Fix/notebooks#83

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melonora wants to merge 6 commits into from
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Fix/notebooks #83

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b2236aa
fixed densenet and query notebooks
Mar 21, 2024
431b0cf
fixed aggregation notebook
Mar 21, 2024
fc54769
fixed alignment using landmarks
Mar 21, 2024
1883979
Merge branch 'main' into fix/notebooks
Mar 21, 2024
5611fb9
fixed alignment notebook
Mar 21, 2024
fdbae15
wip visium xenium
Mar 21, 2024
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349 changes: 222 additions & 127 deletions notebooks/examples/aggregation.ipynb
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70 changes: 41 additions & 29 deletions notebooks/examples/alignment_using_landmarks.ipynb
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Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
"/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
" warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
]
},
Expand All@@ -88,19 +88,14 @@
"├── Shapes\n",
"│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
"│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
"│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
"│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
"└── Table\n",
" └── AnnData object with n_obs × n_vars = 167780 × 313\n",
" obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
" var: 'gene_ids', 'feature_types', 'genome'\n",
" uns: 'spatialdata_attrs'\n",
" obsm: 'spatial': AnnData (167780, 313)\n",
"│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
"└── Tables\n",
" └── 'table': AnnData (167780, 313)\n",
"with coordinate systems:\n",
"▸ 'aligned', with elements:\n",
" morphology_mip (Images)\n",
"▸ 'global', with elements:\n",
" morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
" morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
]
},
"execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
"│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
"│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
"│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
"├── Points\n",
"│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
"│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
"├── Shapes\n",
"│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
"│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
"└── Table\n",
" └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
" obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
" var: 'gene_ids', 'feature_types', 'genome'\n",
" uns: 'spatial', 'spatialdata_attrs'\n",
" obsm: 'spatial': AnnData (4992, 18085)\n",
"│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
"└── Tables\n",
" └── 'table': AnnData (4992, 18085)\n",
"with coordinate systems:\n",
"▸ 'aligned', with elements:\n",
" CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
" CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
"▸ 'downscaled_hires', with elements:\n",
" CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
"▸ 'downscaled_lowres', with elements:\n",
" CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
"▸ 'global', with elements:\n",
" CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
" CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
]
},
"execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
"end_time": "2023-04-10T18:59:26.909684Z",
"start_time": "2023-04-10T18:59:25.642148Z"
},
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
"id": "e71718a2",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"tags": []
},
"outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
"id": "be9277db",
"metadata": {},
"source": [
"### Saving the alignment back to Zarr\n"
"### Saving the landmarks and the alignment back to Zarr\n"
]
},
{
"cell_type": "markdown",
"id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
"metadata": {},
"source": [
"We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
"We will now save the landmark points and the transformations of the other elements to disk. \n",
"\n",
"Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
]
},
{
"cell_type": "markdown",
"id": "a30ee131-40a4-44a2-b736-763532cf570e",
"metadata": {},
"source": [
"WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"id": "474410bd-2d02-45c1-b073-eba1152ab615",
"metadata": {
"tags": []
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
"\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
]
}
],
"source": [
"from spatialdata import save_transformations\n",
"\n",
Expand All@@ -541,7 +553,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.10.13"
},
"vscode": {
"interpreter": {
Expand Down
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
Fix/notebooks#83

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to your account

 Closed

melonora
wants to merge
6
commits into

from

 Closed


Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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Large diffs are not rendered by default.


70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Fix/notebooks#83

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

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 Closed

melonora
wants to merge
6
commits into

from

 Closed


Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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Large diffs are not rendered by default.


70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix/notebooks#83

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melonora
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Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

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Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Fix/notebooks#83

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 Closed

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Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix/notebooks#83

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 Closed

melonora
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Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

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Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix/notebooks#83

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 Closed

melonora
wants to merge
6
commits into

from

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Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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})();
})();

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Fix/notebooks
#83
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b2236aa

fixed densenet and query notebooks

Mar 21, 2024
431b0cf

fixed aggregation notebook

Mar 21, 2024
fc54769

fixed alignment using landmarks

Mar 21, 2024
1883979

Merge branch 'main' into fix/notebooks

Mar 21, 2024
5611fb9

fixed alignment notebook

Mar 21, 2024
fdbae15

wip visium xenium

Mar 21, 2024

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349 changes: 222 additions & 127 deletions
notebooks/examples/aggregation.ipynb

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70 changes: 41 additions & 29 deletions
notebooks/examples/alignment_using_landmarks.ipynb

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Original file line numberDiff line numberDiff line change
Expand Up@@ -72,7 +72,7 @@
 "name": "stderr",
 "output_type": "stream",
 "text": [
 "/Users/macbook/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
 "/mnt/miniconda3/envs/ome/lib/python3.10/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.\n",
 " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
 ]
 },
Expand All@@ -88,19 +88,14 @@
 "├── Shapes\n",
 "│ ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)\n",
 "│ ├── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "│ └── 'xenium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 167780 × 313\n",
 " obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (167780, 313)\n",
 "│ └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (167780, 313)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " morphology_mip (Images)\n",
 "▸ 'global', with elements:\n",
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes), xenium_landmarks (Shapes)"
 " morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)"
 ]
 },
 "execution_count": 2,
Expand DownExpand Up@@ -133,27 +128,19 @@
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': MultiscaleSpatialImage[cyx] (3, 21571, 19505), (3, 10785, 9752), (3, 5392, 4876), (3, 2696, 2438), (3, 1348, 1219)\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': SpatialImage[cyx] (3, 2000, 1809)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': SpatialImage[cyx] (3, 600, 543)\n",
 "├── Points\n",
 "│ ├── 'Points': DataFrame with shape: (3, 2) (2D points)\n",
 "│ └── 'Points_1': DataFrame with shape: (1, 2) (2D points)\n",
 "├── Shapes\n",
 "│ ├── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "│ └── 'visium_landmarks': GeoDataFrame shape: (3, 2) (2D shapes)\n",
 "└── Table\n",
 " └── AnnData object with n_obs × n_vars = 4992 × 18085\n",
 " obs: 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'\n",
 " var: 'gene_ids', 'feature_types', 'genome'\n",
 " uns: 'spatial', 'spatialdata_attrs'\n",
 " obsm: 'spatial': AnnData (4992, 18085)\n",
 "│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (4992, 2) (2D shapes)\n",
 "└── Tables\n",
 " └── 'table': AnnData (4992, 18085)\n",
 "with coordinate systems:\n",
 "▸ 'aligned', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), Points (Points), Points_1 (Points), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_hires', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'downscaled_lowres', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)\n",
 "▸ 'global', with elements:\n",
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes), visium_landmarks (Shapes)"
 " CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)"
 ]
 },
 "execution_count": 3,
Expand DownExpand Up@@ -184,7 +171,10 @@
 "end_time": "2023-04-10T18:59:26.909684Z",
 "start_time": "2023-04-10T18:59:25.642148Z"
 },
 "collapsed": false
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 }
 },
 "source": [
 "Interactive([visium_sdata, xenium_sdata], points=False, shapes=False)"
Expand DownExpand Up@@ -335,6 +325,9 @@
 "id": "e71718a2",
 "metadata": {
 "collapsed": false,
 "jupyter": {
 "outputs_hidden": false
 },
 "tags": []
 },
 "outputs": [
Expand DownExpand Up@@ -498,25 +491,44 @@
 "id": "be9277db",
 "metadata": {},
 "source": [
 "### Saving the alignment back to Zarr\n"
 "### Saving the landmarks and the alignment back to Zarr\n"
 ]
 },
 {
 "cell_type": "markdown",
 "id": "2e98374c-85cd-45ad-ac62-b2bc075c9631",
 "metadata": {},
 "source": [
 "We will now save the transformations to disk. Notice that this is a lightweight operation because we are just mofiying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 "We will now save the landmark points and the transformations of the other elements to disk. \n",
 "\n",
 "Notice that these are both lightweight operations because the two sets of landmark points are small, and when saving the transformation of the other elements we are modifying the objects metadata, not transforming the actual data. This is useful when dealing with large images and when one may need to reiterate multiple steps of landmark-based alignment in order to improve the spatial agreement of the alignment."
 ]
 },
 {
 "cell_type": "markdown",
 "id": "a30ee131-40a4-44a2-b736-763532cf570e",
 "metadata": {},
 "source": [
 "WARNING: unfortunately the modular saving of transformation and elements have been refactored out of the latest release and is still not finalized. This function will be re-enabled with high priority, please see the issue tracker here: https://github.com/scverse/spatialdata/issues/496."
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "execution_count": 10,
 "id": "474410bd-2d02-45c1-b073-eba1152ab615",
 "metadata": {
 "tags": []
 },
 "outputs": [],
 "outputs": [
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/visium_landmarks as it is not found in Zarr storage \n",
 "\u001b[34mINFO \u001b[0m Not saving the transformation to element shapes/xenium_landmarks as it is not found in Zarr storage \n"
 ]
 }
 ],
 "source": [
 "from spatialdata import save_transformations\n",
 "\n",
Expand All@@ -541,7 +553,7 @@
 "name": "python",
 "nbconvert_exporter": "python",
 "pygments_lexer": "ipython3",
 "version": "3.10.12"
 "version": "3.10.13"
 },
 "vscode": {
 "interpreter": {
Expand Down
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