{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e90d448e-8b8a-408c-859f-bf4e2aa930d5",
   "metadata": {},
   "source": [
    "# B cell annotations\n",
    "\n",
    "In this notebook, we upload cell type annotations assigned by our domain experts. These annotations were generated using markers and by interactive investigation of our .h5ad files.\n",
    "\n",
    "As these are derived from our .h5ad files, we'll connect them to the HISE analysis chain by chaching those files and using `hisepy.upload.upload_files()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d61a2467-035c-45e2-b0a8-c94523406816",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hisepy\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6119e2-df2a-4fed-ad82-3248b0ed3a4d",
   "metadata": {},
   "source": [
    "For B cells, this was done in two steps - all B cells that were clustered after Immunoglobulin removal, and with subclustering of non-effector memory B cells.\n",
    "\n",
    "Here, we'll combine these steps to build the link in our reproducibility chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8123c57a-c51c-4832-bc30-c7ef787cacc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading fileID: 99f83994-26ee-49af-a882-c1f2558daed2\n",
      "downloading fileID: 51838754-b378-4f13-b447-82511bcd0a66\n",
      "Files have been successfully downloaded!\n"
     ]
    }
   ],
   "source": [
    "in_files = [\n",
    "    '99f83994-26ee-49af-a882-c1f2558daed2', # B cell .h5ad\n",
    "    '51838754-b378-4f13-b447-82511bcd0a66'  # B Memory cell focus .h5ad\n",
    "]\n",
    "hise_res = hisepy.reader.cache_files(in_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eed57930-debb-40c4-a201-b787af10520f",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_files = [\n",
    "    'annotations/pbmc_ref_b-cells-no-ig_annotations_2024-03-08.csv',\n",
    "    'annotations/pbmc_ref_b-cells-mem-no-ig_annotations_2024-03-08.csv'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c94c9511-5758-49aa-9171-95ea152ad99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "study_space_uuid = '64097865-486d-43b3-8f94-74994e0a72e0'\n",
    "title = 'Ref. B cell annotations {d}'.format(d = date.today())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b30714da-90d4-4ecd-9d6b-053e1545b070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annotations/pbmc_ref_b-cells-no-ig_annotations_2024-03-08.csv\n",
      "annotations/pbmc_ref_b-cells-mem-no-ig_annotations_2024-03-08.csv\n",
      "Cannot determine the current notebook.\n",
      "1) /home/jupyter/scRNA-Reference-IH-A/04-Annotation/12-Python_B_cell_annotations.ipynb\n",
      "2) /home/jupyter/scRNA-Reference-IH-A/visualizations/04-Python_other_markers.ipynb\n",
      "3) /home/jupyter/scRNA-Reference-IH-A/visualizations/03-Python_T_markers.ipynb\n",
      "Please select (1-3) \n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "you are trying to upload file_ids... ['annotations/pbmc_ref_b-cells-no-ig_annotations_2024-03-08.csv', 'annotations/pbmc_ref_b-cells-mem-no-ig_annotations_2024-03-08.csv']. Do you truly want to proceed?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "(y/n) y\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'trace_id': '9dae5780-00b0-47c4-b148-ee2f2868bd9e',\n",
       " 'files': ['annotations/pbmc_ref_b-cells-no-ig_annotations_2024-03-08.csv',\n",
       "  'annotations/pbmc_ref_b-cells-mem-no-ig_annotations_2024-03-08.csv']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hisepy.upload.upload_files(\n",
    "    files = out_files,\n",
    "    study_space_id = study_space_uuid,\n",
    "    title = title,\n",
    "    input_file_ids = in_files\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae5ad1de-b624-4727-a4bd-2a3bc3d6e313",
   "metadata": {},
   "outputs": [
    {
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       "<details>\n",
       "<summary>Click to view session information</summary>\n",
       "<pre>\n",
       "-----\n",
       "hisepy              0.3.0\n",
       "session_info        1.0.0\n",
       "-----\n",
       "</pre>\n",
       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
       "<pre>\n",
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       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
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       "-----\n",
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       "notebook            6.5.4\n",
       "-----\n",
       "Python 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0]\n",
       "Linux-5.15.0-1052-gcp-x86_64-with-glibc2.31\n",
       "-----\n",
       "Session information updated at 2024-03-09 04:56\n",
       "</pre>\n",
       "</details>"
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     },
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   "source": [
    "import session_info\n",
    "session_info.show()"
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  {
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   "execution_count": null,
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