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feat: add pdac_nac project
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‎notebooks/00-pdac_nac.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"using Muon\n",
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"using RData\n",
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"using Revise\n",
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"using ISCHIA\n",
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"using DataFrames\n",
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"using Combinatorics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AnnData object 216180 ✕ 17764"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"spatial_object = readh5ad(\"../data/06-pdac_nac-clusters-rmv_unk.h5ad\")\n",
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"# adata = readh5ad(\"../data/05-pdac_nac-clusters.h5ad\")\n",
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"lr_network = load(\"../data/lr_network.rds\")\n",
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"\n",
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"# # Remove spots where neoadjuvant_chemo is unknown\n",
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"# mask = .!(adata.obs.neoadjuvant_chemo .== \"Unknown\")\n",
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"# spatial_object = @view adata[mask, :]\n",
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"# # spatial_object = adata[mask, :]\n",
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"\n",
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"spatial_object"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2-element Vector{String}:\n",
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" \"No\"\n",
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" \"Yes\""
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"display(unique(spatial_object.obs.neoadjuvant_chemo))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"gene_names = collect(spatial_object.var_names)\n",
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"spatial_object.var.name = gene_names\n",
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"\n",
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"# Create LR_Pairs column\n",
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"lr_network[!, :LR_Pairs] = string.(lr_network.from, \"_\", lr_network.to);\n",
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"lr_network = lr_network[:, [:from, :to, :LR_Pairs]];\n",
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"\n",
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"# Filter lr_network to only include genes in adata\n",
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"from_filter = in.(lr_network[!, :from], Ref(gene_names))\n",
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"to_filter = in.(lr_network[:, :to], Ref(gene_names))\n",
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"all_LR_network = lr_network[from_filter .& to_filter, :];"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Extract unique genes and common genes\n",
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"all_LR_genes = unique(vcat(all_LR_network[:, :from], all_LR_network[:, :to]))\n",
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"all_LR_genes_comm = intersect(all_LR_genes, collect(gene_names));\n",
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"\n",
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"# Create LR.pairs and LR.pairs.AllCombos\n",
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"LR_pairs = all_LR_network[:, :LR_Pairs]\n",
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"all_combos = [join(combo, \"_\") for combo in combinations(all_LR_genes_comm, 2)];"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\"neoadjuvant_chemo\""
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# spatial_object = adata\n",
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"LR_list = all_LR_genes_comm\n",
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"LR_pairs = LR_pairs\n",
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"exp_th = 1\n",
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"corr_th = 0.2;\n",
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"\n",
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"cc_column = \"CC_k10\"\n",
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"cc_list = [\"CC10\"]\n",
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"# Condition = unique(spatial_object.obs[!, \"orig.ident\"])\n",
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"condition_list = [\"Yes\", \"No\"]\n",
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"condition_column = \"neoadjuvant_chemo\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running for CC10\n",
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"Running for Yes\n",
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"Preparing L-R presence/absence matrix\n",
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"Calculating L-R pairs correlation\n",
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"Preparing for cooccurrence\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\r"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cooccurrence calculation starts...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32mCalculate Incidence 100%|████████████████████████████████| Time: 0:02:17\u001b[39mm\n",
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"\u001b[32mCalculate Co-occurrences 100%|███████████████████████████| Time: 0:02:15\u001b[39m\n",
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"\u001b[32mMain Comp 100%|██████████████████████████████████████████| Time: 0:00:43\u001b[39m\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cooccurrence calculation ended\n",
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"\n",
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"Summary of cooccurrence results:\n",
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"Of 824970 species pair combinations, 573708 pairs (69.54%) were removed from the analysis because expected co-occurrence was < 1 and\n",
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"251262 pairs were analyzed\n",
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"\n",
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"Cooccurrence Summary:\n",
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"\n",
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"Species => 1285\n",
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"Non-random (%) => 66.3\n",
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"Sites => 4494\n",
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"Negative => 2665\n",
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"Random => 84781\n",
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"Positive => 163816\n",
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"Unclassifiable => 0\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32mCalculate Significantly occurring pairs 2%|█ | ETA: 1 days, 7:24:31\u001b[39mm\r"
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]
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}
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],
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"source": [
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"for cc in cc_list\n",
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" println(\"Running for $cc\")\n",
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" for condition in condition_list\n",
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" println(\"Running for $condition\")\n",
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" lr_result = find_enriched_LR_pairs(\n",
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" spatial_object,\n",
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" [cc],\n",
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" [condition],\n",
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" LR_list,\n",
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" LR_pairs,\n",
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" exp_th,\n",
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" corr_th,\n",
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" cc_column=cc_column,\n",
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" condition_column=condition_column\n",
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" )\n",
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"\n",
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" CSV.write(\"outputs/pdac_nac/$(cc)_lr_enrichment_$(condition).csv\", lr_result[\"enriched_LRs\"])\n",
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" CSV.write(\"outputs/pdac_nac/$(cc)_cooccurr_mat_$(condition).csv\", lr_result[\"cooccurrence_table\"].results)\n",
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" end\n",
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"end"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.9.3",
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"language": "julia",
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"name": "julia-1.9"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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