{
"cells": [
{
"cell_type": "markdown",
"id": "b6dec3da",
"metadata": {},
"source": [
"# Neuronal Cell Painting\n",
"\n",
"{func}`~mantispy.ds.neuropainting` serves `cpg0038-tegtmeyer-neuropainting`, iPSC-derived neurons and astrocytes imaged at 20x and 63x. It is the example with no perturbation reagent: the design factor is genotype and donor, a control-versus-deletion contrast over patient and isogenic lines, rather than a compound or a guide. The object is well-level.\n",
"\n",
"**Use it if** your own experiment has no perturbation reagent and the contrast is a design factor (genotype, donor, cell type), so you group with an explicit `groupby=` rather than by a perturbation column. No tutorial is built on it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "58acd0b8",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T00:59:47.777796Z",
"iopub.status.busy": "2026-10-21T00:59:47.777430Z",
"iopub.status.idle": "2026-10-21T00:59:51.395880Z",
"shell.execute_reply": "2026-10-21T00:59:51.394102Z"
}
},
"outputs": [],
"source": [
"import mantispy as mt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bd9703f6",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T00:59:51.400279Z",
"iopub.status.busy": "2026-10-21T00:59:51.400013Z",
"iopub.status.idle": "2026-10-21T00:59:51.549558Z",
"shell.execute_reply": "2026-10-21T00:59:51.548425Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 1691 × 204\n",
" obs: 'Metadata_plate_map_name', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site_Count', 'Metadata_Object_Count', 'Metadata_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_SiteCount'\n",
" var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind'\n",
" uns: 'mantispy'\n",
" layers: None (.X)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata = mt.ds.neuropainting()\n",
"adata"
]
},
{
"cell_type": "markdown",
"id": "dfa056a7",
"metadata": {},
"source": [
"## No `Metadata_Perturbation`\n",
"\n",
"Because the contrast is genotype and donor, and those columns are spelled differently on each plate, the per-plate column intersect keeps none of them, so no `Metadata_Perturbation` is set. Group with an explicit `groupby=` on the column an analysis needs. One plate barcode appears in more than one batch, so the observations are not indexed by plate and well either."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "26106ff3",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T00:59:51.553149Z",
"iopub.status.busy": "2026-10-21T00:59:51.552990Z",
"iopub.status.idle": "2026-10-21T00:59:51.556978Z",
"shell.execute_reply": "2026-10-21T00:59:51.555960Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['Metadata_CellCount',\n",
" 'Metadata_Col',\n",
" 'Metadata_Object_Count',\n",
" 'Metadata_Plate',\n",
" 'Metadata_Row',\n",
" 'Metadata_SiteCount',\n",
" 'Metadata_Site_Count',\n",
" 'Metadata_Well',\n",
" 'Metadata_plate_map_name']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(c for c in adata.obs.columns if c.startswith(\"Metadata_\"))"
]
},
{
"cell_type": "markdown",
"id": "8079b342",
"metadata": {},
"source": [
"## Cells per well by plate\n",
"\n",
"The plates differ in magnification and cell type, so cells per well spreads widely between them."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "97e269d5",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T00:59:51.560135Z",
"iopub.status.busy": "2026-10-21T00:59:51.559959Z",
"iopub.status.idle": "2026-10-21T00:59:52.456369Z",
"shell.execute_reply": "2026-10-21T00:59:52.455425Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mt.pl.cell_counts(adata, groupby=\"Metadata_Plate\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.13.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}