.sns <- (X17r_tHDR_pos_merge_df$X17r1_tHDR_post_lib_loess_r1/(X17r_tHDR_post_lib_loess_median_syn_r1-X17r_tHDR_post_lib_loess_median_ns_r1))*(X17r_tHDR_post_lib_loess_median_syn_r1r2-X17r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate new b values for r2 'e.sns' = exon syn/ns norm.
X17r_tHDR_pos_merge_df$X17r2_tHDR_post_lib_loess_r2_e.sns <- (X17r_tHDR_pos_merge_df$X17r2_tHDR_post_lib_loess_r2/(X17r_tHDR_post_lib_loess_median_syn_r2-X17r_tHDR_post_lib_loess_median_ns_r2))*(X17r_tHDR_post_lib_loess_median_syn_r1r2-X17r_tHDR_post_lib_loess_median_ns_r1r2)
#calculate a new post_lib mean based on individually fit, exon-normalized data
X17r_tHDR_pos_merge_df$X17r_tHDR_post_lib_loess_e.sns_r1r2_mean <- (X17r_tHDR_pos_merge_df$X17r1_tHDR_post_lib_loess_r1_e.sns+X17r_tHDR_pos_merge_df$X17r2_tHDR_post_lib_loess_r2_e.sns)/2
#histograms like above but with e.sns post-lib measurements
#test bimodality with a histogram (using each rep's post-lib w. each rep's fit):
#histo of exon mean from individually processed replicates:
#comparing the averaged processing (top) with individual replicate processing (bottom):
##change the names of these categories to take out the exon number so they can be merged into a single df later.
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_pre_lib_pos_effects.x"] <- "r1_tHDR_pre_lib_pos_effects.x"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_pre_lib_pos_effects.x"] <- "r2_tHDR_pre_lib_pos_effects.x"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1r2_tHDR_pre_lib_pos_effects.x"] <- "r1r2_tHDR_pre_lib_pos_effects.x"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_post_lib_loess_r1"] <- "r1_tHDR_post_lib_loess_r1"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_post_lib_loess_r1r2"] <- "r1_tHDR_post_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_post_lib_loess_r2"] <- "r2_tHDR_post_lib_loess_r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_post_lib_loess_r1r2"] <- "r2_tHDR_post_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1r2_tHDR_post_lib_loess_r1r2"] <- "r1r2_tHDR_post_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_pre_lib_loess_r1"] <- "r1_tHDR_pre_lib_loess_r1"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_pre_lib_loess_r1r2"] <- "r1_tHDR_pre_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_pre_lib_loess_r2"] <- "r2_tHDR_pre_lib_loess_r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_pre_lib_loess_r1r2"] <- "r2_tHDR_pre_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1r2_tHDR_pre_lib_loess_r1r2"] <- "r1r2_tHDR_pre_lib_loess_r1r2"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r1_tHDR_post_lib_loess_r1_e.sns"] <- "r1_tHDR_post_lib_loess_r1_e.sns"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r2_tHDR_post_lib_loess_r2_e.sns"] <- "r2_tHDR_post_lib_loess_r2_e.sns"
names(X17r_tHDR_pos_merge_df)[names(X17r_tHDR_pos_merge_df) == "X17r_tHDR_post_lib_loess_e.sns_r1r2_mean"] <- "r_tHDR_post_lib_loess_e.sns_r1r2_mean"
#add the name of the exon and the replicate type (e.g. 'r')
X17r_tHDR_pos_merge_df$exon = 'X17'
X17r_tHDR_pos_merge_df$rep_type = 'r'
#X18r --
#X18r1
X18_posrange <- range(as.numeric(as.character(X18r1_prog_map_thresh5_df_r2_int_ord_df$pos)))
X18_posseq <- seq(from=X18_posrange[1], to=X18_posrange[2])
X18r1_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X18r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X18r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_synnorm > 0.8 & X18r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X18r1_tHDR_pre_lib_pred <- predict(X18r1_tHDR_pre_lib_ratio.lo, newdata = X18_posseq, se=TRUE)
X18r1_tHDR_pre_lib_pos_effects <- X18r1_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X18r1_tHDR_pre_lib_pos_effects.x <- extend_fit(X18r1_tHDR_pre_lib_pos_effects)
X18r1_tHDR_pre_lib_pos_effects_ci <- X18r1_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X18r1_tHDR_pre_lib_pred$df)
X18r1_tHDR_pre_lib_pos_effects_ci_ymin = X18r1_tHDR_pre_lib_pos_effects - X18r1_tHDR_pre_lib_pos_effects_ci
X18r1_tHDR_pre_lib_pos_effects_ci_ymax = X18r1_tHDR_pre_lib_pos_effects + X18r1_tHDR_pre_lib_pos_effects_ci
X18r1_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X18_posseq, X18r1_tHDR_pre_lib_pos_effects, X18r1_tHDR_pre_lib_pos_effects.x, X18r1_tHDR_pre_lib_pos_effects_ci_ymin, X18r1_tHDR_pre_lib_pos_effects_ci_ymax, X18r1_tHDR_pre_lib_pred_se = X18r1_tHDR_pre_lib_pred$se.fit)
#X18r2
X18r2_tHDR_pre_lib_ratio.lo <- loess(log2(r2_tHDR_pre_lib_ratio_synnorm) ~ as.numeric(as.character(pos)), X18r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X18r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_post_pre_ratio_synnorm > 0.8 & X18r1_prog_map_thresh5_df_r2_int_ord_df$r2_tHDR_pre_lib_ratio_synnorm > 0.5), model=TRUE,span=0.15)
X18r2_tHDR_pre_lib_pred <- predict(X18r2_tHDR_pre_lib_ratio.lo, newdata = X18_posseq, se=TRUE)
X18r2_tHDR_pre_lib_pos_effects <- X18r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X18r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X18r2_tHDR_pre_lib_pos_effects)
X18r2_tHDR_pre_lib_pos_effects_ci <- X18r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X18r2_tHDR_pre_lib_pred$df)
X18r2_tHDR_pre_lib_pos_effects_ci_ymin = X18r2_tHDR_pre_lib_pos_effects - X18r2_tHDR_pre_lib_pos_effects_ci
X18r2_tHDR_pre_lib_pos_effects_ci_ymax = X18r2_tHDR_pre_lib_pos_effects + X18r2_tHDR_pre_lib_pos_effects_ci
X18r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X18_posseq, X18r2_tHDR_pre_lib_pos_effects, X18r2_tHDR_pre_lib_pos_effects.x, X18r2_tHDR_pre_lib_pos_effects_ci_ymin, X18r2_tHDR_pre_lib_pos_effects_ci_ymax, X18r2_tHDR_pre_lib_pred_se = X18r2_tHDR_pre_lib_pred$se.fit)
#X18r1r2
X18r1r2_tHDR_pre_lib_ratio.lo <- loess(log2(tHDR_pre_lib_ratio_r1r2_mean_synnorm) ~ as.numeric(as.character(pos)), X18r1_prog_map_thresh5_df_r2_int_ord_df, subset = which(X18r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_post_pre_ratio_r1r2_mean_synnorm > 0.8 & X18r1_prog_map_thresh5_df_r2_int_ord_df$tHDR_pre_lib_ratio_r1r2_mean_synnorm > 0.5), model=TRUE,span=0.15)
X18r1r2_tHDR_pre_lib_pred <- predict(X18r1r2_tHDR_pre_lib_ratio.lo, newdata = X18_posseq, se=TRUE)
X18r1r2_tHDR_pre_lib_pos_effects <- X18r1r2_tHDR_pre_lib_pred$fit
#.x represents the extended pos_effects.
X18r1r2_tHDR_pre_lib_pos_effects.x <- extend_fit(X18r1r2_tHDR_pre_lib_pos_effects)
X18r1r2_tHDR_pre_lib_pos_effects_ci <- X18r1r2_tHDR_pre_lib_pred$se.fit * qt(0.95 / 2 + .5, X18r1r2_tHDR_pre_lib_pred$df)
X18r1r2_tHDR_pre_lib_pos_effects_ci_ymin = X18r1r2_tHDR_pre_lib_pos_effects - X18r1r2_tHDR_pre_lib_pos_effects_ci
X18r1r2_tHDR_pre_lib_pos_effects_ci_ymax = X18r1r2_tHDR_pre_lib_pos_effects + X18r1r2_tHDR_pre_lib_pos_effects_ci
X18r1r2_tHDR_pre_lib_pos_effects.df <- data.frame(pos = X18_posseq, X18r1r2_tHDR_pre_lib_pos_effects, X18r1r2_tHDR_pre_lib_pos_effects.x, X18r1r2_tHDR_pre_lib_pos_effects_ci_ymin, X18r1r2_tHDR_pre_lib_pos_effects_ci_ymax, X18r1r2_tHDR_pre_lib_pred_se = X18r1r2_tHDR_pre_lib_pred$se.fit)
#merge into a single df (merge original df, and pos effects (extended) df's from all 3 fits -- r1, r2, r1r2)
X18r_tHDR_pos_merge_df <- merge(X18r1_prog_map_thresh5_df_r2_int_ord_df, X18r1_tHDR_pre_lib_pos_effects.df[, c("pos", "X18r1_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X18r_tHDR_pos_merge_df <- merge(X18r_tHDR_pos_merge_df, X18r2_tHDR_pre_lib_pos_effects.df[, c("pos","X18r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
X18r_tHDR_pos_merge_df <- merge(X18r_tHDR_pos_merge_df, X18r1r2_tHDR_pre_lib_pos_effects.df[, c("pos","X18r1r2_tHDR_pre_lib_pos_effects.x")], by="pos",all.x=TRUE)
#Calculate adjusted ratios and put into df (along with affect sizes)
#5 adjustments for each post-lib and pre-lib measurement (e.g. r1 vs. r1_fit and vs. mean_fit)
X18r_tHDR_pos_merge_df$X18r1_tHDR_post_lib_loess_r1 <- log2(X18r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X18r_tHDR_pos_merge_df$X18r1_tHDR_pre_lib_pos_effects.x
X18r_tHDR_pos_merge_df$X18r1_tHDR_post_lib_loess_r1r2 <- log2(X18r_tHDR_pos_merge_df$tHDR_post_lib_ratio_synnorm) - X18r_tHDR_pos_merge_df$X18r1r2_tHDR_pre_lib_pos_effects.x