h5ad_to_seurat.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "d94f83ba",
   "metadata": {},
   "source": [
    "My opinion: learn to use scanpy instead\n",
    "\n",
    "scanpy is great and integrates with machine learning very nicely, eg scVI tools, scArches, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1457418",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "from scipy import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8cff35b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir matrix_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "480f32c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_h5ad('TS_Heart.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4564bb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = adata.raw.to_adata()  #only if adata has RAW saved and thats what you want!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb07b27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('matrix_files/barcodes.tsv', 'w') as f:\n",
    "    for item in adata.obs_names:\n",
    "        f.write(item + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f64c503",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('matrix_files/features.tsv', 'w') as f:\n",
    "    for item in ['\\t'.join([x,x,'Gene Expression']) for x in adata.var_names]:\n",
    "        f.write(item + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f375b043",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.mmwrite('matrix_files/matrix', adata.X.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbee4699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "barcodes.tsv  features.tsv  matrix.mtx\r\n"
     ]
    }
   ],
   "source": [
    "!ls matrix_files/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8417f9fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "!gzip matrix_files/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4891515a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "barcodes.tsv.gz  features.tsv.gz  matrix.mtx.gz\r\n"
     ]
    }
   ],
   "source": [
    "!ls matrix_files/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0afd24f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs.to_csv('metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9ec92fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#example script\n",
    "#to run:\n",
    "#mkdir <out_dir>\n",
    "#python name_of_this_script.py <adata_path> <out_dir>\n",
    "#gzip <out_dir>/*\n",
    "import scanpy as sc\n",
    "from scipy import io\n",
    "import sys\n",
    "\n",
    "adata = sc.read_h5ad(sys.argv[1])\n",
    "out_dir = sys.argv[2]\n",
    "\n",
    "adata = adata.raw.to_adata() #only if adata has RAW saved and thats what you want!!\n",
    "\n",
    "with open(out_dir + '/barcodes.tsv', 'w') as f:\n",
    "    for item in adata.obs_names:\n",
    "        f.write(item + '\\n')\n",
    "        \n",
    "with open(out_dir + '/features.tsv', 'w') as f:\n",
    "    for item in ['\\t'.join([x,x,'Gene Expression']) for x in adata.var_names]:\n",
    "        f.write(item + '\\n')\n",
    "        \n",
    "io.mmwrite(out_dir +'/matrix', adata.X.T)\n",
    "\n",
    "adata.obs.to_csv(sys.argv[1] + '.metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bbf5690",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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