\n", "Transcription factor gene list: a list of transcription factors of interest. This list is requried for the network inference step. Data can be downloaded through pySCENIC_TF_list and TF_lists.\n", "\n", "Ranked whole genome databases: Databases ranking the whole genome of species of interest based on transcription factors in feather v2 format. Data can typically be downloaded from public databases, through cisTarget databases.\n", "\n", "Motif to TF annotation databases: Motif databases (in tbl format) serve as a reference for identifying potential TF binding sites in the genome and inferring the regulatory relationships between TFs and target genes. Data can be downloaded through Motif2TF annotations." ] }, { "cell_type": "code", "execution_count": 10, "id": "313fb32b", "metadata": {}, "outputs": [], "source": [ "tfs_fn = 'mouse_tfs.txt'\n", "database_fn = 'mm10_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather'\n", "motif_anno_fn = 'motifs-v10nr_clust-nr.mgi-m0.001-o0.0.tbl'" ] }, { "cell_type": "markdown", "id": "3af8f5d7", "metadata": {}, "source": [ "### 2. Create a spagrn object\n", "pos_label is the obsm key where spots coordinates were stored." ] }, { "cell_type": "code", "execution_count": 11, "id": "0ff9b0dd", "metadata": {}, "outputs": [], "source": [ "grn = irn(data)" ] }, { "cell_type": "markdown", "id": "8d3205a6", "metadata": {}, "source": [ "[optional] set parameters..." ] }, { "cell_type": "code", "execution_count": 12, "id": "13f83012", "metadata": {}, "outputs": [], "source": [ "grn.add_params({'prune_auc_threshold': 0.05, 'rank_threshold': 9000, 'auc_threshold': 0.05})" ] }, { "cell_type": "markdown", "id": "abcde939", "metadata": {}, "source": [ "### 3. Set Ligand-Receptor data\n", "this ..." ] }, { "cell_type": "code", "execution_count": 13, "id": "1d23696d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " from to database source\n", "0 2300002M23Rik Ddr1 omnipath omnipath\n", "1 2610528A11Rik Gpr15 omnipath omnipath\n", "2 9530003J23Rik Itgal omnipath omnipath\n", "3 a Atrn omnipath omnipath\n", "4 a F11r omnipath omnipath\n", "... ... ... ... ...\n", "4981 ZNRF3 FZD4 omnipath omnipath\n", "4982 ZP3 EGFR omnipath omnipath\n", "4983 ZP3 CHRNA7 omnipath omnipath\n", "4984 ZP3 MERTK omnipath omnipath\n", "4985 ZPBP2 CD80 omnipath omnipath\n", "\n", "[10654 rows x 4 columns]\n" ] } ], "source": [ "niche_human = pd.read_csv('lr_network_human.csv')\n", "niche_mouse = pd.read_csv('lr_network_mouse.csv')\n", "# concat the databases together if you are using multiple ligand-receptors databases\n", "niches = pd.concat([niche_mouse, niche_human])\n", "print(niches)" ] }, { "cell_type": "markdown", "id": "c452c883", "metadata": {}, "source": [ "### 4. Construct the network" ] }, { "cell_type": "code", "execution_count": 17, "id": "32df5bdc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████| 3235/3235 [00:11<00:00, 280.82it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing spatial weights matrix...\n", "Computing Moran's I...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", "/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/site-packages/geopandas/_compat.py:124: UserWarning: The Shapely GEOS version (3.11.4-CAPI-1.17.4) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", " warnings.warn(\n", "Process SpawnPoolWorker-5:\n", "Traceback (most recent call last):\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n", " self.run()\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/multiprocessing/process.py\", line 108, in run\n", " self._target(*self._args, **self._kwargs)\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/multiprocessing/pool.py\", line 125, in worker\n", " result = (True, func(*args, **kwds))\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/multiprocessing/pool.py\", line 48, in mapstar\n", " return list(map(*args))\n", " File \"/Users/Oreo/Library/CloudStorage/OneDrive-BGIHongKongTechCo.,Limited/PycharmProjects/SpaGRN/spagrn/m_autocor.py\", line 71, in _compute_i_for_gene\n", " i = Moran(x, w)\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/site-packages/esda/moran.py\", line 182, in __init__\n", " sim = [\n", " File \"/Users/Oreo/anaconda3/envs/spagrn/lib/python3.8/site-packages/esda/moran.py\", line 183, in
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Atf2(+) Atf4(+) Bcl11a(+) Bcl6(+) Bnc2(+) Cebpb(+) \\\n",
"Cell \n",
"Cell_1 0.156431 0.111644 0.098457 0.000000 0.033453 0.085540 \n",
"Cell_1000 0.212292 0.218080 0.164389 0.053236 0.056976 0.215377 \n",
"Cell_10000 0.111001 0.060451 0.075737 0.000000 0.032233 0.107542 \n",
"Cell_10001 0.081370 0.148219 0.101323 0.086841 0.028692 0.177124 \n",
"Cell_10002 0.139363 0.371802 0.186718 0.074740 0.000000 0.286048 \n",
"... ... ... ... ... ... ... \n",
"Cell_9994 0.081529 0.192922 0.121566 0.000000 0.011434 0.107356 \n",
"Cell_9996 0.253249 0.273179 0.174062 0.058310 0.078524 0.259639 \n",
"Cell_9997 0.190197 0.204892 0.185552 0.089932 0.016441 0.182882 \n",
"Cell_9998 0.240043 0.216039 0.157211 0.226659 0.027269 0.211629 \n",
"Cell_9999 0.143029 0.303464 0.129246 0.171726 0.023894 0.087445 \n",
"\n",
" Cebpd(+) Dbp(+) Dlx1(+) Ebf1(+) ... Satb2(+) Sox10(+) \\\n",
"Cell ... \n",
"Cell_1 0.000000 0.170651 0.122474 0.000000 ... 0.099124 0.030581 \n",
"Cell_1000 0.060005 0.180853 0.053370 0.000000 ... 0.124200 0.023578 \n",
"Cell_10000 0.000000 0.134629 0.040831 0.000000 ... 0.079151 0.021887 \n",
"Cell_10001 0.000000 0.187842 0.034786 0.000000 ... 0.062423 0.040046 \n",
"Cell_10002 0.000000 0.324644 0.075673 0.075543 ... 0.104446 0.040820 \n",
"... ... ... ... ... ... ... ... \n",
"Cell_9994 0.000000 0.079788 0.091360 0.000000 ... 0.072796 0.048270 \n",
"Cell_9996 0.000000 0.178899 0.204684 0.163190 ... 0.140081 0.041906 \n",
"Cell_9997 0.052197 0.216455 0.096070 0.000000 ... 0.139947 0.031018 \n",
"Cell_9998 0.000000 0.173408 0.153566 0.083944 ... 0.146612 0.053716 \n",
"Cell_9999 0.000000 0.420941 0.067038 0.000000 ... 0.073910 0.077494 \n",
"\n",
" Sox9(+) Srebf1(+) Tbx18(+) Tcf4(+) Tcf7l2(+) Thra(+) \\\n",
"Cell \n",
"Cell_1 0.043982 0.108448 0.000000 0.282513 0.121834 0.123972 \n",
"Cell_1000 0.023990 0.059154 0.000000 0.368584 0.047002 0.218843 \n",
"Cell_10000 0.069703 0.349720 0.000000 0.320939 0.076905 0.085574 \n",
"Cell_10001 0.135491 0.336981 0.000000 0.172192 0.035783 0.144633 \n",
"Cell_10002 0.137917 0.359795 0.000000 0.311838 0.066502 0.279441 \n",
"... ... ... ... ... ... ... \n",
"Cell_9994 0.059478 0.116394 0.000000 0.124870 0.011098 0.116962 \n",
"Cell_9996 0.123377 0.315487 0.000000 0.347189 0.114236 0.220211 \n",
"Cell_9997 0.139405 0.583368 0.000000 0.314701 0.095673 0.242292 \n",
"Cell_9998 0.107629 0.365332 0.000000 0.579174 0.114361 0.212512 \n",
"Cell_9999 0.173375 0.530861 0.018987 0.336947 0.084933 0.217689 \n",
"\n",
" Thrb(+) Vsx1(+) \n",
"Cell \n",
"Cell_1 0.103947 0.0 \n",
"Cell_1000 0.112490 0.0 \n",
"Cell_10000 0.074182 0.0 \n",
"Cell_10001 0.052906 0.0 \n",
"Cell_10002 0.090039 0.0 \n",
"... ... ... \n",
"Cell_9994 0.060993 0.0 \n",
"Cell_9996 0.146768 0.0 \n",
"Cell_9997 0.116345 0.0 \n",
"Cell_9998 0.162407 0.0 \n",
"Cell_9999 0.068903 0.0 \n",
"\n",
"[50140 rows x 63 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata.obsm['auc_mtx']"
]
},
{
"cell_type": "markdown",
"id": "e52be4bd",
"metadata": {},
"source": [
"## Part III. Visualization of the network"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "13c85f0e-9ea0-44a0-a605-7fa0ca83831f",
"metadata": {},
"outputs": [],
"source": [
"import spagrn.plot as prn"
]
},
{
"cell_type": "markdown",
"id": "c3587377",
"metadata": {},
"source": [
"### Regulon 2D spatially distribution plot\n",
"`plot_2d` plots the spatial distribution of the regulon activity level, with each point representing a spatial location and the color indicating the ISR value of the corresponding regulon."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "d265482b",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"prn.plot_2d(adata, adata.obsm['auc_mtx'], pos_label='spatial', reg_name='Pbx3', fn='Pbx3.png')"
]
},
{
"cell_type": "markdown",
"id": "133b72fc-9ac3-4d03-810c-d9737c9bc43a",
"metadata": {},
"source": [
"| \n", " | Atf2(+) | \n", "Atf4(+) | \n", "Bcl11a(+) | \n", "Bcl6(+) | \n", "Bnc2(+) | \n", "Cebpb(+) | \n", "Cebpd(+) | \n", "Dbp(+) | \n", "Dlx1(+) | \n", "Ebf1(+) | \n", "... | \n", "Satb2(+) | \n", "Sox10(+) | \n", "Sox9(+) | \n", "Srebf1(+) | \n", "Tbx18(+) | \n", "Tcf4(+) | \n", "Tcf7l2(+) | \n", "Thra(+) | \n", "Thrb(+) | \n", "Vsx1(+) | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cell | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
| Cell_1 | \n", "0.156431 | \n", "0.111644 | \n", "0.098457 | \n", "0.000000 | \n", "0.033453 | \n", "0.085540 | \n", "0.000000 | \n", "0.170651 | \n", "0.122474 | \n", "0.000000 | \n", "... | \n", "0.099124 | \n", "0.030581 | \n", "0.043982 | \n", "0.108448 | \n", "0.000000 | \n", "0.282513 | \n", "0.121834 | \n", "0.123972 | \n", "0.103947 | \n", "0.0 | \n", "
| Cell_1000 | \n", "0.212292 | \n", "0.218080 | \n", "0.164389 | \n", "0.053236 | \n", "0.056976 | \n", "0.215377 | \n", "0.060005 | \n", "0.180853 | \n", "0.053370 | \n", "0.000000 | \n", "... | \n", "0.124200 | \n", "0.023578 | \n", "0.023990 | \n", "0.059154 | \n", "0.000000 | \n", "0.368584 | \n", "0.047002 | \n", "0.218843 | \n", "0.112490 | \n", "0.0 | \n", "
| Cell_10000 | \n", "0.111001 | \n", "0.060451 | \n", "0.075737 | \n", "0.000000 | \n", "0.032233 | \n", "0.107542 | \n", "0.000000 | \n", "0.134629 | \n", "0.040831 | \n", "0.000000 | \n", "... | \n", "0.079151 | \n", "0.021887 | \n", "0.069703 | \n", "0.349720 | \n", "0.000000 | \n", "0.320939 | \n", "0.076905 | \n", "0.085574 | \n", "0.074182 | \n", "0.0 | \n", "
| Cell_10001 | \n", "0.081370 | \n", "0.148219 | \n", "0.101323 | \n", "0.086841 | \n", "0.028692 | \n", "0.177124 | \n", "0.000000 | \n", "0.187842 | \n", "0.034786 | \n", "0.000000 | \n", "... | \n", "0.062423 | \n", "0.040046 | \n", "0.135491 | \n", "0.336981 | \n", "0.000000 | \n", "0.172192 | \n", "0.035783 | \n", "0.144633 | \n", "0.052906 | \n", "0.0 | \n", "
| Cell_10002 | \n", "0.139363 | \n", "0.371802 | \n", "0.186718 | \n", "0.074740 | \n", "0.000000 | \n", "0.286048 | \n", "0.000000 | \n", "0.324644 | \n", "0.075673 | \n", "0.075543 | \n", "... | \n", "0.104446 | \n", "0.040820 | \n", "0.137917 | \n", "0.359795 | \n", "0.000000 | \n", "0.311838 | \n", "0.066502 | \n", "0.279441 | \n", "0.090039 | \n", "0.0 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| Cell_9994 | \n", "0.081529 | \n", "0.192922 | \n", "0.121566 | \n", "0.000000 | \n", "0.011434 | \n", "0.107356 | \n", "0.000000 | \n", "0.079788 | \n", "0.091360 | \n", "0.000000 | \n", "... | \n", "0.072796 | \n", "0.048270 | \n", "0.059478 | \n", "0.116394 | \n", "0.000000 | \n", "0.124870 | \n", "0.011098 | \n", "0.116962 | \n", "0.060993 | \n", "0.0 | \n", "
| Cell_9996 | \n", "0.253249 | \n", "0.273179 | \n", "0.174062 | \n", "0.058310 | \n", "0.078524 | \n", "0.259639 | \n", "0.000000 | \n", "0.178899 | \n", "0.204684 | \n", "0.163190 | \n", "... | \n", "0.140081 | \n", "0.041906 | \n", "0.123377 | \n", "0.315487 | \n", "0.000000 | \n", "0.347189 | \n", "0.114236 | \n", "0.220211 | \n", "0.146768 | \n", "0.0 | \n", "
| Cell_9997 | \n", "0.190197 | \n", "0.204892 | \n", "0.185552 | \n", "0.089932 | \n", "0.016441 | \n", "0.182882 | \n", "0.052197 | \n", "0.216455 | \n", "0.096070 | \n", "0.000000 | \n", "... | \n", "0.139947 | \n", "0.031018 | \n", "0.139405 | \n", "0.583368 | \n", "0.000000 | \n", "0.314701 | \n", "0.095673 | \n", "0.242292 | \n", "0.116345 | \n", "0.0 | \n", "
| Cell_9998 | \n", "0.240043 | \n", "0.216039 | \n", "0.157211 | \n", "0.226659 | \n", "0.027269 | \n", "0.211629 | \n", "0.000000 | \n", "0.173408 | \n", "0.153566 | \n", "0.083944 | \n", "... | \n", "0.146612 | \n", "0.053716 | \n", "0.107629 | \n", "0.365332 | \n", "0.000000 | \n", "0.579174 | \n", "0.114361 | \n", "0.212512 | \n", "0.162407 | \n", "0.0 | \n", "
| Cell_9999 | \n", "0.143029 | \n", "0.303464 | \n", "0.129246 | \n", "0.171726 | \n", "0.023894 | \n", "0.087445 | \n", "0.000000 | \n", "0.420941 | \n", "0.067038 | \n", "0.000000 | \n", "... | \n", "0.073910 | \n", "0.077494 | \n", "0.173375 | \n", "0.530861 | \n", "0.018987 | \n", "0.336947 | \n", "0.084933 | \n", "0.217689 | \n", "0.068903 | \n", "0.0 | \n", "
50140 rows × 63 columns
\n", "![](\"Pbx3.png\")
"
]
},
{
"cell_type": "markdown",
"id": "29a499b8",
"metadata": {},
"source": [
"### Heatmaps\n",
"`auc_heatmap` visualize the activity levels of regulons across cells, allowing for easy comparison and identification of trends or patterns. By clustering the cells based on regulon activities, one can see whether there are groups of cells that tend to have the same regulons active, and reveal the network states that are recurrent across multiple cells.\n",
"#### 1. regular heatmap"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1c504d4",
"metadata": {},
"outputs": [],
"source": [
"prn.auc_heatmap(data,\n",
" auc_mtx,\n",
" cluster_label='annotation',\n",
" topn=10,\n",
" subset=False,\n",
" save=True,\n",
" fn=f'{out_dir}/{method}_clusters_heatmap_top10.png',\n",
" legend_fn=f\"{out_dir}/{method}_rss_celltype_legend_top10.png\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.12"
}
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"nbformat": 4,
"nbformat_minor": 5
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