06_072222_Zach_singlecell_meninges_prep_cpdb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.8.2 anndata==0.7.6 umap==0.5.1 numpy==1.21.6 scipy==1.5.2 pandas==1.3.5 scikit-learn==0.23.2 statsmodels==0.12.1 python-igraph==0.8.3 leidenalg==0.8.2 pynndescent==0.5.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10839 × 3266\n",
       "    obs: 'sample_id', 'condition', 'site', 'sequencing_batch', 'scrublet_score', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'gmm_pct_count_clusters_keep', 'is_doublet', 'filter_rna', 'S_score', 'G2M_score', 'phase', 'leiden', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_UP', 'leiden_R', 'celltype', 'keep'\n",
       "    var: 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'\n",
       "    uns: 'celltype_colors', 'condition_colors', 'filter_rna_colors', 'hvg', 'is_doublet_colors', 'keep_colors', 'leiden', 'leiden_R_colors', 'leiden_colors', 'neighbors', 'pca', 'rank_genes_groups', 'sample_id_colors', 'umap'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'\n",
       "    obsp: 'connectivities', 'distances'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import tools\n",
    "\n",
    "sc.logging.print_header()\n",
    "\n",
    "os.chdir(os.path.expanduser('/home/jovyan/Zach/scanpy/'))\n",
    "adata = sc.read_h5ad('h5ad/meninges_integrated.h5ad')\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color = 'celltype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10839 × 29836\n",
       "    obs: 'sample_id', 'condition', 'site', 'sequencing_batch', 'scrublet_score', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'gmm_pct_count_clusters_keep', 'is_doublet', 'filter_rna', 'S_score', 'G2M_score', 'phase', 'leiden', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_UP', 'leiden_R', 'celltype', 'keep'\n",
       "    uns: 'filter_rna_colors'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    layers: 'counts'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_raw = sc.read_h5ad('h5ad/combined_raw.h5ad')\n",
    "adata_raw = adata_raw[adata_raw.obs_names.isin(adata.obs_names)].copy()\n",
    "adata_raw.obs = adata.obs.copy()\n",
    "adata_raw.obsm = adata.obsm.copy()\n",
    "adata_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### convert to human symbols first\n",
    "import anndata2ri\n",
    "anndata2ri.activate()\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: SingleCellExperiment \n",
      "dim: 29836 10839 \n",
      "metadata(1): filter_rna_colors\n",
      "assays(2): X counts\n",
      "rownames(29836): Xkr4 Gm1992 ... Vmn2r122 CAAA01147332.1\n",
      "rowData names(0):\n",
      "colnames(10839): Men_naive_AAACCCATCATCTGTT-1\n",
      "  Men_naive_AAACGAAGTCATACCA-1 ... VSV_HUB_TTTGTTGTCAATCCAG-1\n",
      "  VSV_HUB_TTTGTTGTCTCAACGA-1\n",
      "colData names(26): sample_id condition ... celltype keep\n",
      "reducedDimNames(2): PCA UMAP\n",
      "altExpNames(0):\n"
     ]
    }
   ],
   "source": [
    "%%R -i adata_raw\n",
    "adata_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: \n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:biomaRt’:\n",
      "\n",
      "    select\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:Biobase’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:GenomeInfoDb’:\n",
      "\n",
      "    intersect\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    collapse, desc, intersect, setdiff, slice, union\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from ‘package:matrixStats’:\n",
      "\n",
      "    count\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    first, intersect, rename, setdiff, setequal, union\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(biomaRt)\n",
    "library(dplyr)\n",
    "mart <- useMart(\"ensembl\", dataset=\"hsapiens_gene_ensembl\")\n",
    "m <- getBM(attributes=c(\"external_gene_name\", \"mmusculus_homolog_associated_gene_name\"), mart=mart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "library(SingleCellExperiment)\n",
    "counts <- counts(adata_raw)\n",
    "idx <- match(row.names(counts), m$mmusculus_homolog_associated_gene_name)\n",
    "ids <- m$external_gene_name[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: SingleCellExperiment \n",
      "dim: 18136 10839 \n",
      "metadata(1): filter_rna_colors\n",
      "assays(2): X counts\n",
      "rownames(18136): Xkr4 Rp1 ... mt-Cytb Gm16367\n",
      "rowData names(0):\n",
      "colnames(10839): Men_naive_AAACCCATCATCTGTT-1\n",
      "  Men_naive_AAACGAAGTCATACCA-1 ... VSV_HUB_TTTGTTGTCAATCCAG-1\n",
      "  VSV_HUB_TTTGTTGTCTCAACGA-1\n",
      "colData names(26): sample_id condition ... celltype keep\n",
      "reducedDimNames(2): PCA UMAP\n",
      "altExpNames(0):\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "adata_raw_h <- adata_raw[-which(is.na(ids)), ]\n",
    "adata_raw_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R -o idsx\n",
    "idsx <- ids[!is.na(ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R -o adata_raw_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10839 × 18136\n",
       "    obs: 'sample_id', 'condition', 'site', 'sequencing_batch', 'scrublet_score', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'gmm_pct_count_clusters_keep', 'is_doublet', 'filter_rna', 'S_score', 'G2M_score', 'phase', 'leiden', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_DARK_ZONE_VS_NAIVE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_DARK_ZONE_BCELL_UP', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_DN', 'GSE23925_LIGHT_ZONE_VS_NAIVE_BCELL_UP', 'leiden_R', 'celltype', 'keep'\n",
       "    uns: 'filter_rna_colors'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    layers: 'counts'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_raw_h.var_names = list(idsx)\n",
    "adata_raw_h.var_names_make_unique()\n",
    "adata_raw_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 10839 × 18136\n",
       "    obs: 'sample_id', 'condition', 'site', 'sequencing_batch', 'celltype'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    layers: 'counts'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### only need condition and celltype\n",
    "adata_raw_h.obs = adata_raw_h.obs[['sample_id', 'condition', 'site', 'sequencing_batch', 'celltype',]]\n",
    "adata_raw_h.uns = {}\n",
    "adata_raw_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise\n",
    "sc.pp.normalize_total(adata_raw_h, target_sum=1e4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_raw_h.obs['cpdb_celltype'] = [x + '_' + y for x,y in zip(adata_raw_h.obs['condition'], adata_raw_h.obs['celltype'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into condition\n",
    "naive = adata_raw_h[adata_raw_h.obs['condition'] == 'naive'].copy()\n",
    "vsv = adata_raw_h[adata_raw_h.obs['condition'] == 'VSV'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/my-conda-envs/dandelion/lib/python3.7/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'cpdb_celltype' as categorical\n"
     ]
    }
   ],
   "source": [
    "# save\n",
    "naive.write_h5ad('h5ad/meninges_integrated_naive.h5ad', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/my-conda-envs/dandelion/lib/python3.7/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Reordering categories will always return a new Categorical object.\n",
      "  c.reorder_categories(natsorted(c.categories), inplace=True)\n",
      "... storing 'cpdb_celltype' as categorical\n"
     ]
    }
   ],
   "source": [
    "vsv.write_h5ad('h5ad/meninges_integrated_vsv.h5ad', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out the meta\n",
    "naive_meta = pd.DataFrame(naive.obs['cpdb_celltype']).reset_index()\n",
    "naive_meta.columns = ['cell', 'Cell_type']\n",
    "naive_meta.to_csv('h5ad/meninges_integrated_naive_meta.txt', sep = '\\t', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write out the meta\n",
    "vsv_meta = pd.DataFrame(vsv.obs['cpdb_celltype']).reset_index()\n",
    "vsv_meta.columns = ['cell', 'Cell_type']\n",
    "vsv_meta.to_csv('h5ad/meninges_integrated_vsv_meta.txt', sep = '\\t', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "conda activate cpdb\n",
    "cd /home/jovyan/Zach/scanpy/\n",
    "cellphonedb method statistical_analysis h5ad/meninges_integrated_naive_meta.txt h5ad/meninges_integrated_naive.h5ad --threads=23 --output-path naive_cpdb --counts-data gene_name\n",
    "\n",
    "cellphonedb method statistical_analysis h5ad/meninges_integrated_vsv_meta.txt h5ad/meninges_integrated_vsv.h5ad --threads=23 --output-path vsv_cpdb --counts-data gene_name\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prepare the SCE object from cpdb h5ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.8.2 anndata==0.7.6 umap==0.5.1 numpy==1.21.6 scipy==1.5.2 pandas==1.3.5 scikit-learn==0.23.2 statsmodels==0.12.1 python-igraph==0.8.3 leidenalg==0.8.2 pynndescent==0.5.2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import tools\n",
    "\n",
    "sc.logging.print_header()\n",
    "\n",
    "os.chdir(os.path.expanduser('/home/jovyan/Zach/scanpy/'))\n",
    "adata1 = sc.read_h5ad('h5ad/meninges_integrated_naive.h5ad')\n",
    "adata2 = sc.read_h5ad('h5ad/meninges_integrated_vsv.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "import anndata2ri\n",
    "anndata2ri.activate()\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R -i adata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R -i adata2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: SingleCellExperiment \n",
      "dim: 18136 10839 \n",
      "metadata(0):\n",
      "assays(2): X counts\n",
      "rownames(18136): XKR4 RP1 ... MT-CYB PRAMEF15-26\n",
      "rowData names(0):\n",
      "colnames(10839): Men_naive_AAACCCATCATCTGTT-1\n",
      "  Men_naive_AAACGAAGTCATACCA-1 ... VSV_HUB_TTTGTTGTCAATCCAG-1\n",
      "  VSV_HUB_TTTGTTGTCTCAACGA-1\n",
      "colData names(6): sample_id condition ... celltype cpdb_celltype\n",
      "reducedDimNames(2): PCA UMAP\n",
      "altExpNames(0):\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "sce <- cbind(adata1, adata2)\n",
    "sce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "saveRDS(sce, 'h5ad/meninges_integrated_naive_vsv_cpdb.RDS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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