Results/Figure5/figure5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Figure5\n",
        "\n",
        "## Figure 5: Lipid concentration across different Lipid Classes\n",
        "\n",
        "## Load External standard calibration file for Calibration"
      ],
      "id": "d2e58d20-10eb-471d-9654-17b893718a9b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "library(dplyr)\n",
        "library(readxl)\n",
        "library(writexl)\n",
        "library(openxlsx)\n",
        "library(tidyverse)\n",
        "library(pheatmap)\n",
        "library(viridis)\n",
        "library(ggplot2)\n",
        "library(scales)\n",
        "\n",
        "# Load the Excel file\n",
        "file_path <- \"ESTD-ALL-POS-NEG-CAL.xlsx\"\n",
        "df_std_cal <- read_excel(file_path, sheet = \"Sheet1\")\n",
        "\n",
        "calculate_calibration_parameters <- function(row) {\n",
        "  concentrations <- c(0)\n",
        "  areas <- c(0)\n",
        "  \n",
        "  initial_concentration <- row[[\"Initial concentration\"]]\n",
        "  injection_columns <- c(names(row)[grep(\"^0_0\", names(row))], \n",
        "                         unlist(lapply(1:7, function(i) paste0(i, \"_\", c(5, 1)))))\n",
        "  \n",
        "  for (col in injection_columns) {\n",
        "    if (grepl(\"^0_0\", col) || !is.na(row[[col]])) {\n",
        "      if (grepl(\"^0_0\", col)) {\n",
        "        effective_concentration <- 0\n",
        "        area <- 0\n",
        "      } else {\n",
        "        volume <- as.numeric(unlist(strsplit(col, \"_\"))[2])\n",
        "        factor <- 1 / (2 ^ (as.numeric(unlist(strsplit(col, \"_\"))[1]) - 1))\n",
        "        concentration <- initial_concentration * factor\n",
        "        effective_concentration <- concentration * volume\n",
        "        area <- row[[col]]\n",
        "      }\n",
        "      \n",
        "      concentrations <- c(concentrations, effective_concentration)\n",
        "      areas <- c(areas, area)\n",
        "    }\n",
        "  }\n",
        "  \n",
        "  data <- data.frame(concentrations = concentrations, areas = areas)\n",
        "  model <- lm(areas ~ concentrations, data = data)\n",
        "  slope <- coef(model)[2]\n",
        "  intercept <- coef(model)[1]\n",
        "  r_squared <- summary(model)$r.squared\n",
        "  non_zero_concentrations <- concentrations[concentrations > 0 & areas > 0]\n",
        "  LOD <- ifelse(length(non_zero_concentrations) > 0, min(non_zero_concentrations), NA)\n",
        "  LOQ <- ifelse(!is.na(LOD), min(concentrations[concentrations >= 3 * LOD & areas > 0]), NA)\n",
        "  if (is.na(LOQ)) {\n",
        "    LOQ <- max(concentrations)\n",
        "  }\n",
        "  \n",
        "  return(data.frame(\n",
        "    Slope = slope,\n",
        "    Intercept = intercept,\n",
        "    `LOD (ng)` = LOD,\n",
        "    `LOQ (ng)` = LOQ,\n",
        "    `R²` = r_squared\n",
        "  ))\n",
        "}\n",
        "\n",
        "calibration_params <- df_std_cal %>% \n",
        "  rowwise() %>% \n",
        "  do(calculate_calibration_parameters(.))\n",
        "df_std_cal <- bind_cols(df_std_cal, calibration_params)\n",
        "\n",
        "output_path <- \"Fig5-ALL-updated.xlsx\"\n",
        "write_xlsx(df_std_cal, output_path)"
      ],
      "id": "088671e8-e132-4f58-a3ea-e9143067f7cc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Calculate the concentration of the sample with peak area"
      ],
      "id": "d619aed3-804e-4de3-8033-cb73fd24855d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "file_path <- \"Fig5-ALL.xlsx\"\n",
        "df_ls_std <- read_excel(file_path, sheet = \"LS-SIRIUS-STD\")\n",
        "\n",
        "file_path <- \"Fig5-ALL-updated.xlsx\"\n",
        "data <- read_excel(file_path, sheet = \"Sheet1\")\n",
        "colnames(data) <- trimws(colnames(data))\n",
        "\n",
        "df_updated_ls_std_cal <- data %>%\n",
        "  group_by(`Molecule List`, `Precursor Adduct`) %>%\n",
        "  filter(R. == max(R.)) %>%\n",
        "  ungroup()\n",
        "\n",
        "merged_df <- df_ls_std %>%\n",
        "  left_join(df_updated_ls_std_cal %>% select(`Molecule List`, `Precursor Adduct`, `Slope`, `Intercept`, `LOD..ng.`, `LOQ..ng.`), \n",
        "            by = c(\"Molecule List\", \"Precursor Adduct\"))\n",
        "\n",
        "merged_df <- merged_df %>%\n",
        "  mutate(across(starts_with('Sum Total Area'), as.numeric),\n",
        "         Slope = as.numeric(Slope),\n",
        "         Intercept = as.numeric(Intercept))\n",
        "\n",
        "sum_total_area_cols <- grep(\"Sum Total Area\", names(merged_df), value = TRUE)\n",
        "\n",
        "merged_df <- merged_df %>%\n",
        "  mutate(across(all_of(sum_total_area_cols), \n",
        "                ~as.numeric(ifelse(.x %in% c(\"#N/A\", \"N/A\", \"\"), NA, .x))))\n",
        "\n",
        "concentration_columns <- sum_total_area_cols\n",
        "\n",
        "for (col in concentration_columns) {\n",
        "  concentration_col <- paste0(col, \" Concentration\")\n",
        "  merged_df[[concentration_col]] <- (merged_df[[col]] - merged_df[[\"Intercept\"]]) / merged_df[[\"Slope\"]]\n",
        "  \n",
        "  above_loq_col <- paste0(concentration_col, \" > LOQ\")\n",
        "  merged_df[[above_loq_col]] <- merged_df[[concentration_col]] > merged_df[[\"LOQ..ng.\"]]\n",
        "}\n",
        "\n",
        "write.xlsx(merged_df, \"Fig5-ALL-concentration-updated.xlsx\", sheetName = \"SIRIUS-STD\", rowNames = FALSE)"
      ],
      "id": "53dddaa8-c841-40a9-9e76-e362fa53258b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load the metadata to calculate Lipid concentration with treatment"
      ],
      "id": "137b991f-1e1c-44b6-b35c-6a1db13fd2cc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"Refined Concentration Columns:\""
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " [1] \"19 Sum Total Area Concentration\" \"22 Sum Total Area Concentration\"\n",
            " [3] \"27 Sum Total Area Concentration\" \"28 Sum Total Area Concentration\"\n",
            " [5] \"35 Sum Total Area Concentration\" \"36 Sum Total Area Concentration\"\n",
            " [7] \"40 Sum Total Area Concentration\" \"43 Sum Total Area Concentration\"\n",
            " [9] \"44 Sum Total Area Concentration\" \"48 Sum Total Area Concentration\"\n",
            "[11] \"49 Sum Total Area Concentration\" \"52 Sum Total Area Concentration\""
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# A tibble: 6 × 13\n",
            "# Rowwise: \n",
            "  `Molecule List` `19 Sum Total Area Concentration` 22 Sum Total Area Concentr…¹\n",
            "  <chr>                                       <dbl>                        <dbl>\n",
            "1 Cer                                       118.                         145.   \n",
            "2 DG                                       2994.                        3319.   \n",
            "3 DGDG                                      465.                         660.   \n",
            "4 DGTS                                        5.48                         2.73 \n",
            "5 HexCer                                     56.8                         66.6  \n",
            "6 LPA                                         0.807                        0.628\n",
            "# ℹ abbreviated name: ¹​`22 Sum Total Area Concentration`\n",
            "# ℹ 10 more variables: `27 Sum Total Area Concentration` <dbl>,\n",
            "#   `28 Sum Total Area Concentration` <dbl>,\n",
            "#   `35 Sum Total Area Concentration` <dbl>,\n",
            "#   `36 Sum Total Area Concentration` <dbl>,\n",
            "#   `40 Sum Total Area Concentration` <dbl>,\n",
            "#   `43 Sum Total Area Concentration` <dbl>, …"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"Processed: 19 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 22 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 27 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 28 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 35 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 36 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 40 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 43 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 44 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 48 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 49 Sum Total Area Concentration\"\n",
            "[1] \"Processed: 52 Sum Total Area Concentration\""
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# A tibble: 6 × 27\n",
            "  `Molecule List` `19 Sum Total Area Concentration` 22 Sum Total Area Concentr…¹\n",
            "  <chr>                                       <dbl>                        <dbl>\n",
            "1 Cer                                       118.                         145.   \n",
            "2 DG                                       2994.                        3319.   \n",
            "3 DGDG                                      465.                         660.   \n",
            "4 DGTS                                        5.48                         2.73 \n",
            "5 HexCer                                     56.8                         66.6  \n",
            "6 LPA                                         0.807                        0.628\n",
            "# ℹ abbreviated name: ¹​`22 Sum Total Area Concentration`\n",
            "# ℹ 24 more variables: `27 Sum Total Area Concentration` <dbl>,\n",
            "#   `28 Sum Total Area Concentration` <dbl>,\n",
            "#   `35 Sum Total Area Concentration` <dbl>,\n",
            "#   `36 Sum Total Area Concentration` <dbl>,\n",
            "#   `40 Sum Total Area Concentration` <dbl>,\n",
            "#   `43 Sum Total Area Concentration` <dbl>, …"
          ]
        }
      ],
      "source": [
        "# Load the weight data\n",
        "df_lipids <- data.frame(\n",
        "  Sample = c(19, 22, 36, 28, 44, 49, 35, 40, 52, 27, 43, 48, 50),\n",
        "  Type1 = c(\"Ambient\", \"Ambient\", \"Ambient\", \"Ambient\", \"Ambient\", \"Ambient\", \"Future\", \"Future\", \"Future\", \"Future\", \"Future\", \"Future\", \"Future\"),\n",
        "  Type2 = c(\"No_drought\", \"No_drought\", \"No_drought\", \"Drought\", \"Drought\", \"Drought\", \"No_drought\", \"No_drought\", \"No_drought\", \"Drought\", \"Drought\", \"Drought\", \"Drought\"),\n",
        "  Weight = c(2.054, 1.965, 2.063, 2.048, 2.063, 2.082, 2.065, 2.032, 2.033, 2.071, 2.028, 2.027, 0.178)\n",
        ")\n",
        "\n",
        "# Define the samples\n",
        "samples <- c(19, 22, 36, 28, 44, 49, 35, 40, 52, 27, 43, 48, 50)\n",
        "\n",
        "# Now use the samples in the regular expression\n",
        "concentration_cols <- grep(paste(samples, collapse = \"|\"), grep('Sum Total Area Concentration', colnames(merged_df), value = TRUE), value = TRUE)\n",
        "\n",
        "\n",
        "# Extract weights for the samples relevant to the concentration columns\n",
        "sample_weights <- setNames(df_lipids$Weight[df_lipids$Sample %in% as.numeric(samples)], df_lipids$Sample[df_lipids$Sample %in% as.numeric(samples)])\n",
        "\n",
        "# Define columns_containing_loq\n",
        "columns_containing_loq <- grep(\"> LOQ\", colnames(merged_df), value = TRUE)\n",
        "\n",
        "# Now use the defined columns in the filter function\n",
        "df <- merged_df %>%\n",
        "  filter(!rowSums(is.na(select(., all_of(columns_containing_loq)))) & # Filter out rows with any NA values in LOQ columns\n",
        "           !rowSums(select(., all_of(columns_containing_loq)) == 0)) %>%  # Filter out rows where LOQ columns are zero\n",
        "  select(-all_of(columns_containing_loq)) %>%\n",
        "  select(`Molecule List`, LipidMolecule, any_of(concentration_cols))\n",
        "\n",
        "df <- df %>%\n",
        "  group_by(`Molecule List`) %>%\n",
        "  summarize(across(any_of(concentration_cols), sum, na.rm = TRUE))\n",
        "\n",
        "# Exclude columns with \"> LOQ\" for normalization calculation\n",
        "concentration_cols <- concentration_cols[!grepl(\"> LOQ\", concentration_cols)]\n",
        "print(\"Refined Concentration Columns:\")"
      ],
      "id": "253149a8-364c-40ee-bf78-9b68e11fc5eb"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Process Class list for plotting the Lipids across treatment"
      ],
      "id": "ee9e62af-9dac-4e6d-a602-f8be392324cb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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Jitc7RsWmjQo2jLxJ1NoIgiWbtOJQnDYdJ55C+IlbN1Fpx20z9E/6Nn0ialMD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3qBFI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\nBbeP8L2HkXO+V1cM7+F7Hid8ccmzqmHVvXzPrfXI9kmU34nZ0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6EJRRKMmWUh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          }
        }
      ],
      "source": [
        "process_molecule_list <- function(molecule_lists) {\n",
        "  # Function to create a summarized dataframe for a specific treatment\n",
        "  create_summary_df <- function(samples, treatment_name) {\n",
        "    concentration_cols <- grep(paste(samples, collapse = \"|\"), grep('Sum Total Area Concentration', colnames(merged_df), value = TRUE), value = TRUE)\n",
        "    \n",
        "    df <- merged_df %>% \n",
        "      select(`Molecule List`, LipidMolecule, all_of(concentration_cols)) %>%\n",
        "      group_by(`Molecule List`) %>%\n",
        "      summarize(across(all_of(concentration_cols), sum, na.rm = TRUE)) %>%\n",
        "      rowwise() %>%\n",
        "      mutate(mean_concentration = mean(c_across(all_of(concentration_cols)), na.rm = TRUE),\n",
        "             std_concentration = sd(c_across(all_of(concentration_cols)), na.rm = TRUE)) %>%\n",
        "      ungroup()\n",
        "    \n",
        "    df$TreatmentType <- treatment_name\n",
        "    return(df)\n",
        "  }\n",
        "  \n",
        "  # Create summarized dataframes for each treatment\n",
        "  ambient_no_drought_df <- create_summary_df(c('19', '22', '36'), 'Ambient_No_drought')\n",
        "  future_no_drought_df <- create_summary_df(c('35', '40', '52'), 'Future_No_drought')\n",
        "  ambient_drought_df <- create_summary_df(c('28', '44', '49'), 'Ambient_Drought')\n",
        "  future_drought_df <- create_summary_df(c('27', '43', '48'), 'Future_Drought')\n",
        "  \n",
        "  # Combine all the summarized dataframes\n",
        "  combined_df <- bind_rows(ambient_no_drought_df, future_no_drought_df, ambient_drought_df, future_drought_df)\n",
        "  \n",
        "  # Remove rows with NA mean_concentration\n",
        "  combined_df <- combined_df %>% filter(!is.na(mean_concentration))\n",
        "  \n",
        "  # Check if there's any data left to plot\n",
        "  if(nrow(combined_df) == 0) {\n",
        "    warning(\"No valid data for any molecule list\")\n",
        "    return(NULL)\n",
        "  }\n",
        "  \n",
        "  # Calculate total concentration for each Molecule List\n",
        "  total_conc <- combined_df %>%\n",
        "    group_by(`Molecule List`) %>%\n",
        "    summarize(total_conc = sum(mean_concentration, na.rm = TRUE)) %>%\n",
        "    arrange(desc(total_conc))\n",
        "  \n",
        "  # Order the Molecule List factor levels based on total concentration\n",
        "  combined_df$`Molecule List` <- factor(combined_df$`Molecule List`, \n",
        "                                        levels = total_conc$`Molecule List`)\n",
        "  \n",
        "  # Create the plot\n",
        "  p <- ggplot(combined_df, aes(x = TreatmentType, y = mean_concentration, fill = TreatmentType)) +\n",
        "    geom_bar(stat = 'identity', position = position_dodge(), width = 0.7) +\n",
        "    geom_errorbar(aes(ymin = pmax(mean_concentration - std_concentration, 0), \n",
        "                      ymax = mean_concentration + std_concentration),\n",
        "                  position = position_dodge(0.7), width = 0.25) +\n",
        "    facet_wrap(~ `Molecule List`, scales = \"free_y\", ncol = 3) +\n",
        "    scale_fill_brewer(palette = \"Set2\") +\n",
        "    labs(title = 'Total Lipid Concentration Across Treatments for Each Molecule List',\n",
        "         x = 'Treatment Type', \n",
        "         y = 'Total Concentration') +\n",
        "    theme_minimal() +\n",
        "    theme(\n",
        "      axis.text.x = element_text(angle = 45, hjust = 1),\n",
        "      legend.position = \"top\",\n",
        "      strip.background = element_rect(fill = \"lightgrey\"),\n",
        "      strip.text = element_text(face = \"bold\"),\n",
        "      plot.title = element_text(hjust = 0.5, face = \"bold\")\n",
        "    )\n",
        "  \n",
        "  print(p)\n",
        "  \n",
        "  # Save the plot\n",
        "  ggsave(\"all_molecule_lists_comparison_ordered.pdf\", plot = p, width = 15, height = 12)\n",
        "  \n",
        "  # Return the combined dataframe\n",
        "  return(combined_df)\n",
        "}\n",
        "\n",
        "# Get all unique Molecule List values\n",
        "molecule_lists <- unique(merged_df$`Molecule List`)\n",
        "\n",
        "# Process all Molecule Lists\n",
        "results <- process_molecule_list(molecule_lists)"
      ],
      "id": "6c0a17ab-b1c1-4fe4-b5d5-35e5ad371dc3"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
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      "display_name": "Python 3",
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