# lungcancer

    library(funkycells)

This vignette gives the code used to analyze the lung cancer data. However, due to data privacy, that data is not yet available in package. This is being investigated and we hope future version of the package include this data.

    dat <- lungcancer

This data is pre-processed and thus we only need to complete a few steps. But to aid in these, we define a few functions.

    getInteractions <- function(cells,
                                dropCells = c(
                                  "Epithelialcells",
                                  "Stromalcells", "Other"
                                )) {
      cells <- cells[!(cells %in% dropCells)]
      return_df <- data.frame("c1" = cells[1], "c2" = cells[1])
      if (length(cells) > 1) {
        return_df <- rbind(
          data.frame(t(combn(cells, 2))),
          data.frame("X1" = cells, "X2" = cells)
        )
      }

      return_df
    }

    clean_figures <- function(model_rf) {
      specify_decimal <- .specify_decimal
      # Get Vars
      viData <- model_rf$VariableImportance
      accData <- model_rf$AccuracyEstimate
      NoiseCutoff <- model_rf$NoiseCutoff
      InterpolationCutoff <- model_rf$InterpolationCutoff
      subsetPlotSize <- model_rf$AdditionalParams$subsetPlotSize

      # Plot Full
      maxVal <- max(InterpolationCutoff, NoiseCutoff, viData$est)
      plot_full <- ggplot2::ggplot(
        data = viData,
        mapping = ggplot2::aes(
          x = factor(stats::reorder(var, est)),
          y = ifelse(est / maxVal > 1, 1,
            ifelse(est / maxVal < 0, 0,
              est / maxVal
            )
          ),
          group = 1
        )
      ) +
        ggplot2::geom_errorbar(
          ggplot2::aes(
            ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
            ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
          ),
          color = "black", width = 0.2
        ) +
        ggplot2::geom_point(color = "black", size = 3) +
        ggplot2::geom_hline(
          ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
          color = "red", linetype = "dotted", linewidth = 2
        ) +
        ggplot2::coord_flip(ylim = c(0, 1)) +
        ggplot2::xlab(NULL) +
        ggplot2::ylim(c(0, 1)) +
        ggplot2::ylab(NULL) +
        ggplot2::theme_bw() +
        ggplot2::theme(
          axis.text = ggplot2::element_text(size = 18),
          axis.text.y = ggplot2::element_blank(),
          axis.ticks.y = ggplot2::element_blank(),
          panel.grid.major.y = ggplot2::element_blank(),
          axis.title = ggplot2::element_text(size = 20)
        ) +
        ggplot2::geom_line(ggplot2::aes(
          x = ordered(viData[order(-est), "var"]),
          y = InterpolationCutoff / maxVal
        ), color = "orange", linetype = "dashed", linewidth = 2) +
        ggplot2::ylab(paste0(
          "OOB (",
          specify_decimal(accData$OOB, 2),
          "), Guess (",
          specify_decimal(accData$guess, 2),
          "), Bias (",
          specify_decimal(accData$bias, 2), ")"
        ))

      # Plot Subset
      InterpolationCutoff1 <- InterpolationCutoff[1:subsetPlotSize]
      viData1 <- viData[order(-viData$est), ]
      viData1 <- viData1[1:subsetPlotSize, ]

      maxVal <- max(InterpolationCutoff1, NoiseCutoff, viData1$est)
      plot_top25 <- ggplot2::ggplot(
        data = viData1,
        mapping = ggplot2::aes(
          x = factor(stats::reorder(var, est)),
          y = ifelse(est / maxVal > 1, 1,
            ifelse(est / maxVal < 0, 0,
              est / maxVal
            )
          ),
          group = 1
        )
      ) +
        ggplot2::geom_errorbar(
          ggplot2::aes(
            ymin = ifelse((est - sd) / maxVal < 0, 0, (est - sd) / maxVal),
            ymax = ifelse((est + sd) / maxVal > 1, 1, (est + sd) / maxVal)
          ),
          color = "black", width = 0.2
        ) +
        ggplot2::geom_point(color = "black", size = 3) +
        ggplot2::geom_hline(
          ggplot2::aes(yintercept = max(0, min(1, NoiseCutoff / maxVal))),
          color = "red", linetype = "dotted", linewidth = 2
        ) +
        ggplot2::coord_flip(ylim = c(0, 1)) +
        ggplot2::xlab(NULL) +
        ggplot2::ylim(c(0, 1)) +
        ggplot2::ylab(NULL) +
        ggplot2::theme_bw() +
        ggplot2::theme(
          axis.text.x = ggplot2::element_text(size = 17),
          axis.text.y = ggplot2::element_text(size = 14),
          axis.ticks.y = ggplot2::element_blank(),
          panel.grid.major.y = ggplot2::element_blank(),
          axis.title = ggplot2::element_text(size = 17)
        ) +
        ggplot2::geom_line(ggplot2::aes(
          x = ordered(viData1[order(-est), "var"]),
          y = InterpolationCutoff1 / maxVal
        ), color = "orange", linetype = "dashed", linewidth = 2) +
        ggplot2::ylab(paste0(
          "OOB (",
          specify_decimal(accData$OOB, 2),
          "), Guess (",
          specify_decimal(accData$guess, 2),
          "), Bias (",
          specify_decimal(accData$bias, 2), ")"
        ))

      list(
        "full" = plot_full,
        "top" = plot_top25
      )
    }

The data must be converted to \(K\) functions, then to in finite dimensions. This data is then combined with the smoking meta-data.

    pcaData <- getKsPCAData(
      data = use_data[!(colnames(use_data) %in% c("SmokingStatusLong"))],
      outcome = "Cancer",
      unit = "Patient",
      replicate = "Image",
      rCheckVals = seq(0, 50, 1),
      nPCs = 3,
      agents_df = ints,
      displayTVE = FALSE
    )

    data_rf <- merge(pcaData,
      unique(use_data[, c("Patient", "SmokingStatusLong")]),
      keep.x = TRUE, by.x = "Patient", by.y = "Patient"
    )

And then we fit the model on the data:

    set.seed(12345)
    model_fm <- funkyModel(
      data = data_rf[!is.na(data_rf[["Cancer"]]), ],
      outcome = "Cancer", unit = "Patient",
      metaNames = "SmokingStatusLong"
    )

The plots can be given as well:

    figs <- clean_figures(model_fm)
    figs$full
    fits$top