diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0d05526..1e40de4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,14 +4,14 @@ repos: # Keep pre-commit itself up to date - repo: https://gitlab.com/vojko.pribudic.foss/pre-commit-update - rev: v0.6.1 + rev: v0.7.0 hooks: - id: pre-commit-update args: ["--verbose"] # Ruff for linting Python files - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: v0.11.5 + rev: v0.11.8 hooks: - id: ruff args: ["--fix"] diff --git a/notebooks/exploratory-analysis/pairwise-analysis/dmso-consensus-profiles.ipynb b/notebooks/exploratory-analysis/pairwise-analysis/dmso-consensus-profiles.ipynb new file mode 100644 index 0000000..230de77 --- /dev/null +++ b/notebooks/exploratory-analysis/pairwise-analysis/dmso-consensus-profiles.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7a503159", + "metadata": {}, + "source": [ + "# Generating DMSO consensus profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5eddb05c", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import pathlib\n", + "import pandas as pd\n", + "from pycytominer import consensus\n", + "\n", + "sys.path.append(\"../../../\")\n", + "from utils import data_utils " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75422c40", + "metadata": {}, + "outputs": [], + "source": [ + "# setting profile path\n", + "concat_profile_path = pathlib.Path(\"../UMAP-aggregated-fs-profiles/results/concat_data/batch_1_concat_agg_fs.csv\").resolve(strict=True)\n", + "\n", + "# setting output path\n", + "# output_path = pathlib.Path(\"results/\").resolve(strict=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "efa524fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (32, 485)\n" + ] + }, + { + "data": { + "text/html": [ + "
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This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n", + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/pycytominer/aggregate.py:111: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n", + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/pycytominer/aggregate.py:111: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n" + ] + } + ], + "source": [ + "consensus_df = consensus(profiles = dmso_agg_df,\n", + " replicate_columns=[\"Metadata_plate_barcode\", \"Metadata_plate_name\", \"Metadata_treatment\"],\n", + " operation=\"median\",\n", + " features=dmso_agg_feats,\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cfret-map", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/dmso-consensus-profiles.py b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/dmso-consensus-profiles.py new file mode 100644 index 0000000..a04dc20 --- /dev/null +++ b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/dmso-consensus-profiles.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python + +# # Generating DMSO consensus profiles + +# In[1]: + + +import pathlib +import sys + +import pandas as pd +from pycytominer import consensus + +sys.path.append("../../../") +from utils import data_utils + +# In[2]: + + +# setting profile path +concat_profile_path = pathlib.Path("../UMAP-aggregated-fs-profiles/results/concat_data/batch_1_concat_agg_fs.csv").resolve(strict=True) + +# setting output path +# output_path = pathlib.Path("results/").resolve(strict=True) + + +# In[3]: + + +# load in aggregate profiles +agg_df = pd.read_csv(concat_profile_path) + +# update aggregate profiles to only DMSO treated wells +dmso_agg_df = agg_df.loc[ + (agg_df["Metadata_control_type"] == "positive") +| (agg_df["Metadata_control_type"] == "negative")] + +# split the metadata and morphology features +dmso_agg_meta, dmso_agg_feats = data_utils.split_meta_and_features(dmso_agg_df) + +# display +print("Shape: ", dmso_agg_df.shape) +dmso_agg_df.head() + + +# In[4]: + + +consensus_df = consensus(profiles = dmso_agg_df, + replicate_columns=["Metadata_plate_barcode", "Metadata_plate_name", "Metadata_treatment"], + operation="median", + features=dmso_agg_feats, +) diff --git a/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/pairwise-compare.py b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/pairwise-compare.py index 720dd3c..5942603 100644 --- a/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/pairwise-compare.py +++ b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/pairwise-compare.py @@ -1,5 +1,9 @@ #!/usr/bin/env python +# # Comparing controls and treatments using pairwise compare +# +# This notebook employs pairwise comparison to quantify the similarity between cellular profiles. In this section, we assess the consistency of experimental replicates and evaluate the similarity between treated wells (containing failing cardiac fibroblast cells) and control wells. + # In[1]: @@ -9,6 +13,7 @@ import pandas as pd from comparators.PearsonsCorrelation import PearsonsCorrelation from comparison_tools.PairwiseCompareManager import PairwiseCompareManager +from pycytominer import consensus from pycytominer.cyto_utils import load_profiles # loading project utils @@ -37,7 +42,7 @@ # split the features: metadata, features = split_meta_and_features(agg_profile) -# now only select DMSO profiles that are DMSO-positive and DMSO-negative +# now only select DMSO profiles that are DMSO_positive and DMSO-negative dmso_profiles = agg_profile.loc[ (agg_profile["Metadata_treatment"] == "DMSO-positive") | (agg_profile["Metadata_treatment"] == "DMSO-negative") @@ -125,6 +130,136 @@ ) +# ## Calculate pair wise across DMSO consensus profiles + +# In[7]: + + +consensus_dmso_df = consensus( + profiles=dmso_profiles, + replicate_columns=[ + "Metadata_plate_barcode", + "Metadata_plate_name", + "Metadata_treatment", + ], + operation="median", + features=features, +) + +# split to positive and negative controls +consensus_dmso_pos_df = consensus_dmso_df.loc[ + consensus_dmso_df["Metadata_treatment"] == "DMSO-positive" +] +consensus_dmso_neg_df = consensus_dmso_df.loc[ + consensus_dmso_df["Metadata_treatment"] == "DMSO-negative" +] + + +# In[8]: + + +# comparing the consensus profiles of the positive controls +consensus_dmso_pos_cntrl_comparer = PairwiseCompareManager( + _df=consensus_dmso_pos_df, + _feat_cols=features, + _different_columns=["Metadata_plate_name"], + _same_columns=["Metadata_treatment"], + _comparator=PearsonsCorrelation(), +) + +# comparing the consensus profiles of the negative controls +consensus_dmso_neg_cntrl_comparer = PairwiseCompareManager( + _df=consensus_dmso_neg_df, + _feat_cols=features, + _different_columns=["Metadata_plate_name"], + _same_columns=["Metadata_treatment"], + _comparator=PearsonsCorrelation(), +) + +# collecting all pairwise scores +consensus_pos_cntrl_pairwise_scores = consensus_dmso_pos_cntrl_comparer() +consensus_neg_cntrl_pairwise_scores = consensus_dmso_neg_cntrl_comparer() + + +# In[9]: + + +# selecting only relevant columns +consensus_pos_cntrl_scores = consensus_pos_cntrl_pairwise_scores[ + [ + "pearsons_correlation", + "Metadata_treatment__antehoc_group0", + "Metadata_plate_name__posthoc_group0", + "Metadata_plate_name__posthoc_group1", + ] +] +consensus_neg_cntrl_scores = consensus_neg_cntrl_pairwise_scores[ + [ + "pearsons_correlation", + "Metadata_treatment__antehoc_group0", + "Metadata_plate_name__posthoc_group0", + "Metadata_plate_name__posthoc_group1", + ] +] + +# generated plate well names +final_consensus_pairwise_scores = pd.concat( + [ + consensus_pos_cntrl_scores, + consensus_neg_cntrl_scores, + ] +).rename(columns={"Metadata_treatment__antehoc_group0": "Metadata_treatment"}).reset_index(drop=True) + +# saving the final consensus pairwise scores +final_consensus_pairwise_scores.to_csv(output_path / "final_dmso_consensus_pairwise_scores.csv", index=False) + + +# ## Calculating pairwise compare within replicates +# +# In this section, we compute pairwise Pearson correlations between replicates of the same treatment. This helps identify poorly performing technical replicates—those with low correlation values—while high correlations indicate consistent and reliable measurements across replicates. + +# In[10]: + + +# selecting only the treated wells without the DMSO profiles +treated_wells_only_df = agg_profile.loc[ + (agg_profile["Metadata_treatment"] != "DMSO-positive") & (agg_profile["Metadata_treatment"] != "DMSO-negative") +].copy() + +# reducing the metadata to only the relevant ones +treated_wells_only_df = treated_wells_only_df[["Metadata_plate_name", "Metadata_treatment"] + features] + + +# In[ ]: + + +# calculating the pairwise scores between replicates +replicate_pairwise_comparer = PairwiseCompareManager( + _df=treated_wells_only_df, + _feat_cols=features, + _different_columns=["Metadata_plate_name"], + _same_columns=["Metadata_treatment"], + _comparator=PearsonsCorrelation(), +) + +# collecting all pairwise scores +replicate_pairwise_scores = replicate_pairwise_comparer() + + +# In[12]: + + +# selecting only relevant columns +replicate_pairwise_scores = replicate_pairwise_scores[["pearsons_correlation", "Metadata_treatment__antehoc_group0", "Metadata_plate_name__posthoc_group0", "Metadata_plate_name__posthoc_group1"]] + +# renaming the columns +replicate_pairwise_scores.columns = ["pearsons_correlation", "Metadata_treatment", "plate_name_0", "plate_name_1"] + +# saving the final pairwise scores +replicate_pairwise_scores.to_csv(output_path / "final_replicate_pairwise_scores.csv", index=False) + + + # ## Calculating pair wise across treatments # # In this section of the notebook, we conduct pairwise comparisons across all treatments and specific controls. Two data frames are created: @@ -132,7 +267,7 @@ # - **healthy_ref**: This dataset contains pairwise calculations comparing all treated failing cells to the healthy reference. # - **failing_ref**: This dataset contains pairwise calculations comparing all treated failing cells to the failing reference. -# In[7]: +# In[13]: # calculating pairwise correlation between healthy control and treated failing wells @@ -159,25 +294,25 @@ failing_ref_trt_pairwise_scores = failing_ref_trt_pairwise_comparer() -# In[8]: +# In[14]: # Select only the relevant columns and add a reference column for healthy controls health_ref_pairwise_scores = healthy_ref_trt_pairwise_scores[ - ["pearsons_correlation", "Metadata_treatment__antehoc_group1"] + ["pearsons_correlation", "Metadata_treatment__posthoc_group1"] ].copy() health_ref_pairwise_scores["reference"] = "Healthy" # Select only the relevant columns and add a reference column for failing controls failing_ref_pairwise_scores = failing_ref_trt_pairwise_scores[ - ["pearsons_correlation", "Metadata_treatment__antehoc_group1"] + ["pearsons_correlation", "Metadata_treatment__posthoc_group1"] ].copy() failing_ref_pairwise_scores["reference"] = "Failing" # Combine the healthy and failing control dataframes into a single dataframe final_trt_pairwise_scores = ( pd.concat([health_ref_pairwise_scores, failing_ref_pairwise_scores]) - .rename(columns={"Metadata_treatment__antehoc_group1": "Metadata_treatment"}) + .rename(columns={"Metadata_treatment__posthoc_group1": "Metadata_treatment"}) .reset_index(drop=True) ) diff --git a/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/r_plot-pairwise-scores.r b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/r_plot-pairwise-scores.r index 4d984b4..3c27a30 100644 --- a/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/r_plot-pairwise-scores.r +++ b/notebooks/exploratory-analysis/pairwise-analysis/nbconverted/r_plot-pairwise-scores.r @@ -6,17 +6,21 @@ suppressMessages(library(ggplot2)) # plotting suppressMessages(library(data.table)) # loading csv files suppressMessages(library(RColorBrewer)) # for ColorBrewer palettes -# setting dmso pairwise scores data path -dmso_pairwise_scores <- file.path("./results/final_pairwise_scores.csv") -if (!file.exists(dmso_pairwise_scores)) { - stop("Please run ./pairwise-compair.ipynb first to generate the pairwise scores.") +# Function to set file paths and check their existence +set_file_path <- function(file_name) { + file_path <- file.path("./results", file_name) + if (!file.exists(file_path)) { + stop(paste("The file", file_name, + "does not exist. Please run the notebook to generate the required output files.")) + } + return(file_path) } -# setting trt pairwise scores data path -trt_pairwise_scores <- file.path("./results/final_trt_pairwise_scores.csv") -if (!file.exists(trt_pairwise_scores)) { - stop("Please run ./pairwise-compair.ipynb first to generate the pairwise scores.") -} +# Setting file paths using the function +dmso_pairwise_scores <- set_file_path("final_pairwise_scores.csv") +trt_pairwise_scores <- set_file_path("final_trt_pairwise_scores.csv") +replicate_pairwise_scores <- set_file_path("final_replicate_pairwise_scores.csv") +consensus_scores <- set_file_path("final_dmso_consensus_pairwise_scores.csv") # creating a figures directory in the results folder plot_output_dir <- "./results/figures" @@ -25,9 +29,10 @@ if (!dir.exists(plot_output_dir)) { } -# loading csv files -dmso_pairwise_df <- fread(dmso_pairwise_scores) -trt_pairwise_df <- fread(trt_pairwise_scores) +dmso_pairwise_df <- fread(dmso_pairwise_scores) # loads the dmso pairwise scores +consensus_dmso_df <- fread(consensus_scores) # loads the dmso consensus scores +trt_pairwise_df <- fread(trt_pairwise_scores) # loads the trt pairwise scores +replicate_pairwise_df <- fread(replicate_pairwise_scores) # loads the replicate pairwise scores # setting the figure size for the notebook width <- 10 @@ -166,6 +171,92 @@ ggsave( heatmap_plot +# updating column names for the consensus scores +consensus_dmso_df <- consensus_dmso_df %>% + rename( + metadata_plate_0 = Metadata_plate_name__posthoc_group0, + metadata_plate_1 = Metadata_plate_name__posthoc_group1 + ) + +# separating based on metadata_treatment (DMSO-positive or DMSO-negative) +healthy_dmso_consensus_df <- consensus_dmso_df %>% + filter(Metadata_treatment == "DMSO-positive") %>% + select(pearsons_correlation, Metadata_treatment, metadata_plate_0, metadata_plate_1) %>% + arrange(desc(pearsons_correlation)) +failing_dmso_consensus_df <- consensus_dmso_df %>% + filter(Metadata_treatment == "DMSO-negative") %>% + select(pearsons_correlation, Metadata_treatment, metadata_plate_0, metadata_plate_1) %>% + arrange(desc(pearsons_correlation)) + +# Make plots show up nice in the notebook +width <- 8 +height <- 12 +options(repr.plot.width = width, repr.plot.height = height) + +# Plot1: Create the consensus health controls heatmap +health_control_consensus_heatmap <- ggplot(healthy_dmso_consensus_df, aes(x = metadata_plate_0, y = metadata_plate_1)) + + geom_tile(aes(fill = pearsons_correlation), color = "white") + + geom_text(aes(label = round(pearsons_correlation, 2)), size = 7) + + scale_fill_gradient2( + low = "blue", mid = "white", high = "red", midpoint = 0, limits = c(-1, 1), name = "Correlation" + ) + + labs( + title = "Pairwise score heatmap consensus healthy controls", + x = "", + y = "" + ) + + theme_bw() + + theme( + plot.title = element_text(hjust = 0.5, size = 18), + axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 16), + axis.text.y = element_text(size = 16), + legend.text = element_text(size = 10), + legend.title = element_text(size = 10, margin = margin(b = 10)) + ) + +# Plot2: Create the consensus failing controls heatmap +failing_dmso_consensus_heatmap <- ggplot(failing_dmso_consensus_df, aes(x = metadata_plate_0, y = metadata_plate_1)) + + geom_tile(aes(fill = pearsons_correlation), color = "white") + + geom_text(aes(label = round(pearsons_correlation, 2)), size = 7) + + scale_fill_gradient2( + low = "blue", mid = "white", high = "red", midpoint = 0, limits = c(-1, 1), name = "Correlation" + ) + + labs( + title = "Pairwise score heatmap consensus failing controls", + x = "", + y = "" + ) + + theme_bw() + + theme( + plot.title = element_text(hjust = 0.5, size = 18), + axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 16), + axis.text.y = element_text(size = 16), + legend.text = element_text(size = 10), + legend.title = element_text(size = 10, margin = margin(b = 10)) + ) + +# Wrap both plots in a grid +consensus_dmso_pairwise_heatmap <- grid.arrange( + health_control_consensus_heatmap, + failing_dmso_consensus_heatmap, + ncol = 1, + top = textGrob("Pairwise consensus DMSO correlation scores", gp = gpar(fontsize = 20, fontface = "bold")) +) + +# save the heatmap plot into the figures directory +ggsave( + filename = file.path(plot_output_dir, "consensus_dmso_pairwise_heatmap.png"), + plot = consensus_dmso_pairwise_heatmap, + width = width, + height = height, + dpi = 400, + bg = "white" +) + +# Display the heatmap +consensus_dmso_pairwise_heatmap + + # Reordering the reference factor to place "Healthy" on top trt_pairwise_df$reference <- factor(trt_pairwise_df$reference, levels = c("Healthy", "Failing")) @@ -232,3 +323,62 @@ ggsave( ) pairwise_score_boxplot + +# data manipulation to create a new column for the compared combinations +# updating the plate names to only have the numeric value +# creating a new column for the compared combinations +replicate_pairwise_df <- replicate_pairwise_df %>% + mutate( + plate_name_0 = gsub("Plate_", "", plate_name_0), + plate_name_1 = gsub("Plate_", "", plate_name_1), + compared_combinations = paste0( + Metadata_treatment, "_Plates_", plate_name_0, "_", plate_name_1 + ) + ) +head(replicate_pairwise_df) + +# Set figure size in notebook +width <- 20 +height <- 12 +options(repr.plot.width = width, repr.plot.height = height) + +# Reorder treatments (optional: based on some logic) +replicate_pairwise_df$Metadata_treatment <- factor(replicate_pairwise_df$Metadata_treatment, + levels = unique(replicate_pairwise_df$Metadata_treatment)) + +# Create styled boxplot +replicate_pairwise_plot <- ggplot(replicate_pairwise_df, aes(x = Metadata_treatment, y = pearsons_correlation)) + + geom_boxplot(outlier.size = 0.5, linewidth = 0.8, fill = "#73add1") + + + geom_hline(yintercept = 0, color = "lightcoral", linetype = "dashed", linewidth = 1.2) + + + # Adjust axis and labels + labs( + title = "Pairwise correlation scores across technical replicates", + x = "Treatment", + y = "Pearson's correlation" + ) + + + scale_y_continuous(breaks = seq(-1, 1, by = 0.2)) + + + # Match styling + theme_bw() + + theme( + legend.position = "none", + axis.text.x = element_text(size = 14, angle = 90, vjust = 0.5), + axis.text.y = element_text(size = 14), + axis.title.x = element_text(size = 17, face = "bold", vjust = -1.0), + axis.title.y = element_text(size = 17, face = "bold"), + plot.title = element_text(hjust = 0.5, size = 25, face = "bold") + ) + +# Save to figures directory if needed +ggsave( + filename = file.path(plot_output_dir, "replicate_pairwise_boxplot.png"), + plot = replicate_pairwise_plot, + width = width, + height = height, + dpi = 300 +) + +replicate_pairwise_plot diff --git a/notebooks/exploratory-analysis/pairwise-analysis/pairwise-compare.ipynb b/notebooks/exploratory-analysis/pairwise-analysis/pairwise-compare.ipynb index d0460e0..5261e98 100644 --- a/notebooks/exploratory-analysis/pairwise-analysis/pairwise-compare.ipynb +++ b/notebooks/exploratory-analysis/pairwise-analysis/pairwise-compare.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "cac2465f", + "metadata": {}, + "source": [ + "# Comparing controls and treatments using pairwise compare \n", + "\n", + "This notebook employs pairwise comparison to quantify the similarity between cellular profiles. In this section, we assess the consistency of experimental replicates and evaluate the similarity between treated wells (containing failing cardiac fibroblast cells) and control wells." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -10,6 +20,7 @@ "import sys\n", "import pathlib\n", "import pandas as pd\n", + "from pycytominer import consensus\n", "from pycytominer.cyto_utils import load_profiles\n", "from comparison_tools.PairwiseCompareManager import PairwiseCompareManager\n", "from comparators.PearsonsCorrelation import PearsonsCorrelation\n", @@ -46,9 +57,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_285482/2571744443.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "/tmp/ipykernel_1730669/2571744443.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " dmso_profiles[\"Metadata_plate_well\"] = dmso_profiles[[\"Metadata_plate_name\", \"Metadata_Well\"]].apply(lambda row: f\"{row[0]}_{row[1]}\", axis=1)\n", - "/tmp/ipykernel_285482/2571744443.py:12: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "/tmp/ipykernel_1730669/2571744443.py:12: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " dmso_profiles[\"Metadata_plate_well\"] = dmso_profiles[[\"Metadata_plate_name\", \"Metadata_Well\"]].apply(lambda row: f\"{row[0]}_{row[1]}\", axis=1)\n" ] } @@ -128,16 +139,7 @@ "execution_count": 5, "id": "8fc73504", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/comparison_tools/PairwiseCompare.py:80: UserWarning: DataFrame contains NaNs\n", - " warnings.warn(\"DataFrame contains NaNs\")\n" - ] - } - ], + "outputs": [], "source": [ "# Comparing all negative controls (unhealthy cells) cross all plates to see if they are similar\n", "dmso_neg_cntrl_comparer = PairwiseCompareManager(\n", @@ -190,6 +192,194 @@ ")" ] }, + { + "cell_type": "markdown", + "id": "bbbd7fc5", + "metadata": {}, + "source": [ + "## Calculate pair wise across DMSO consensus profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4315ac67", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/pycytominer/aggregate.py:111: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n", + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/pycytominer/aggregate.py:111: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n", + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/pycytominer/aggregate.py:111: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " population_df = population_df.median().reset_index()\n" + ] + } + ], + "source": [ + "consensus_dmso_df = consensus(\n", + " profiles=dmso_profiles,\n", + " replicate_columns=[\n", + " \"Metadata_plate_barcode\",\n", + " \"Metadata_plate_name\",\n", + " \"Metadata_treatment\",\n", + " ],\n", + " operation=\"median\",\n", + " features=features,\n", + ")\n", + "\n", + "# split to positive and negative controls\n", + "consensus_dmso_pos_df = consensus_dmso_df.loc[\n", + " consensus_dmso_df[\"Metadata_treatment\"] == \"DMSO-positive\"\n", + "]\n", + "consensus_dmso_neg_df = consensus_dmso_df.loc[\n", + " consensus_dmso_df[\"Metadata_treatment\"] == \"DMSO-negative\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "79aa4262", + "metadata": {}, + "outputs": [], + "source": [ + "# comparing the consensus profiles of the positive controls\n", + "consensus_dmso_pos_cntrl_comparer = PairwiseCompareManager(\n", + " _df=consensus_dmso_pos_df,\n", + " _feat_cols=features,\n", + " _different_columns=[\"Metadata_plate_name\"],\n", + " _same_columns=[\"Metadata_treatment\"],\n", + " _comparator=PearsonsCorrelation(),\n", + ")\n", + "\n", + "# comparing the consensus profiles of the negative controls\n", + "consensus_dmso_neg_cntrl_comparer = PairwiseCompareManager(\n", + " _df=consensus_dmso_neg_df,\n", + " _feat_cols=features,\n", + " _different_columns=[\"Metadata_plate_name\"],\n", + " _same_columns=[\"Metadata_treatment\"],\n", + " _comparator=PearsonsCorrelation(),\n", + ")\n", + "\n", + "# collecting all pairwise scores\n", + "consensus_pos_cntrl_pairwise_scores = consensus_dmso_pos_cntrl_comparer()\n", + "consensus_neg_cntrl_pairwise_scores = consensus_dmso_neg_cntrl_comparer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "65998c2d", + "metadata": {}, + "outputs": [], + "source": [ + "# selecting only relevant columns\n", + "consensus_pos_cntrl_scores = consensus_pos_cntrl_pairwise_scores[\n", + " [\n", + " \"pearsons_correlation\",\n", + " \"Metadata_treatment__antehoc_group0\",\n", + " \"Metadata_plate_name__posthoc_group0\",\n", + " \"Metadata_plate_name__posthoc_group1\",\n", + " ]\n", + "]\n", + "consensus_neg_cntrl_scores = consensus_neg_cntrl_pairwise_scores[\n", + " [\n", + " \"pearsons_correlation\",\n", + " \"Metadata_treatment__antehoc_group0\",\n", + " \"Metadata_plate_name__posthoc_group0\",\n", + " \"Metadata_plate_name__posthoc_group1\",\n", + " ]\n", + "] \n", + "\n", + "# generated plate well names \n", + "final_consensus_pairwise_scores = pd.concat(\n", + " [\n", + " consensus_pos_cntrl_scores,\n", + " consensus_neg_cntrl_scores,\n", + " ]\n", + ").rename(columns={\"Metadata_treatment__antehoc_group0\": \"Metadata_treatment\"}).reset_index(drop=True)\n", + "\n", + "# saving the final consensus pairwise scores\n", + "final_consensus_pairwise_scores.to_csv(output_path / \"final_dmso_consensus_pairwise_scores.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "f7a3fe00", + "metadata": {}, + "source": [ + "## Calculating pairwise compare within replicates\n", + "\n", + "In this section, we compute pairwise Pearson correlations between replicates of the same treatment. This helps identify poorly performing technical replicates—those with low correlation values—while high correlations indicate consistent and reliable measurements across replicates." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c9753a5b", + "metadata": {}, + "outputs": [], + "source": [ + "# selecting only the treated wells without the DMSO profiles\n", + "treated_wells_only_df = agg_profile.loc[\n", + " (agg_profile[\"Metadata_treatment\"] != \"DMSO-positive\") & (agg_profile[\"Metadata_treatment\"] != \"DMSO-negative\")\n", + "].copy()\n", + "\n", + "# reducing the metadata to only the relevant ones\n", + "treated_wells_only_df = treated_wells_only_df[[\"Metadata_plate_name\", \"Metadata_treatment\"] + features]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed1eb04c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/comparison_tools/PairwiseCompare.py:112: UserWarning: Intra Comparisons were empty\n", + " warnings.warn(f\"{_comparison_type_name} were empty\", UserWarning)\n" + ] + } + ], + "source": [ + "# calculating the pairwise scores between replicates\n", + "replicate_pairwise_comparer = PairwiseCompareManager(\n", + " _df=treated_wells_only_df,\n", + " _feat_cols=features,\n", + " _different_columns=[\"Metadata_plate_name\"],\n", + " _same_columns=[\"Metadata_treatment\"],\n", + " _comparator=PearsonsCorrelation(),\n", + ")\n", + "\n", + "# collecting all pairwise scores\n", + "replicate_pairwise_scores = replicate_pairwise_comparer()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3130c1f7", + "metadata": {}, + "outputs": [], + "source": [ + "# selecting only relevant columns\n", + "replicate_pairwise_scores = replicate_pairwise_scores[[\"pearsons_correlation\", \"Metadata_treatment__antehoc_group0\", \"Metadata_plate_name__posthoc_group0\", \"Metadata_plate_name__posthoc_group1\"]]\n", + "\n", + "# renaming the columns\n", + "replicate_pairwise_scores.columns = [\"pearsons_correlation\", \"Metadata_treatment\", \"plate_name_0\", \"plate_name_1\"]\n", + "\n", + "# saving the final pairwise scores\n", + "replicate_pairwise_scores.to_csv(output_path / \"final_replicate_pairwise_scores.csv\", index=False)\n", + "\n" + ] + }, { "cell_type": "markdown", "id": "9bdb883a", @@ -205,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "8bdbf5c3", "metadata": {}, "outputs": [ @@ -213,8 +403,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/comparison_tools/PairwiseCompare.py:80: UserWarning: DataFrame contains NaNs\n", - " warnings.warn(\"DataFrame contains NaNs\")\n", "/home/erikserrano/Programs/miniconda3/envs/cfret-map/lib/python3.11/site-packages/comparison_tools/PairwiseCompare.py:80: UserWarning: DataFrame contains NaNs\n", " warnings.warn(\"DataFrame contains NaNs\")\n" ] @@ -247,27 +435,27 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "c3058db2", "metadata": {}, "outputs": [], "source": [ "# Select only the relevant columns and add a reference column for healthy controls\n", "health_ref_pairwise_scores = healthy_ref_trt_pairwise_scores[\n", - " [\"pearsons_correlation\", \"Metadata_treatment__antehoc_group1\"]\n", + " [\"pearsons_correlation\", \"Metadata_treatment__posthoc_group1\"]\n", "].copy()\n", "health_ref_pairwise_scores[\"reference\"] = \"Healthy\"\n", "\n", "# Select only the relevant columns and add a reference column for failing controls\n", "failing_ref_pairwise_scores = failing_ref_trt_pairwise_scores[\n", - " [\"pearsons_correlation\", \"Metadata_treatment__antehoc_group1\"]\n", + " [\"pearsons_correlation\", \"Metadata_treatment__posthoc_group1\"]\n", "].copy()\n", "failing_ref_pairwise_scores[\"reference\"] = \"Failing\"\n", "\n", "# Combine the healthy and failing control dataframes into a single dataframe\n", "final_trt_pairwise_scores = (\n", " pd.concat([health_ref_pairwise_scores, failing_ref_pairwise_scores])\n", - " .rename(columns={\"Metadata_treatment__antehoc_group1\": \"Metadata_treatment\"})\n", + " .rename(columns={\"Metadata_treatment__posthoc_group1\": \"Metadata_treatment\"})\n", " .reset_index(drop=True)\n", ")\n", "\n", @@ -332,7 +520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/notebooks/exploratory-analysis/pairwise-analysis/r_plot-pairwise-scores.ipynb b/notebooks/exploratory-analysis/pairwise-analysis/r_plot-pairwise-scores.ipynb index be7582c..ca0c66d 100644 --- a/notebooks/exploratory-analysis/pairwise-analysis/r_plot-pairwise-scores.ipynb +++ b/notebooks/exploratory-analysis/pairwise-analysis/r_plot-pairwise-scores.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "85d1e8af", "metadata": { "vscode": { @@ -20,6 +20,14 @@ "suppressMessages(library(RColorBrewer)) # for ColorBrewer palettes" ] }, + { + "cell_type": "markdown", + "id": "e1d50b03", + "metadata": {}, + "source": [ + "Setting input paths and output paths. An error will be raised if the file does not exists" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -31,17 +39,21 @@ }, "outputs": [], "source": [ - "# setting dmso pairwise scores data path\n", - "dmso_pairwise_scores <- file.path(\"./results/final_pairwise_scores.csv\")\n", - "if (!file.exists(dmso_pairwise_scores)) {\n", - " stop(\"Please run ./pairwise-compair.ipynb first to generate the pairwise scores.\")\n", + "# Function to set file paths and check their existence\n", + "set_file_path <- function(file_name) {\n", + " file_path <- file.path(\"./results\", file_name)\n", + " if (!file.exists(file_path)) {\n", + " stop(paste(\"The file\", file_name, \n", + " \"does not exist. Please run the notebook to generate the required output files.\"))\n", + " }\n", + " return(file_path)\n", "}\n", "\n", - "# setting trt pairwise scores data path\n", - "trt_pairwise_scores <- file.path(\"./results/final_trt_pairwise_scores.csv\")\n", - "if (!file.exists(trt_pairwise_scores)) {\n", - " stop(\"Please run ./pairwise-compair.ipynb first to generate the pairwise scores.\")\n", - "}\n", + "# Setting file paths using the function\n", + "dmso_pairwise_scores <- set_file_path(\"final_pairwise_scores.csv\")\n", + "trt_pairwise_scores <- set_file_path(\"final_trt_pairwise_scores.csv\")\n", + "replicate_pairwise_scores <- set_file_path(\"final_replicate_pairwise_scores.csv\")\n", + "consensus_scores <- set_file_path(\"final_dmso_consensus_pairwise_scores.csv\")\n", "\n", "# creating a figures directory in the results folder\n", "plot_output_dir <- \"./results/figures\"\n", @@ -52,10 +64,10 @@ }, { "cell_type": "markdown", - "id": "bfa84a6e", + "id": "4752d8aa", "metadata": {}, "source": [ - "## Creating a histogram of the pairwise compare scores with only controls" + "loading the pairwise compare scores" ] }, { @@ -69,9 +81,18 @@ }, "outputs": [], "source": [ - "# loading csv files\n", - "dmso_pairwise_df <- fread(dmso_pairwise_scores)\n", - "trt_pairwise_df <- fread(trt_pairwise_scores)" + "dmso_pairwise_df <- fread(dmso_pairwise_scores) # loads the dmso pairwise scores\n", + "consensus_dmso_df <- fread(consensus_scores) # loads the dmso consensus scores\n", + "trt_pairwise_df <- fread(trt_pairwise_scores) # loads the trt pairwise scores\n", + "replicate_pairwise_df <- fread(replicate_pairwise_scores) # loads the replicate pairwise scores" + ] + }, + { + "cell_type": "markdown", + "id": "bfa84a6e", + "metadata": {}, + "source": [ + "## Creating a histogram of the pairwise compare scores with only controls" ] }, { @@ -169,12 +190,20 @@ "dmso_pairwise_histogram\n" ] }, + { + "cell_type": "markdown", + "id": "52e88199", + "metadata": {}, + "source": [ + "## Heatmaps of pairwise similarity scores compared to controls" + ] + }, { "cell_type": "markdown", "id": "02ed54dc", "metadata": {}, "source": [ - "Creating a heat map of the correlation pairwise scores of the controls to the individual scores of each compairson of controls" + "Creating a heatmap to visualize the pairwise correlation scores of controls, highlighting the individual comparison scores for each control." ] }, { @@ -308,17 +337,167 @@ "heatmap_plot" ] }, + { + "cell_type": "markdown", + "id": "1e3fdd85", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "source": [ + "Creating a heatmap to visualize the pairwise correlation scores of consensus profiles of the controls." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2f098ad4", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# updating column names for the consensus scores\n", + "consensus_dmso_df <- consensus_dmso_df %>%\n", + " rename(\n", + " metadata_plate_0 = Metadata_plate_name__posthoc_group0,\n", + " metadata_plate_1 = Metadata_plate_name__posthoc_group1\n", + " )\n", + "\n", + "# separating based on metadata_treatment (DMSO-positive or DMSO-negative)\n", + "healthy_dmso_consensus_df <- consensus_dmso_df %>%\n", + " filter(Metadata_treatment == \"DMSO-positive\") %>%\n", + " select(pearsons_correlation, Metadata_treatment, metadata_plate_0, metadata_plate_1) %>%\n", + " arrange(desc(pearsons_correlation))\n", + "failing_dmso_consensus_df <- consensus_dmso_df %>%\n", + " filter(Metadata_treatment == \"DMSO-negative\") %>%\n", + " select(pearsons_correlation, Metadata_treatment, metadata_plate_0, metadata_plate_1) %>%\n", + " arrange(desc(pearsons_correlation))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "60d33e5d", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TableGrob (3 x 1) \"arrange\": 3 grobs\n", + " z cells name grob\n", + "1 1 (2-2,1-1) arrange gtable[layout]\n", + "2 2 (3-3,1-1) arrange gtable[layout]\n", + "3 3 (1-1,1-1) arrange text[GRID.text.463]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 720, + "width": 480 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Make plots show up nice in the notebook\n", + "width <- 8\n", + "height <- 12\n", + "options(repr.plot.width = width, repr.plot.height = height)\n", + "\n", + "# Plot1: Create the consensus health controls heatmap\n", + "health_control_consensus_heatmap <- ggplot(healthy_dmso_consensus_df, aes(x = metadata_plate_0, y = metadata_plate_1)) +\n", + " geom_tile(aes(fill = pearsons_correlation), color = \"white\") +\n", + " geom_text(aes(label = round(pearsons_correlation, 2)), size = 7) +\n", + " scale_fill_gradient2(\n", + " low = \"blue\", mid = \"white\", high = \"red\", midpoint = 0, limits = c(-1, 1), name = \"Correlation\"\n", + " ) +\n", + " labs(\n", + " title = \"Pairwise score heatmap consensus healthy controls\", \n", + " x = \"\",\n", + " y = \"\"\n", + " ) +\n", + " theme_bw() +\n", + " theme(\n", + " plot.title = element_text(hjust = 0.5, size = 18), \n", + " axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 16),\n", + " axis.text.y = element_text(size = 16),\n", + " legend.text = element_text(size = 10), \n", + " legend.title = element_text(size = 10, margin = margin(b = 10))\n", + " )\n", + "\n", + "# Plot2: Create the consensus failing controls heatmap\n", + "failing_dmso_consensus_heatmap <- ggplot(failing_dmso_consensus_df, aes(x = metadata_plate_0, y = metadata_plate_1)) +\n", + " geom_tile(aes(fill = pearsons_correlation), color = \"white\") +\n", + " geom_text(aes(label = round(pearsons_correlation, 2)), size = 7) +\n", + " scale_fill_gradient2(\n", + " low = \"blue\", mid = \"white\", high = \"red\", midpoint = 0, limits = c(-1, 1), name = \"Correlation\"\n", + " ) +\n", + " labs(\n", + " title = \"Pairwise score heatmap consensus failing controls\", \n", + " x = \"\", \n", + " y = \"\"\n", + " ) +\n", + " theme_bw() +\n", + " theme(\n", + " plot.title = element_text(hjust = 0.5, size = 18), \n", + " axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 16),\n", + " axis.text.y = element_text(size = 16),\n", + " legend.text = element_text(size = 10),\n", + " legend.title = element_text(size = 10, margin = margin(b = 10))\n", + " )\n", + "\n", + "# Wrap both plots in a grid\n", + "consensus_dmso_pairwise_heatmap <- grid.arrange(\n", + " health_control_consensus_heatmap,\n", + " failing_dmso_consensus_heatmap,\n", + " ncol = 1,\n", + " top = textGrob(\"Pairwise consensus DMSO correlation scores\", gp = gpar(fontsize = 20, fontface = \"bold\"))\n", + ")\n", + "\n", + "# save the heatmap plot into the figures directory\n", + "ggsave(\n", + " filename = file.path(plot_output_dir, \"consensus_dmso_pairwise_heatmap.png\"),\n", + " plot = consensus_dmso_pairwise_heatmap,\n", + " width = width,\n", + " height = height,\n", + " dpi = 400,\n", + " bg = \"white\"\n", + ")\n", + "\n", + "# Display the heatmap\n", + "consensus_dmso_pairwise_heatmap\n" + ] + }, { "cell_type": "markdown", "id": "2bf573c7", "metadata": {}, "source": [ - "## Creating box plot " + "## Box plot of pairwise scores between treated wells and controls (healthy vs. failing)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "4f3ea4d4", "metadata": { "vscode": { @@ -332,20 +511,12 @@ "text": [ "Warning message:\n", "“\u001b[1m\u001b[22mThe `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.\n", - "\u001b[36mℹ\u001b[39m Please use the `linewidth` argument instead.”\n", - "Warning message in RColorBrewer::brewer.pal(n, pal):\n", - "“n too large, allowed maximum for palette Set3 is 12\n", - "Returning the palette you asked for with that many colors\n", - "”\n", - "Warning message in RColorBrewer::brewer.pal(n, pal):\n", - "“n too large, allowed maximum for palette Set3 is 12\n", - "Returning the palette you asked for with that many colors\n", - "”\n" + "\u001b[36mℹ\u001b[39m Please use the `linewidth` argument instead.”\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "plot without title" ] @@ -427,6 +598,181 @@ "\n", "pairwise_score_boxplot" ] + }, + { + "cell_type": "markdown", + "id": "deb43f0f", + "metadata": {}, + "source": [ + "## Creating a scatter plot of the pairwise controls between replicates" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1ed5acc9", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.table: 6 × 5
pearsons_correlationMetadata_treatmentplate_name_0plate_name_1compared_combinations
<dbl><chr><chr><chr><chr>
0.8598942UCD-000045012UCD-0000450_Plates_1_2
0.8220859UCD-000045013UCD-0000450_Plates_1_3
0.8666370UCD-000045014UCD-0000450_Plates_1_4
0.8121859UCD-000045023UCD-0000450_Plates_2_3
0.7955506UCD-000045024UCD-0000450_Plates_2_4
0.7956993UCD-000045034UCD-0000450_Plates_3_4
\n" + ], + "text/latex": [ + "A data.table: 6 × 5\n", + "\\begin{tabular}{lllll}\n", + " pearsons\\_correlation & Metadata\\_treatment & plate\\_name\\_0 & plate\\_name\\_1 & compared\\_combinations\\\\\n", + " & & & & \\\\\n", + "\\hline\n", + "\t 0.8598942 & UCD-0000450 & 1 & 2 & UCD-0000450\\_Plates\\_1\\_2\\\\\n", + "\t 0.8220859 & UCD-0000450 & 1 & 3 & UCD-0000450\\_Plates\\_1\\_3\\\\\n", + "\t 0.8666370 & UCD-0000450 & 1 & 4 & UCD-0000450\\_Plates\\_1\\_4\\\\\n", + "\t 0.8121859 & UCD-0000450 & 2 & 3 & UCD-0000450\\_Plates\\_2\\_3\\\\\n", + "\t 0.7955506 & UCD-0000450 & 2 & 4 & UCD-0000450\\_Plates\\_2\\_4\\\\\n", + "\t 0.7956993 & UCD-0000450 & 3 & 4 & UCD-0000450\\_Plates\\_3\\_4\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.table: 6 × 5\n", + "\n", + "| pearsons_correlation <dbl> | Metadata_treatment <chr> | plate_name_0 <chr> | plate_name_1 <chr> | compared_combinations <chr> |\n", + "|---|---|---|---|---|\n", + "| 0.8598942 | UCD-0000450 | 1 | 2 | UCD-0000450_Plates_1_2 |\n", + "| 0.8220859 | UCD-0000450 | 1 | 3 | UCD-0000450_Plates_1_3 |\n", + "| 0.8666370 | UCD-0000450 | 1 | 4 | UCD-0000450_Plates_1_4 |\n", + "| 0.8121859 | UCD-0000450 | 2 | 3 | UCD-0000450_Plates_2_3 |\n", + "| 0.7955506 | UCD-0000450 | 2 | 4 | UCD-0000450_Plates_2_4 |\n", + "| 0.7956993 | UCD-0000450 | 3 | 4 | UCD-0000450_Plates_3_4 |\n", + "\n" + ], + "text/plain": [ + " pearsons_correlation Metadata_treatment plate_name_0 plate_name_1\n", + "1 0.8598942 UCD-0000450 1 2 \n", + "2 0.8220859 UCD-0000450 1 3 \n", + "3 0.8666370 UCD-0000450 1 4 \n", + "4 0.8121859 UCD-0000450 2 3 \n", + "5 0.7955506 UCD-0000450 2 4 \n", + "6 0.7956993 UCD-0000450 3 4 \n", + " compared_combinations \n", + "1 UCD-0000450_Plates_1_2\n", + "2 UCD-0000450_Plates_1_3\n", + "3 UCD-0000450_Plates_1_4\n", + "4 UCD-0000450_Plates_2_3\n", + "5 UCD-0000450_Plates_2_4\n", + "6 UCD-0000450_Plates_3_4" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# data manipulation to create a new column for the compared combinations\n", + "# updating the plate names to only have the numeric value\n", + "# creating a new column for the compared combinations\n", + "replicate_pairwise_df <- replicate_pairwise_df %>%\n", + " mutate(\n", + " plate_name_0 = gsub(\"Plate_\", \"\", plate_name_0),\n", + " plate_name_1 = gsub(\"Plate_\", \"\", plate_name_1),\n", + " compared_combinations = paste0(\n", + " Metadata_treatment, \"_Plates_\", plate_name_0, \"_\", plate_name_1\n", + " )\n", + " )\n", + "head(replicate_pairwise_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "26c7ab02", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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xY+PHj5eU/Pzzz4PsHzdF9qlI39QV2XQV6UkF1b/9KzJkosx+VnCIFentcA5ZkLONIpuieoPbs2dPvxkp34noiy++8C6g4JzW4Gys1McPhiJnYsHQybF48uTJfttZv369pGSvXr3k9kNkjWw492i9HVwAAGjapN9WAwAAkOu5556bM2eOZOHly5ePHDlSW1vrW/6pp55yP9ZFloSEBEn7clvw67bbbsvMzPT71qhRow4dOuT5Mz8/f+TIke7XmzZt8izv1atXamrq1VdfvW7dOveSLVu2eN69ePFiYWGh58/mzZtfccUVwYeXnZ2dkpJSVVXlWfLqq6/u3Lnz7rvvHjFiRJcuXYJpZMeOHZIl3bt391tScue0vXv3Bh+qIAjbt2+XLBkzZozfki+++OKLL77o9628vDzJkhtuuCHQGocNG5acnFxdXe1ZsmfPHovFEh8fH6hKPSMut3zYOjZIKu0jfqk9THfffbf3T3g9hg4d+vXXX3svqaysDDZoQRAU2qcifVNXZNNVpCcVVP/2r8iQ+VVPPys4xIr0tt6GTFBoU1RvcO+++27Jw9LcQpiI1JvT1Pv4vsJ5lJHQ5Fjs94mDgiCMGjUqJibG4XB4lhw9etRqtfp9rmTIInFkm+TBBQCApo0EIQAAaKyDBw8GX/jqq69+4YUXAr27e/furVu3Hjx48OLFi1VVVS6Xy/OW5I5DoiiGEKqv0aNH1/PW/PnzPX/u2bPH83rz5s2e18OGDRMEYfjw4Z4l+/fvr6iocD8vZ/fu3d5tjho1yu8lwnrcc8893mEIgvDNN9988803giA0b958yJAhI0eOvPbaa/v37+/3OqYgCEeOHJEseeqpp7xvkepRXl7u/eePP/4o64LX/v37JUsCXRiqx759+yRL+vbtG6iwyWTq1q3bd99951lis9mKiorqycLWM+Jyy4etY72Ffx/xS+1hGjRokN/lkl85CIJgtVobDve/NX6fivRNXalNt/E9KVfI278iQ+ZXPf2s7BAr0tvhH7L6KbIpqje4Q4YM8bvcdyKyWCz1N6XenKb4x9fJUUZCk2Ox7/Mm3eLj4zt06FBUVORZ4nA4zp49K7ltZiNF4shG7sEFAICoRYIQAACEz5AhQ9auXev3WsyGDRtmzJjh/Yu98OjWrVugt0aNGuX9p+f2Slar1ftr3e4vdF9zzTVms9lmswmC4HK58vLy3A+U2rVrl3cjcrNTgiD8/ve/X7dune8TWQRBKCsrW7dunfuXizk5OVOnTp0+fbrv9amKigrJEu9kZz0cDkdpaWn79u2DDFVyuUcQhObNmwdZ16OsrEyypHXr1vWUz87Oliy5ePFiPeXrGXG55cPWsW5a7SN+qT1MOTk5fpf7vc4oV+P3qUjf1JXadBvfk8Fr5PavyJD5VU8/KzvEivR2OIcsGIpsiuoNroITkXpzmoIfX1dHGQlNjsVt27YN9FZWVpZ3glAQBO9fvCkiEkc2Eg8uAABEOb5rAwAAwiElJeWVV17Jzc31e3Vj7ty548eP1+SaVHp6eqC3srKyevbs6fnz2LFj7mu1O3bs8P6tgDvnl5CQ4P37AM9dRiUJwkC3h6pHq1atNm7c2L9///qLFRcX/+1vf+vRo8fzzz/v/a1woXE3AZNV99KlS5IlkptWhdZIYmJiPeV9r676tuCtnhGXWz5sHStouo/4pfYwBdpyFPmtQOP3qUjf1JXadBvfk0Fq/PavyJD5VU8/KzvEivR22IYsSIpsiuoNroITkXpzmlIfX29HGQlNjsXJycmB3vLdl73vD6yISBzZiDu4AAAAfkEIAAAay2g0+t4202QyJSUl5eTk9OnTZ9y4cVOnTk1JSfFbfcuWLTNnzpQs7Nmz56hRo7KysmJjY91L/va3v/n+GqPx6r/N46hRow4fPuz5c8+ePePHj/e+v2jPnj09v/kYPXr0t99+637tSRB632K0RYsWvXr1CiHIrl275ufnL1my5B//+MeePXvqufWT3W5//fXXz549u2zZMs9C36eUderUydOx9XM6ncHHGRMjPbd0/6RSFt8Rsdvt9ZT3vRtb/VfQ5N7Ys57yYetYbfcRv9QeJrU1cp+K9E1dwU23kT0ZDEW2f0WGzK96+lnxIVakt8MwZMFTZFNUb3AjgiIfX4dHGQlNjsXeTxmU8O1kZR9AKETmyEbWwQUAAAgkCAEAQON99tlnkydPDrn6iy++KFnyzDPPzJ49W/K1+uXLl4f/stTo0aMXLlzo+TM/P1+SIPT+ReDo0aP/9Kc/uV+7H0NYXl7ufYeoEB5A6GEymR5++OGHH364pKRk48aNeXl5O3bsOHz4sN9vTC9fvvzWW2+98cYb3X/6fu/+iy++UOoRTd7cj130FsJ3yZs1ayZZUv/PpHzf9W1BJWHrWB3uIxE0TIE0Zp+K9E1d2U23MT0ZDEW2f0WGTC41hliR3lZ7yIKnyKaoyeDqhyIfX4dHmeCpdyyurKwMNM36dnJqamrj1+gtEkc2sg4uAABA4BajAABAWxUVFdu2bfNekpWV9dprr/nedKu0tDSMcf1M8hjCPXv2OBwO7+epeD9TcOjQoWaz2f3a5XLt2LGj8fcX9dWqVat77rln8eLFBw8erKys3LBhw29+8xvfG7cuXrzYu4rkXd8H2yiiZcuWkiUnT56U20i7du0kSwoLC+spf/ToUcmSeh4apKzwdKw+95EIGqYGhbBPRfqmrtKmG0JPNkip7V+RIZNL1SFWpLfVGDJZFNkUNRlc/Wj8x9fnUSZ46h2LJU8Z9HbixAnJkhYtWiiyUo9IHNkIOrgAAAA3EoQAAEBLx44dk9xTqG/fvr53VTp48GBVVVUY4/pZy5Ytvb/4nJ+ff/Dgwbq6OvefBoPBO0EoeQzh7t27ve8vKvx3NlERycnJY8eOnTNnTmFhoeRJLQcPHvS8vvrqqyUVvd9V0ODBgyVL8vPz/ZbcuHHj5P+2YcMG91sjRoyQFN66dWugNRYVFZ07d857Sc+ePRW/SBdIeDpWn/tIBA2TLEHuU5G+qYdh0w2yJxuk1PavyJDJFbYhVqS3lRoyWRTZFDUZXP1o/MfX51EmeOpNaNu3b/e7/OjRo5WVld5LWrdu7fuDv0aKxJGNoIMLAABwI0EIAAC0JLnCIgR4RNOCBQskS+p5GImyvH9EePbs2XXr1nn+7NWrl+TqrXcKcO/evd6XcrKyskJ7AKHL5Tp58uRXX3319ttv19TU+C2Tnp4+Y8YM7yUVFRWe10OHDpWUX7Nmjd92Tpw4sX///tLSUllPyPMYOXKkZMnq1av93glqxYoVn/+3zMxM91tjx46V3Ih11apVgT740qVLJUvGjRsXQuShCU/HqrqPhHxNMIKGyVfj96lI39SV2nQb35MNUmr7V2TI5FJ2iBXp7TAMWSB+ZxtFNkVNBlc/Gv/x9X8mVj/1jsUffPCB35789NNPJUuuuuqqYBqURecjq94eLWg6UwEAEG1IEAIAAC2lpaVJlnj/RM/tm2++8X4QoFvYrgJIfvb33nvveV773jLUO5u4d+/effv2BWon+LUnJiZ27Nhx4sSJ06dPX7JkSaCSkk7zvjPV0KFDu3bt6v3uF198sXPnTkkLLpfrxhtv7NevX6tWrcxmc4sWLe677z5Z0Q4ePLhPnz7eS4qKit5//31JsSNHjnz00UfeSzp06OC5uNaxY0fJIy3LyspeeOEF39UdOnRo3rx53kuMRuMTTzwhK+bGCE/HKriP+N5VrKCgIPhIvEXQMEkosk9F+qauyKarSE82SKntX5Ehk0vBIVakt8MzZJ74JUv8zjaKbIqaDK5+NP7j6/9MrH7qHYuPHDny7rvvShaeP3/+r3/9q2ThlClTQoq9Proa2XDu0eGcqQAAAAlCAACgpe7du5tMJu8l5eXljz/+uMViEQShurp6zpw5kyZNcrlciYmJ3sWqqqq802/qkTyG0PupM74JwmHDhnkeQ1hSUuJ95SK0BGHPnj2tVqvnz9/+9reffPKJb7EdO3a8/PLL3kuuvfZaz2uDwTB9+nTvd0VRvPnmmzdv3uxZcunSpWnTpnnuzuRyucrKykK44DVz5kzJkieeeGLp0qWeb4jn5eVNnjxZ8n3wZ5991vt3Nn/4wx8kW8Wbb745Y8YMzxUrh8OxcuXK6667TtLOtGnTunTpIjfmkIWnYxXcR1JSUiSNr169+ptvvnG/9vvDgnpEyjBJKLJPCRG+qSuy6SrVk/VTcPtXZMjkUmqIFent8AyZW5CzjVKzqCaDqx+N/Pj6PxOrn6rH4qeeeurvf/+7zWZz/3nw4MEJEyacP3/eu0xKSooaCUJBTyMbzj06nDMVAAAQRAAAADkmTJggOZ1Yu3ZtYxq87rrrfE9RzGZz27Ztva9r/N///Z/kQl5OTs706dNnzJjhbufXv/61pJETJ05I1hVMGV+Sb0O7GQyGsrIy38LDhg3ze9JVUFDgW7jBeE6fPi25aiMIQqtWrcaNG3f77bffeeedv/jFLzp27CgpEB8ff/jwYe92HA6H38B69OgxefLk4cOHJyUlSd665ZZbGuwZv2644QbfFTVv3nzAgAHZ2dm+b40ZM8bpdEoamT17tm9Js9l8xRVX9O/f3/dL8e7PUlVVJbeHG1lekY5tcKVK7SM2my0+Pt63qfbt23fr1q1Lly5y+yH8w7Ry5UpJyffff7+eAfKl1D4lRvim3vhNV8GerJ9S278iQxbCQUSRIVakt8M2ZKKc2Uapw1M4B7fBiSj8TTXy44fzTCwYOjkWe74Hlp6ePnDgwEBfy/jLX/4SQvyRNbLh3KPDOVMBAAAShAAAQB7FE4R5eXm+dy6SmD59uiiKgwYN8n0rLS3N3Y56CcIHH3zQd719+/b1W/h3v/udb+Hs7Gy/hYOJ54MPPpD1EweTyeQ3ZXLq1Cm/mU6/BgwYcPny5QZ7xq/y8vLBgwcHuaKBAwf6XZHL5Xr66aeD/9Q9e/Y8duxYaD3cmPKiEh3b4EqV2kd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+ "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 720, + "width": 1200 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Set figure size in notebook\n", + "width <- 20\n", + "height <- 12\n", + "options(repr.plot.width = width, repr.plot.height = height)\n", + "\n", + "# Reorder treatments (optional: based on some logic)\n", + "replicate_pairwise_df$Metadata_treatment <- factor(replicate_pairwise_df$Metadata_treatment,\n", + " levels = unique(replicate_pairwise_df$Metadata_treatment))\n", + "\n", + "# Create styled boxplot\n", + "replicate_pairwise_plot <- ggplot(replicate_pairwise_df, aes(x = Metadata_treatment, y = pearsons_correlation)) +\n", + " geom_boxplot(outlier.size = 0.5, linewidth = 0.8, fill = \"#73add1\") +\n", + " \n", + " geom_hline(yintercept = 0, color = \"lightcoral\", linetype = \"dashed\", linewidth = 1.2) +\n", + "\n", + " # Adjust axis and labels\n", + " labs(\n", + " title = \"Pairwise correlation scores across technical replicates\",\n", + " x = \"Treatment\",\n", + " y = \"Pearson's correlation\"\n", + " ) +\n", + "\n", + " scale_y_continuous(breaks = seq(-1, 1, by = 0.2)) +\n", + "\n", + " # Match styling\n", + " theme_bw() +\n", + " theme(\n", + " legend.position = \"none\",\n", + " axis.text.x = element_text(size = 14, angle = 90, vjust = 0.5),\n", + " axis.text.y = element_text(size = 14),\n", + " axis.title.x = element_text(size = 17, face = \"bold\", vjust = -1.0),\n", + " axis.title.y = element_text(size = 17, face = \"bold\"),\n", + " plot.title = element_text(hjust = 0.5, size = 25, face = \"bold\")\n", + " )\n", + "\n", + "# Save to figures directory if needed\n", + "ggsave(\n", + " filename = file.path(plot_output_dir, \"replicate_pairwise_boxplot.png\"),\n", + " plot = replicate_pairwise_plot,\n", + " width = width,\n", + " height = height,\n", + " dpi = 300\n", + ")\n", + "\n", + "replicate_pairwise_plot\n" + ] } ], "metadata": { diff --git a/notebooks/exploratory-analysis/pairwise-analysis/results/figures/consensus_dmso_pairwise_heatmap.png b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/consensus_dmso_pairwise_heatmap.png new file mode 100644 index 0000000..025c09d Binary files /dev/null and b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/consensus_dmso_pairwise_heatmap.png differ diff --git a/notebooks/exploratory-analysis/pairwise-analysis/results/figures/replicate_pairwise_boxplot.png b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/replicate_pairwise_boxplot.png new file mode 100644 index 0000000..46a1e48 Binary files /dev/null and b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/replicate_pairwise_boxplot.png differ diff --git a/notebooks/exploratory-analysis/pairwise-analysis/results/figures/trt_pairwise_boxplot.png b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/trt_pairwise_boxplot.png index 3a53a59..61aca47 100644 Binary files a/notebooks/exploratory-analysis/pairwise-analysis/results/figures/trt_pairwise_boxplot.png and b/notebooks/exploratory-analysis/pairwise-analysis/results/figures/trt_pairwise_boxplot.png differ diff --git a/notebooks/exploratory-analysis/pairwise-analysis/results/final_dmso_consensus_pairwise_scores.csv b/notebooks/exploratory-analysis/pairwise-analysis/results/final_dmso_consensus_pairwise_scores.csv new file mode 100644 index 0000000..0105856 --- /dev/null +++ b/notebooks/exploratory-analysis/pairwise-analysis/results/final_dmso_consensus_pairwise_scores.csv @@ -0,0 +1,13 @@ +pearsons_correlation,Metadata_treatment,Metadata_plate_name__posthoc_group0,Metadata_plate_name__posthoc_group1 +0.816356482791563,DMSO-positive,Plate_1,Plate_2 +0.8183390317291328,DMSO-positive,Plate_1,Plate_3 +0.7682268638890466,DMSO-positive,Plate_1,Plate_4 +0.9135304158963791,DMSO-positive,Plate_2,Plate_3 +0.8940287037502324,DMSO-positive,Plate_2,Plate_4 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a/notebooks/exploratory-analysis/pairwise-analysis/results/final_trt_pairwise_scores.csv b/notebooks/exploratory-analysis/pairwise-analysis/results/final_trt_pairwise_scores.csv index 3f877c5..6a893fa 100644 --- a/notebooks/exploratory-analysis/pairwise-analysis/results/final_trt_pairwise_scores.csv +++ b/notebooks/exploratory-analysis/pairwise-analysis/results/final_trt_pairwise_scores.csv @@ -1,6017 +1,6017 @@ pearsons_correlation,Metadata_treatment,reference,Metadata_Pathway -0.46561260381566383,UCD-0000450,Healthy,Neuronal Signaling -0.48899301239921417,UCD-0000450,Healthy,Neuronal Signaling -0.42915298090967546,UCD-0000450,Healthy,Neuronal Signaling -0.6118066802053691,UCD-0000450,Healthy,Neuronal Signaling -0.5546021343656695,UCD-0000450,Healthy,Neuronal Signaling -0.5371843999606605,UCD-0000450,Healthy,Neuronal Signaling -0.49912615561768225,UCD-0000450,Healthy,Neuronal Signaling -0.6652118470456893,UCD-0000450,Healthy,Neuronal Signaling 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Signaling --0.0763081989813449,UCD-0001024,Healthy,Neuronal Signaling --0.07326867774088267,UCD-0001024,Healthy,Neuronal Signaling --0.05331551933879372,UCD-0001024,Healthy,Neuronal Signaling --0.021695852808257397,UCD-0001024,Healthy,Neuronal Signaling --0.04945746636572453,UCD-0001024,Healthy,Neuronal Signaling --0.011563175538976421,UCD-0001024,Healthy,Neuronal Signaling --0.030449050075720123,UCD-0001024,Healthy,Neuronal Signaling -0.007379418268033991,UCD-0001024,Healthy,Neuronal Signaling --0.16411828500489464,UCD-0001024,Healthy,Neuronal Signaling --0.12216197771837148,UCD-0001024,Healthy,Neuronal Signaling --0.06537581445887314,UCD-0001024,Healthy,Neuronal Signaling --0.03241708891675307,UCD-0001024,Healthy,Neuronal Signaling --0.21144038643838464,UCD-0001024,Healthy,Neuronal Signaling +-0.07630819898134474,UCD-0001024,Healthy,Neuronal Signaling +-0.07326867774088265,UCD-0001024,Healthy,Neuronal Signaling +-0.05331551933879371,UCD-0001024,Healthy,Neuronal Signaling 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Executes pairwise-compare generating scores when comparing controls with each other and other where we us controls as areference and compare it to all treated failing cells -# 2. r_plot-pairwise-compare.r generates the plots for the pairwise comparison +# 1. generates consensus profiles for controls +# 2. Executes pairwise-compare generating scores when comparing controls with each other and other where we us controls as areference and compare it to all treated failing cells +# 3. r_plot-pairwise-compare.r generates the plots for the pairwise comparison # Activate the conda environment for CFReT map analysis conda activate cfret-map @@ -13,6 +14,9 @@ conda activate cfret-map # and save them in the 'nbconverted/' directory jupyter nbconvert --to script --output-dir=nbconverted/ *.ipynb +# generate consensus profiles for controls +python nbconverted/dmso-consensus-profiles.py + # Execute the profile concatenation script python nbconverted/pairwise-compare.py diff --git a/poetry.lock b/poetry.lock index 46cf4b1..909d9a4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3,7 +3,7 @@ [metadata] lock-version = "2.0" python-versions = "3.11.*" -content-hash = "43161cc3d4e81347f3d9fd3b49725beea3b2183f2aec679843674f2cfda8793e" +content-hash = 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