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inst/doc/TNBC.R
In funkycells: Functional Data Analysis for Multiplexed Cell Images
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
# knitr::opts_chunk$set(eval = !is_check)
## ----setup--------------------------------------------------------------------
library(funkycells)
## ----define_functions, echo=FALSE---------------------------------------------
full_run <- FALSE # This runs only computations that are feasibly quick
paperRun <- FALSE * full_run # This creates and saves figures as in paper
save_path <- tempdir() # Location to save files
specify_decimal <- function(x, k) {
trimws(format(round(x, k), nsmall = k))
}
## ----getData------------------------------------------------------------------
TNBC_pheno <- TNBC_pheno
TNBC_meta <- TNBC_meta
## ----specifyPheno-------------------------------------------------------------
phenos <- unique(TNBC_pheno$Phenotype)
pheno_interactions <- rbind(
data.frame(t(combn(phenos, 2))),
data.frame("X1" = phenos, "X2" = phenos)
)
phenos_subset <- c(
"Tumor", "CD3T", "CD4T", "CD8T", "B", "DC",
"DCMono", "Macrophage", "MonoNeu", "NK", "Treg"
)
pheno_interactions_subset <- data.frame(
Var1 = rep("Tumor", 11),
Var2 = c(
"Tumor", "CD3T", "CD4T", "CD8T", "B", "DC",
"DCMono", "Macrophage", "MonoNeu", "NK", "Treg"
)
)
## ----TNBCInfo-----------------------------------------------------------------
data_pheno_stat <- data.frame("pheno" = phenos)
for (person in unique(TNBC_pheno$Person)) {
tmp <- as.data.frame(table(TNBC_pheno[TNBC_pheno$Person == person, "Phenotype"]))
colnames(tmp) <- c("pheno", person)
data_pheno_stat <- merge(x = data_pheno_stat, y = tmp, all.x = TRUE)
}
agent_info_subset <- data.frame(data_pheno_stat[1],
"mean" = rowMeans(data_pheno_stat[-1], na.rm = TRUE),
"med" = apply(data_pheno_stat[-1], 1, median, na.rm = TRUE)
)
## ----paper_K_example, echo=FALSE, eval=paperRun-------------------------------
tnbc_fig <- plotPP(TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 3, colorGuide = "none", dropAxes = T
)
png(paste0(save_path,"/paper/tnbc_person2_phenotypes.png"),
width = 800, height = 800)
print(tnbc_fig)
dev.off()
kf <- getKFunction(
data = TNBC_pheno[TNBC_pheno$Person == 2, -1],
agents = c("Tumor", "MonoNeu"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
k_fig <- ggplot2::ggplot() +
ggplot2::geom_line(ggplot2::aes(x = r, y = K1), data = kf, linewidth = 2) +
ggplot2::geom_line(ggplot2::aes(x = seq(0, 50, 1), y = pi * seq(0, 50, 1)^2),
linewidth = 2, col = "red", linetype = "dashed"
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_blank()
)
png(paste0(save_path,"/paper/tnbc_person2_tumor_mononeu_k.png"),
width = 800, height = 800)
print(k_fig)
dev.off()
## ----buildNUllData------------------------------------------------------------
set.seed(123)
dat0 <- simulatePP(
agentVarData =
data.frame(
"outcome" = c(0, 1),
"c1" = c(0, 0),
"c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
"c4" = c(0, 0),
"c5" = c(0, 0), "c6" = c(0, 0),
"c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
"c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
"c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
"c13" = c(0, 0), "c14" = c(0, 0),
"c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
),
agentKappaData = data.frame(
"agent" = paste0("c", 1:16),
"clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
"kappa" = c(
rbinom(1, 100, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 30, 0.5),
rbinom(1, 80, 0.5),
rbinom(1, 350, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 120, 0.5),
rbinom(1, 150, 0.5),
rbinom(2, 250, 0.5),
rbinom(1, 600, 0.5),
rbinom(1, 60, 0.5),
rbinom(2, 140, 0.5),
rbinom(1, 20, 0.5),
rbinom(1, 5, 0.5)
)
),
unitsPerOutcome = 15,
replicatesPerUnit = 1,
silent = FALSE
)
dat1 <- simulatePP(
agentVarData =
data.frame(
"outcome" = c(0, 1),
"c1" = c(0, 0),
"c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
"c4" = c(0, 0),
"c5" = c(0, 0), "c6" = c(0, 0),
"c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
"c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
"c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
"c13" = c(0, 0), "c14" = c(0, 0),
"c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
),
agentKappaData = data.frame(
"agent" = paste0("c", 1:16),
"clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
"kappa" = c(
rbinom(1, 100, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 30, 0.5),
rbinom(1, 80, 0.5),
rbinom(1, 350, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 120, 0.5),
rbinom(1, 150, 0.5),
rbinom(2, 250, 0.5),
rbinom(1, 600, 0.5),
rbinom(1, 60, 0.5),
rbinom(2, 140, 0.5),
rbinom(1, 20, 0.5),
rbinom(1, 5, 0.5)
)
),
unitsPerOutcome = 2,
replicatesPerUnit = 1,
silent = F
)
dat1$unit <- ifelse(dat1$unit == "u1", "u31",
ifelse(dat1$unit == "u2", "u32",
ifelse(dat1$unit == "u3", "u33",
ifelse(dat1$unit == "u4", "u34", NA)
)
)
)
dat1$replicate <- ifelse(dat1$replicate == "1", "31",
ifelse(dat1$replicate == "2", "32",
ifelse(dat1$replicate == "3", "33",
ifelse(dat1$replicate == "4", "34", NA)
)
)
)
dat <- rbind(dat0, dat1)
## ----tnbc_image, fig.width=5, fig.height=5, fig.cap="TNBC Patient Image"------
plotPP(
data = TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 1, dropAxes = T, colorGuide = "none"
)
## ----sim_image, fig.width=5, fig.height=5, fig.cap="Simulated Patient Image"----
plotPP(dat[dat$replicate == 18, c("x", "y", "type")],
ptSize = 1, dropAxes = T, colorGuide = "none"
)
## ----paper_simtnbc_comparision, echo=FALSE, eval=paperRun---------------------
tmp <- length(table(dat[dat$replicate == 1, c("type")]))
clrs <- scales::hue_pal()(tmp)
tnbc_image <- plotPP(
data = TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 3, dropAxes = T, colorGuide = "none",
colors = clrs[-length(clrs)]
)
png(paste0(save_path,"/paper/tnbc_image.png"),
width = 800, height = 800)
print(tnbc_image)
dev.off()
sim_image <- plotPP(
data = dat[dat$replicate == 18, c("x", "y", "type")],
ptSize = 3, dropAxes = T, colorGuide = "none",
colors = c(clrs[1:2], clrs[16], clrs[3:7], clrs[15], clrs[8:14])
)
png(paste0(save_path,"/paper/sim_image.png"),
width = 800, height = 800)
print(sim_image)
dev.off()
## ----simulateNoInteraction, eval=full_run-------------------------------------
# cells <- paste0("c", 1:16)
# cells_interactions <- rbind(
# data.frame(t(combn(cells, 2))),
# data.frame("X1" = cells, "X2" = cells)
# )
#
# set.seed(12345)
# pcaData <- getKsPCAData(
# data = dat, replicate = "replicate",
# agents_df = cells_interactions,
# xRange = c(0, 1), yRange = c(0, 1),
# silent = F
# )
# pcaMeta <- simulateMeta(pcaData,
# metaInfo = data.frame(
# "var" = c("age"),
# "rdist" = c("rnorm"),
# "outcome_0" = c("25"),
# "outcome_1" = c("25")
# )
# )
## ----analyzeNoEffect, eval=full_run-------------------------------------------
# set.seed(123)
# rfcv <- funkyModel(
# data = pcaMeta,
# outcome = "outcome",
# unit = "unit",
# metaNames = c("age")
# )
## ----plot_noeffect_full, fig.width=5, fig.height=5, fig.cap="No Effect Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----plot_noeffect_top25, fig.width=5, fig.height=5, fig.cap="No Effect Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_noEffect, echo=FALSE, eval=paperRun--------------------------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/noeffect_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.x = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/noeffect_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----effectSimulationAndModel, eval=full_run----------------------------------
# set.seed(123456)
# dat0 <- simulatePP(
# agentVarData =
# data.frame(
# "outcome" = c(0, 1),
# "c1" = c(0, 0),
# "c2" = c(1 / 25, 1 / 60), "c3" = c(1 / 50, 1 / 10),
# "c4" = c(0, 0),
# "c5" = c(0, 0), "c6" = c(0, 0),
# "c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
# "c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
# "c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
# "c13" = c(0, 0), "c14" = c(0, 0),
# "c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
# ),
# agentKappaData = data.frame(
# "agent" = paste0("c", 1:16),
# "clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
# "kappa" = c(
# rbinom(1, 100, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 30, 0.5),
# rbinom(1, 80, 0.5),
# rbinom(1, 350, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 120, 0.5),
# rbinom(1, 150, 0.5),
# rbinom(2, 250, 0.5),
# rbinom(1, 600, 0.5),
# rbinom(1, 60, 0.5),
# rbinom(2, 140, 0.5),
# rbinom(1, 20, 0.5),
# rbinom(1, 5, 0.5)
# )
# ),
# unitsPerOutcome = 15,
# replicatesPerUnit = 1,
# silent = F
# )
# dat1 <- simulatePP(
# agentVarData =
# data.frame(
# "outcome" = c(0, 1),
# "c1" = c(0, 0),
# "c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
# "c4" = c(0, 0),
# "c5" = c(0, 0), "c6" = c(0, 0),
# "c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
# "c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
# "c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
# "c13" = c(0, 0), "c14" = c(0, 0),
# "c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
# ),
# agentKappaData = data.frame(
# "agent" = paste0("c", 1:16),
# "clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
# "kappa" = c(
# rbinom(1, 100, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 30, 0.5),
# rbinom(1, 80, 0.5),
# rbinom(1, 350, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 120, 0.5),
# rbinom(1, 150, 0.5),
# rbinom(2, 250, 0.5),
# rbinom(1, 600, 0.5),
# rbinom(1, 60, 0.5),
# rbinom(2, 140, 0.5),
# rbinom(1, 20, 0.5),
# rbinom(1, 5, 0.5)
# )
# ),
# unitsPerOutcome = 2,
# replicatesPerUnit = 1,
# silent = F
# )
# dat1$unit <- ifelse(dat1$unit == "u1", "u31",
# ifelse(dat1$unit == "u2", "u32",
# ifelse(dat1$unit == "u3", "u33",
# ifelse(dat1$unit == "u4", "u34", NA)
# )
# )
# )
# dat1$replicate <- ifelse(dat1$replicate == "1", "31",
# ifelse(dat1$replicate == "2", "32",
# ifelse(dat1$replicate == "3", "33",
# ifelse(dat1$replicate == "4", "34", NA)
# )
# )
# )
# dat <- rbind(dat0, dat1)
#
# cells <- paste0("c", 1:16)
# cells_interactions <- rbind(
# data.frame(t(combn(cells, 2))),
# data.frame("X1" = cells, "X2" = cells)
# )
#
# pcaData <- getKsPCAData(
# data = dat, replicate = "replicate",
# agents_df = cells_interactions,
# xRange = c(0, 1), yRange = c(0, 1),
# silent = F
# )
# pcaMeta <- simulateMeta(pcaData,
# metaInfo = data.frame(
# "var" = c("age"),
# "rdist" = c("rnorm"),
# "outcome_0" = c("25"),
# "outcome_1" = c("27")
# )
# )
#
# rfcv <- funkyModel(
# data = pcaMeta,
# outcome = "outcome",
# unit = "unit",
# metaNames = c("age")
# )
## ----plot_effect_full, fig.width=5, fig.height=5, fig.cap="Effect Simulation Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----plot_effect_top25, fig.width=5, fig.height=5, fig.cap="Effect Simulation Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_effect, echo=FALSE, eval=paperRun----------------------------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/effect_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.x = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/effect_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----tnbc_pheno_model, eval=full_run------------------------------------------
# dataPCA_pheno <- getKsPCAData(
# data = TNBC_pheno, unit = "Person",
# agents_df = pheno_interactions,
# rCheckVals = seq(0, 50, 1)
# )
#
# dataPCAAge_pheno <- merge(dataPCA_pheno, TNBC_meta)
#
# set.seed(123456)
# rfcv <- funkyModel(
# data = dataPCAAge_pheno, K = 10,
# outcome = "Class", unit = "Person",
# metaNames = c("Age"), synthetics = 100,
# alpha = 0.05, silent = FALSE,
# subsetPlotSize = 25
# )
## ----tnbc_full, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----tnbc_top25, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_tnbc_variable_importance, echo=FALSE, eval=paperRun----------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
# axis.text = ggplot2::element_text(size = 18),
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/tnbc_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(size = 14),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 17)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/tnbc_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----significantTNBCK---------------------------------------------------------
tmp <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 0, -1],
c("Tumor", "Tumor"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp1 <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 1, -1],
c("Tumor", "Tumor"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp_1 <- tidyr::pivot_longer(data = tmp, cols = -r)
tmp1_1 <- tidyr::pivot_longer(data = tmp1, cols = -r)
data_plot <- rbind(
data.frame(
"r" = tmp_1$r,
"K" = tmp_1$value,
"unit" = tmp_1$name,
"outcome" = "0"
),
data.frame(
"r" = tmp1_1$r,
"K" = tmp1_1$value,
"unit" = paste0(tmp1_1$name, "_1"),
"outcome" = "1"
)
)
## ----significantTNBCKPlot, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Significant K Function"----
plot_K_functions(data_plot)
## ----paper_sig_K_functions, echo=FALSE, eval=paperRun-------------------------
data <- data_plot
# Prep Legend
info <- unique(data[, c("unit", "outcome")])
info$Missing <- NA
for (i in 1:nrow(info)) {
info[i, 3] <- nrow(data[data$unit == info$unit[i] &
!stats::complete.cases(data), ]) > 0
}
info_labels <- data.frame(
"outcome" = unique(data$outcome),
"units" = NA,
"Missing" = NA,
"Label" = NA
)
for (i in 1:nrow(info_labels)) {
info_labels[i, 2] <- nrow(info[info$outcome == info_labels[i, 1], ])
info_labels[i, 3] <- nrow(info[info$outcome == info_labels[i, 1] &
info$Missing, ])
# Only label outcomes with numbers if at least one is missing in whole set
if (sum(info$Missing) != 0) {
info_labels[i, 4] <- paste0(
info_labels[i, 1], " (",
info_labels[i, 2] - info_labels[i, 3], "/",
info_labels[i, 2], ")"
)
} else {
info_labels[i, 4] <- info_labels[i, 1]
}
}
# Build Averages
data_wide <- tidyr::pivot_wider(data,
names_from = "unit",
values_from = "K"
)
data_avg <- data.frame("r" = unique(data_wide$r))
for (i in 1:nrow(info_labels)) {
data_avg[[info_labels[i, "outcome"]]] <-
rowMeans(data_wide[data_wide$outcome == info_labels[i, "outcome"], -c(1:2)],
na.rm = TRUE
)
}
data_avg <- tidyr::pivot_longer(data_avg, cols = -r)
colnames(data_avg) <- c("r", "outcome", "Value")
# Plot
return_plot <-
ggplot2::ggplot(
data = stats::na.omit(data),
ggplot2::aes(
x = r, y = K,
group = unit, color = outcome
)
) +
ggplot2::geom_line(alpha = 0.2, linewidth = 1.25) +
ggplot2::geom_line(
ggplot2::aes(
x = r, y = Value,
group = outcome, color = outcome
),
data = data_avg, linewidth = 2
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
# axis.title = ggplot2::element_text(size = 22),
axis.title = ggplot2::element_blank(),
legend.position = "none"
) # +
# ggplot2::geom_line(ggplot2::aes(x = r, y = pi * r^2,
# group = outcome),
# data = data_avg, linewidth = 1.25,
# color = "black", linetype = "dashed"
# )
png(paste0(save_path,"/paper/TNBC_sig_KFunctions.png"))
print(return_plot)
dev.off()
## ----insignificantTNBCK-------------------------------------------------------
tmp <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 0, -1],
c("CD4T", "Endothelial"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp1 <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 1, -1],
c("CD4T", "Endothelial"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp_1 <- tidyr::pivot_longer(data = tmp, cols = -r)
tmp1_1 <- tidyr::pivot_longer(data = tmp1, cols = -r)
data_plot <- rbind(
data.frame(
"r" = tmp_1$r,
"K" = tmp_1$value,
"unit" = tmp_1$name,
"outcome" = "0"
),
data.frame(
"r" = tmp1_1$r,
"K" = tmp1_1$value,
"unit" = paste0(tmp1_1$name, "_1"),
"outcome" = "1"
)
)
## ----insignificantTNBCKPlot, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Insignficant K Function"----
plot_K_functions(data_plot)
## ----paper_insig_K_functions, echo=FALSE, eval=paperRun-----------------------
data <- data_plot
# Prep Legend
info <- unique(data[, c("unit", "outcome")])
info$Missing <- NA
for (i in 1:nrow(info)) {
info[i, 3] <- nrow(data[data$unit == info$unit[i] &
!stats::complete.cases(data), ]) > 0
}
info_labels <- data.frame(
"outcome" = unique(data$outcome),
"units" = NA,
"Missing" = NA,
"Label" = NA
)
for (i in 1:nrow(info_labels)) {
info_labels[i, 2] <- nrow(info[info$outcome == info_labels[i, 1], ])
info_labels[i, 3] <- nrow(info[info$outcome == info_labels[i, 1] &
info$Missing, ])
# Only label outcomes with numbers if at least one is missing in whole set
if (sum(info$Missing) != 0) {
info_labels[i, 4] <- paste0(
info_labels[i, 1], " (",
info_labels[i, 2] - info_labels[i, 3], "/",
info_labels[i, 2], ")"
)
} else {
info_labels[i, 4] <- info_labels[i, 1]
}
}
# Build Averages
data_wide <- tidyr::pivot_wider(data,
names_from = "unit",
values_from = "K"
)
data_avg <- data.frame("r" = unique(data_wide$r))
for (i in 1:nrow(info_labels)) {
data_avg[[info_labels[i, "outcome"]]] <-
rowMeans(data_wide[data_wide$outcome == info_labels[i, "outcome"], -c(1:2)],
na.rm = TRUE
)
}
data_avg <- tidyr::pivot_longer(data_avg, cols = -r)
colnames(data_avg) <- c("r", "outcome", "Value")
# Plot
return_plot <-
ggplot2::ggplot(
data = stats::na.omit(data),
ggplot2::aes(
x = r, y = K,
group = unit, color = outcome
)
) +
ggplot2::geom_line(alpha = 0.2, linewidth = 1.25) +
ggplot2::geom_line(
ggplot2::aes(
x = r, y = Value,
group = outcome, color = outcome
),
data = data_avg, linewidth = 2
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
# axis.title = ggplot2::element_text(size = 22),
axis.title = ggplot2::element_blank(),
legend.position = "none"
) # +
# ggplot2::geom_line(ggplot2::aes(x = r, y = pi * r^2,
# group = outcome),
# data = data_avg, linewidth = 1.25,
# color = "black", linetype = "dashed"
# )
png(paste0(save_path,"/paper/TNBC_insig_KFunctions.png"))
print(return_plot)
dev.off()
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
# knitr::opts_chunk$set(eval = !is_check)
## ----setup--------------------------------------------------------------------
library(funkycells)
## ----define_functions, echo=FALSE---------------------------------------------
full_run <- FALSE # This runs only computations that are feasibly quick
paperRun <- FALSE * full_run # This creates and saves figures as in paper
save_path <- tempdir() # Location to save files
specify_decimal <- function(x, k) {
trimws(format(round(x, k), nsmall = k))
}
## ----getData------------------------------------------------------------------
TNBC_pheno <- TNBC_pheno
TNBC_meta <- TNBC_meta
## ----specifyPheno-------------------------------------------------------------
phenos <- unique(TNBC_pheno$Phenotype)
pheno_interactions <- rbind(
data.frame(t(combn(phenos, 2))),
data.frame("X1" = phenos, "X2" = phenos)
)
phenos_subset <- c(
"Tumor", "CD3T", "CD4T", "CD8T", "B", "DC",
"DCMono", "Macrophage", "MonoNeu", "NK", "Treg"
)
pheno_interactions_subset <- data.frame(
Var1 = rep("Tumor", 11),
Var2 = c(
"Tumor", "CD3T", "CD4T", "CD8T", "B", "DC",
"DCMono", "Macrophage", "MonoNeu", "NK", "Treg"
)
)
## ----TNBCInfo-----------------------------------------------------------------
data_pheno_stat <- data.frame("pheno" = phenos)
for (person in unique(TNBC_pheno$Person)) {
tmp <- as.data.frame(table(TNBC_pheno[TNBC_pheno$Person == person, "Phenotype"]))
colnames(tmp) <- c("pheno", person)
data_pheno_stat <- merge(x = data_pheno_stat, y = tmp, all.x = TRUE)
}
agent_info_subset <- data.frame(data_pheno_stat[1],
"mean" = rowMeans(data_pheno_stat[-1], na.rm = TRUE),
"med" = apply(data_pheno_stat[-1], 1, median, na.rm = TRUE)
)
## ----paper_K_example, echo=FALSE, eval=paperRun-------------------------------
tnbc_fig <- plotPP(TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 3, colorGuide = "none", dropAxes = T
)
png(paste0(save_path,"/paper/tnbc_person2_phenotypes.png"),
width = 800, height = 800)
print(tnbc_fig)
dev.off()
kf <- getKFunction(
data = TNBC_pheno[TNBC_pheno$Person == 2, -1],
agents = c("Tumor", "MonoNeu"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
k_fig <- ggplot2::ggplot() +
ggplot2::geom_line(ggplot2::aes(x = r, y = K1), data = kf, linewidth = 2) +
ggplot2::geom_line(ggplot2::aes(x = seq(0, 50, 1), y = pi * seq(0, 50, 1)^2),
linewidth = 2, col = "red", linetype = "dashed"
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.title = ggplot2::element_blank(),
axis.text = ggplot2::element_blank()
)
png(paste0(save_path,"/paper/tnbc_person2_tumor_mononeu_k.png"),
width = 800, height = 800)
print(k_fig)
dev.off()
## ----buildNUllData------------------------------------------------------------
set.seed(123)
dat0 <- simulatePP(
agentVarData =
data.frame(
"outcome" = c(0, 1),
"c1" = c(0, 0),
"c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
"c4" = c(0, 0),
"c5" = c(0, 0), "c6" = c(0, 0),
"c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
"c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
"c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
"c13" = c(0, 0), "c14" = c(0, 0),
"c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
),
agentKappaData = data.frame(
"agent" = paste0("c", 1:16),
"clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
"kappa" = c(
rbinom(1, 100, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 30, 0.5),
rbinom(1, 80, 0.5),
rbinom(1, 350, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 120, 0.5),
rbinom(1, 150, 0.5),
rbinom(2, 250, 0.5),
rbinom(1, 600, 0.5),
rbinom(1, 60, 0.5),
rbinom(2, 140, 0.5),
rbinom(1, 20, 0.5),
rbinom(1, 5, 0.5)
)
),
unitsPerOutcome = 15,
replicatesPerUnit = 1,
silent = FALSE
)
dat1 <- simulatePP(
agentVarData =
data.frame(
"outcome" = c(0, 1),
"c1" = c(0, 0),
"c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
"c4" = c(0, 0),
"c5" = c(0, 0), "c6" = c(0, 0),
"c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
"c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
"c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
"c13" = c(0, 0), "c14" = c(0, 0),
"c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
),
agentKappaData = data.frame(
"agent" = paste0("c", 1:16),
"clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
"kappa" = c(
rbinom(1, 100, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 30, 0.5),
rbinom(1, 80, 0.5),
rbinom(1, 350, 0.5),
rbinom(1, 100, 0.5),
rbinom(1, 120, 0.5),
rbinom(1, 150, 0.5),
rbinom(2, 250, 0.5),
rbinom(1, 600, 0.5),
rbinom(1, 60, 0.5),
rbinom(2, 140, 0.5),
rbinom(1, 20, 0.5),
rbinom(1, 5, 0.5)
)
),
unitsPerOutcome = 2,
replicatesPerUnit = 1,
silent = F
)
dat1$unit <- ifelse(dat1$unit == "u1", "u31",
ifelse(dat1$unit == "u2", "u32",
ifelse(dat1$unit == "u3", "u33",
ifelse(dat1$unit == "u4", "u34", NA)
)
)
)
dat1$replicate <- ifelse(dat1$replicate == "1", "31",
ifelse(dat1$replicate == "2", "32",
ifelse(dat1$replicate == "3", "33",
ifelse(dat1$replicate == "4", "34", NA)
)
)
)
dat <- rbind(dat0, dat1)
## ----tnbc_image, fig.width=5, fig.height=5, fig.cap="TNBC Patient Image"------
plotPP(
data = TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 1, dropAxes = T, colorGuide = "none"
)
## ----sim_image, fig.width=5, fig.height=5, fig.cap="Simulated Patient Image"----
plotPP(dat[dat$replicate == 18, c("x", "y", "type")],
ptSize = 1, dropAxes = T, colorGuide = "none"
)
## ----paper_simtnbc_comparision, echo=FALSE, eval=paperRun---------------------
tmp <- length(table(dat[dat$replicate == 1, c("type")]))
clrs <- scales::hue_pal()(tmp)
tnbc_image <- plotPP(
data = TNBC_pheno[TNBC_pheno$Person == 2, c("cellx", "celly", "Phenotype")],
ptSize = 3, dropAxes = T, colorGuide = "none",
colors = clrs[-length(clrs)]
)
png(paste0(save_path,"/paper/tnbc_image.png"),
width = 800, height = 800)
print(tnbc_image)
dev.off()
sim_image <- plotPP(
data = dat[dat$replicate == 18, c("x", "y", "type")],
ptSize = 3, dropAxes = T, colorGuide = "none",
colors = c(clrs[1:2], clrs[16], clrs[3:7], clrs[15], clrs[8:14])
)
png(paste0(save_path,"/paper/sim_image.png"),
width = 800, height = 800)
print(sim_image)
dev.off()
## ----simulateNoInteraction, eval=full_run-------------------------------------
# cells <- paste0("c", 1:16)
# cells_interactions <- rbind(
# data.frame(t(combn(cells, 2))),
# data.frame("X1" = cells, "X2" = cells)
# )
#
# set.seed(12345)
# pcaData <- getKsPCAData(
# data = dat, replicate = "replicate",
# agents_df = cells_interactions,
# xRange = c(0, 1), yRange = c(0, 1),
# silent = F
# )
# pcaMeta <- simulateMeta(pcaData,
# metaInfo = data.frame(
# "var" = c("age"),
# "rdist" = c("rnorm"),
# "outcome_0" = c("25"),
# "outcome_1" = c("25")
# )
# )
## ----analyzeNoEffect, eval=full_run-------------------------------------------
# set.seed(123)
# rfcv <- funkyModel(
# data = pcaMeta,
# outcome = "outcome",
# unit = "unit",
# metaNames = c("age")
# )
## ----plot_noeffect_full, fig.width=5, fig.height=5, fig.cap="No Effect Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----plot_noeffect_top25, fig.width=5, fig.height=5, fig.cap="No Effect Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_noEffect, echo=FALSE, eval=paperRun--------------------------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/noeffect_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.x = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/noeffect_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----effectSimulationAndModel, eval=full_run----------------------------------
# set.seed(123456)
# dat0 <- simulatePP(
# agentVarData =
# data.frame(
# "outcome" = c(0, 1),
# "c1" = c(0, 0),
# "c2" = c(1 / 25, 1 / 60), "c3" = c(1 / 50, 1 / 10),
# "c4" = c(0, 0),
# "c5" = c(0, 0), "c6" = c(0, 0),
# "c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
# "c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
# "c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
# "c13" = c(0, 0), "c14" = c(0, 0),
# "c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
# ),
# agentKappaData = data.frame(
# "agent" = paste0("c", 1:16),
# "clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
# "kappa" = c(
# rbinom(1, 100, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 30, 0.5),
# rbinom(1, 80, 0.5),
# rbinom(1, 350, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 120, 0.5),
# rbinom(1, 150, 0.5),
# rbinom(2, 250, 0.5),
# rbinom(1, 600, 0.5),
# rbinom(1, 60, 0.5),
# rbinom(2, 140, 0.5),
# rbinom(1, 20, 0.5),
# rbinom(1, 5, 0.5)
# )
# ),
# unitsPerOutcome = 15,
# replicatesPerUnit = 1,
# silent = F
# )
# dat1 <- simulatePP(
# agentVarData =
# data.frame(
# "outcome" = c(0, 1),
# "c1" = c(0, 0),
# "c2" = c(1 / 25, 1 / 25), "c3" = c(1 / 50, 1 / 50),
# "c4" = c(0, 0),
# "c5" = c(0, 0), "c6" = c(0, 0),
# "c7" = c(0, 0), "c8" = c(1 / 100, 1 / 100),
# "c9" = c(1 / 20, 1 / 20), "c10" = c(1 / 250, 1 / 250),
# "c11" = c(1 / 100, 1 / 100), "c12" = c(1 / 80, 1 / 80),
# "c13" = c(0, 0), "c14" = c(0, 0),
# "c15" = c(0, 0), "c16" = c(1 / 10, 1 / 10)
# ),
# agentKappaData = data.frame(
# "agent" = paste0("c", 1:16),
# "clusterAgent" = c(NA, "c1", "c1", rep(NA, 12), "c1"),
# "kappa" = c(
# rbinom(1, 100, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 30, 0.5),
# rbinom(1, 80, 0.5),
# rbinom(1, 350, 0.5),
# rbinom(1, 100, 0.5),
# rbinom(1, 120, 0.5),
# rbinom(1, 150, 0.5),
# rbinom(2, 250, 0.5),
# rbinom(1, 600, 0.5),
# rbinom(1, 60, 0.5),
# rbinom(2, 140, 0.5),
# rbinom(1, 20, 0.5),
# rbinom(1, 5, 0.5)
# )
# ),
# unitsPerOutcome = 2,
# replicatesPerUnit = 1,
# silent = F
# )
# dat1$unit <- ifelse(dat1$unit == "u1", "u31",
# ifelse(dat1$unit == "u2", "u32",
# ifelse(dat1$unit == "u3", "u33",
# ifelse(dat1$unit == "u4", "u34", NA)
# )
# )
# )
# dat1$replicate <- ifelse(dat1$replicate == "1", "31",
# ifelse(dat1$replicate == "2", "32",
# ifelse(dat1$replicate == "3", "33",
# ifelse(dat1$replicate == "4", "34", NA)
# )
# )
# )
# dat <- rbind(dat0, dat1)
#
# cells <- paste0("c", 1:16)
# cells_interactions <- rbind(
# data.frame(t(combn(cells, 2))),
# data.frame("X1" = cells, "X2" = cells)
# )
#
# pcaData <- getKsPCAData(
# data = dat, replicate = "replicate",
# agents_df = cells_interactions,
# xRange = c(0, 1), yRange = c(0, 1),
# silent = F
# )
# pcaMeta <- simulateMeta(pcaData,
# metaInfo = data.frame(
# "var" = c("age"),
# "rdist" = c("rnorm"),
# "outcome_0" = c("25"),
# "outcome_1" = c("27")
# )
# )
#
# rfcv <- funkyModel(
# data = pcaMeta,
# outcome = "outcome",
# unit = "unit",
# metaNames = c("age")
# )
## ----plot_effect_full, fig.width=5, fig.height=5, fig.cap="Effect Simulation Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----plot_effect_top25, fig.width=5, fig.height=5, fig.cap="Effect Simulation Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_effect, echo=FALSE, eval=paperRun----------------------------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/effect_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.x = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/effect_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----tnbc_pheno_model, eval=full_run------------------------------------------
# dataPCA_pheno <- getKsPCAData(
# data = TNBC_pheno, unit = "Person",
# agents_df = pheno_interactions,
# rCheckVals = seq(0, 50, 1)
# )
#
# dataPCAAge_pheno <- merge(dataPCA_pheno, TNBC_meta)
#
# set.seed(123456)
# rfcv <- funkyModel(
# data = dataPCAAge_pheno, K = 10,
# outcome = "Class", unit = "Person",
# metaNames = c("Age"), synthetics = 100,
# alpha = 0.05, silent = FALSE,
# subsetPlotSize = 25
# )
## ----tnbc_full, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Variable Importance (All)", eval=full_run----
# rfcv$viPlot
## ----tnbc_top25, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Variable Importance (Top 25)", eval=full_run----
# rfcv$subset_viPlot
## ----paper_tnbc_variable_importance, echo=FALSE, eval=paperRun----------------
# Get Vars
viData <- rfcv$VariableImportance
accData <- rfcv$AccuracyEstimate
NoiseCutoff <- rfcv$NoiseCutoff
InterpolationCutoff <- rfcv$InterpolationCutoff
subsetPlotSize <- rfcv$AdditionalParams$subsetPlotSize
# Plot Full
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_full <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
# axis.text = ggplot2::element_text(size = 18),
axis.text = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 20)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/tnbc_variableimportance.png"))
print(plot_full)
dev.off()
# Plot Subset
InterpolationCutoff <- InterpolationCutoff[1:subsetPlotSize]
viData <- viData[order(-viData$est), ]
viData <- viData[1:subsetPlotSize, ]
maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
plot_top25 <- ggplot2::ggplot(
data = viData,
mapping = ggplot2::aes(
x = factor(stats::reorder(var, est)),
y = ifelse(est / maxVal > 1, 1,
ifelse(est / maxVal < 0, 0,
est / maxVal
)
),
group = 1
)
) +
ggplot2::geom_errorbar(
ggplot2::aes(
ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
),
color = "black", width = 0.2
) +
ggplot2::geom_point(color = "black", size = 3) +
ggplot2::geom_hline(
ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
color = "red", linetype = "dotted", linewidth = 2
) +
ggplot2::coord_flip(ylim = c(0, 1)) +
ggplot2::xlab(NULL) +
ggplot2::ylim(c(0, 1)) +
ggplot2::ylab(NULL) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(size = 14),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
axis.title = ggplot2::element_text(size = 17)
) +
ggplot2::geom_line(ggplot2::aes(
x = ordered(viData[order(-est), "var"]),
y = InterpolationCutoff / maxVal
), color = "orange", linetype = "dashed", linewidth = 2) +
ggplot2::ylab(paste0(
"OOB (",
specify_decimal(accData$OOB, 2),
"), Guess (",
specify_decimal(accData$guess, 2),
"), Bias (",
specify_decimal(accData$bias, 2), ")"
))
png(paste0(save_path,"/paper/tnbc_variableimportance_top25.png"))
print(plot_top25)
dev.off()
## ----significantTNBCK---------------------------------------------------------
tmp <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 0, -1],
c("Tumor", "Tumor"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp1 <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 1, -1],
c("Tumor", "Tumor"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp_1 <- tidyr::pivot_longer(data = tmp, cols = -r)
tmp1_1 <- tidyr::pivot_longer(data = tmp1, cols = -r)
data_plot <- rbind(
data.frame(
"r" = tmp_1$r,
"K" = tmp_1$value,
"unit" = tmp_1$name,
"outcome" = "0"
),
data.frame(
"r" = tmp1_1$r,
"K" = tmp1_1$value,
"unit" = paste0(tmp1_1$name, "_1"),
"outcome" = "1"
)
)
## ----significantTNBCKPlot, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Significant K Function"----
plot_K_functions(data_plot)
## ----paper_sig_K_functions, echo=FALSE, eval=paperRun-------------------------
data <- data_plot
# Prep Legend
info <- unique(data[, c("unit", "outcome")])
info$Missing <- NA
for (i in 1:nrow(info)) {
info[i, 3] <- nrow(data[data$unit == info$unit[i] &
!stats::complete.cases(data), ]) > 0
}
info_labels <- data.frame(
"outcome" = unique(data$outcome),
"units" = NA,
"Missing" = NA,
"Label" = NA
)
for (i in 1:nrow(info_labels)) {
info_labels[i, 2] <- nrow(info[info$outcome == info_labels[i, 1], ])
info_labels[i, 3] <- nrow(info[info$outcome == info_labels[i, 1] &
info$Missing, ])
# Only label outcomes with numbers if at least one is missing in whole set
if (sum(info$Missing) != 0) {
info_labels[i, 4] <- paste0(
info_labels[i, 1], " (",
info_labels[i, 2] - info_labels[i, 3], "/",
info_labels[i, 2], ")"
)
} else {
info_labels[i, 4] <- info_labels[i, 1]
}
}
# Build Averages
data_wide <- tidyr::pivot_wider(data,
names_from = "unit",
values_from = "K"
)
data_avg <- data.frame("r" = unique(data_wide$r))
for (i in 1:nrow(info_labels)) {
data_avg[[info_labels[i, "outcome"]]] <-
rowMeans(data_wide[data_wide$outcome == info_labels[i, "outcome"], -c(1:2)],
na.rm = TRUE
)
}
data_avg <- tidyr::pivot_longer(data_avg, cols = -r)
colnames(data_avg) <- c("r", "outcome", "Value")
# Plot
return_plot <-
ggplot2::ggplot(
data = stats::na.omit(data),
ggplot2::aes(
x = r, y = K,
group = unit, color = outcome
)
) +
ggplot2::geom_line(alpha = 0.2, linewidth = 1.25) +
ggplot2::geom_line(
ggplot2::aes(
x = r, y = Value,
group = outcome, color = outcome
),
data = data_avg, linewidth = 2
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
# axis.title = ggplot2::element_text(size = 22),
axis.title = ggplot2::element_blank(),
legend.position = "none"
) # +
# ggplot2::geom_line(ggplot2::aes(x = r, y = pi * r^2,
# group = outcome),
# data = data_avg, linewidth = 1.25,
# color = "black", linetype = "dashed"
# )
png(paste0(save_path,"/paper/TNBC_sig_KFunctions.png"))
print(return_plot)
dev.off()
## ----insignificantTNBCK-------------------------------------------------------
tmp <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 0, -1],
c("CD4T", "Endothelial"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp1 <- getKFunction(TNBC_pheno[TNBC_pheno$Class == 1, -1],
c("CD4T", "Endothelial"),
unit = "Person",
rCheckVals = seq(0, 50, 1)
)
tmp_1 <- tidyr::pivot_longer(data = tmp, cols = -r)
tmp1_1 <- tidyr::pivot_longer(data = tmp1, cols = -r)
data_plot <- rbind(
data.frame(
"r" = tmp_1$r,
"K" = tmp_1$value,
"unit" = tmp_1$name,
"outcome" = "0"
),
data.frame(
"r" = tmp1_1$r,
"K" = tmp1_1$value,
"unit" = paste0(tmp1_1$name, "_1"),
"outcome" = "1"
)
)
## ----insignificantTNBCKPlot, fig.width=5, fig.height=5, fig.cap="TNBC Phenotype Insignficant K Function"----
plot_K_functions(data_plot)
## ----paper_insig_K_functions, echo=FALSE, eval=paperRun-----------------------
data <- data_plot
# Prep Legend
info <- unique(data[, c("unit", "outcome")])
info$Missing <- NA
for (i in 1:nrow(info)) {
info[i, 3] <- nrow(data[data$unit == info$unit[i] &
!stats::complete.cases(data), ]) > 0
}
info_labels <- data.frame(
"outcome" = unique(data$outcome),
"units" = NA,
"Missing" = NA,
"Label" = NA
)
for (i in 1:nrow(info_labels)) {
info_labels[i, 2] <- nrow(info[info$outcome == info_labels[i, 1], ])
info_labels[i, 3] <- nrow(info[info$outcome == info_labels[i, 1] &
info$Missing, ])
# Only label outcomes with numbers if at least one is missing in whole set
if (sum(info$Missing) != 0) {
info_labels[i, 4] <- paste0(
info_labels[i, 1], " (",
info_labels[i, 2] - info_labels[i, 3], "/",
info_labels[i, 2], ")"
)
} else {
info_labels[i, 4] <- info_labels[i, 1]
}
}
# Build Averages
data_wide <- tidyr::pivot_wider(data,
names_from = "unit",
values_from = "K"
)
data_avg <- data.frame("r" = unique(data_wide$r))
for (i in 1:nrow(info_labels)) {
data_avg[[info_labels[i, "outcome"]]] <-
rowMeans(data_wide[data_wide$outcome == info_labels[i, "outcome"], -c(1:2)],
na.rm = TRUE
)
}
data_avg <- tidyr::pivot_longer(data_avg, cols = -r)
colnames(data_avg) <- c("r", "outcome", "Value")
# Plot
return_plot <-
ggplot2::ggplot(
data = stats::na.omit(data),
ggplot2::aes(
x = r, y = K,
group = unit, color = outcome
)
) +
ggplot2::geom_line(alpha = 0.2, linewidth = 1.25) +
ggplot2::geom_line(
ggplot2::aes(
x = r, y = Value,
group = outcome, color = outcome
),
data = data_avg, linewidth = 2
) +
ggplot2::theme_bw() +
ggplot2::theme(
axis.text = ggplot2::element_text(size = 18),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.y = ggplot2::element_blank(),
# axis.title = ggplot2::element_text(size = 22),
axis.title = ggplot2::element_blank(),
legend.position = "none"
) # +
# ggplot2::geom_line(ggplot2::aes(x = r, y = pi * r^2,
# group = outcome),
# data = data_avg, linewidth = 1.25,
# color = "black", linetype = "dashed"
# )
png(paste0(save_path,"/paper/TNBC_insig_KFunctions.png"))
print(return_plot)
dev.off()
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