diff --git a/figures/scprint_v2_paper.ipynb b/figures/scprint_v2_paper.ipynb index 77350c0..0b09863 100644 --- a/figures/scprint_v2_paper.ipynb +++ b/figures/scprint_v2_paper.ipynb @@ -1,5 +1,19 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# scPRINT-2 Dataset Composition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook generates summary figures for the scPRINT-2 dataset, including the distribution of tissues, species, and assay types." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -57,7 +71,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.express as px\n", + "import plotly.io as pio\n", + "import pandas as pd \n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,69 +106,62 @@ "'other (17 assays)': 1423506,\n", "}\n", "species = {\n", - " 'human': 230822337,\n", - " 'mouse': 102753838,\n", + " 'human - 66.39%': 230822337,\n", + " 'mouse - 29.56%': 102753838,\n", " # other 14070000\n", - " 'macaque': 3161179,\n", - " 'zebrafish': 3117237,\n", - " 'pig': 1641179,\n", - " 'thale cress': 1564711,\n", - " 'drosophila': 1564460,\n", - " 'chicken': 606680,\n", - " 'nake mole rat': 441042,\n", - " 'rabbit': 417091,\n", - " 'cow': 390675,\n", - " 'corn': 336166,\n", - " 'chimpanzee': 293472,\n", - " 'c. elegans': 251759,\n", - " 'sheep': 177871,\n", - " 'marmoset': 115584,\n", + " 'macaque - 0.91%': 3161179,\n", + " 'zebrafish - 0.90%': 3117237,\n", + " 'pig - 0.47%': 1641179,\n", + " 'thale cress - 0.45%': 1564711,\n", + " 'drosophila - 0.45%': 1564460,\n", + " 'chicken - 0.17%': 606680,\n", + " 'nake mole rat - 0.13%': 441042,\n", + " 'rabbit - 0.12%': 417091,\n", + " 'cow - 0.11%': 390675,\n", + " 'corn - 0.10%': 336166,\n", + " 'chimpanzee - 0.08%': 293472,\n", + " 'c. elegans - 0.07%': 251759,\n", + " 'sheep - 0.05%': 177871,\n", + " 'marmoset - 0.03%': 115584,\n", "}\n", "\n", "tissues = {\n", - " 'lung': 42877488,\n", - " 'blood': 34180713,\n", - " 'brain': 29530595,\n", - " 'colon': 25830811,\n", - " 'unknown': 23810521,\n", - " 'pancreas': 21597602,\n", - " 'embryo': 16976623,\n", - " 'skin': 12513695,\n", - " 'liver': 10683313,\n", - " 'breast': 10539762,\n", - " 'marrow': 9543512,\n", - " 'kidney': 8213043,\n", - " 'heart': 4800567,\n", - " 'immune system': 4687291,\n", - " 'eye': 4677504,\n", - " 'other (310 tissues)': 87200000\n", + " 'lung - 12.30%': 42877488,\n", + " 'blood - 9.83%': 34180713,\n", + " 'brain - 8.49%': 29530595,\n", + " 'colon - 7.43%': 25830811,\n", + " 'unknown - 6.85%': 23810521,\n", + " 'pancreas - 6.21%': 21597602,\n", + " 'embryo - 4.88%': 16976623,\n", + " 'skin - 3.60%': 12513695,\n", + " 'liver - 3.07%': 10683313,\n", + " 'breast - 3.03%': 10539762,\n", + " 'marrow - 2.75%': 9543512,\n", + " 'kidney - 2.36%': 8213043,\n", + " 'heart - 1.38%': 4800567,\n", + " 'immune system - 1.35%': 4687291,\n", + " 'eye - 1.35%': 4677504,\n", + " 'other (310 tissues) - 25.10%': 87200000\n", "}" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "import plotly.express as px\n", - "import plotly.io as pio\n", - "\n", - "# Create a camembert plot\n", - "fig = px.pie(\n", - " names=tissues.keys(),\n", - " values=tissues.values(),\n", - " title=\"Tissues in scPRINT-2\",\n", - " hole=0.3 # for a donut-style camembert\n", - ")\n", - "\n", - "# Export to PDF\n", - "pio.write_image(fig, \"tissues_pie_plotly.pdf\")\n" + "### Tissue Distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With all labels:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -159,45 +185,31 @@ "hole": 0.3, "hovertemplate": "label=%{label}
value=%{value}", "labels": [ - "human", - "mouse", - "macaque", - "zebrafish", - "pig", - "thale cress", - "drosophila", - "chicken", - "nake mole rat", - "rabbit", - "cow", - "corn", - "chimpanzee", - "c. elegans", - "sheep", - "marmoset" + "lung - 12.30%", + "blood - 9.83%", + "brain - 8.49%", + "colon - 7.43%", + "unknown - 6.85%", + "pancreas - 6.21%", + "embryo - 4.88%", + "skin - 3.60%", + "liver - 3.07%", + "breast - 3.03%", + "marrow - 2.75%", + "kidney - 2.36%", + "heart - 1.38%", + "immune system - 1.35%", + "eye - 1.35%", + "other (310 tissues) - 25.10%" ], "legendgroup": "", "name": "", "showlegend": true, "type": "pie", - "values": [ - "human (66.39%)", - "mouse (29.56%)", - "macaque (0.91%)", - "zebrafish (0.90%)", - "pig (0.47%)", - "thale cress (0.45%)", - "drosophila (0.45%)", - "chicken (0.17%)", - "nake mole rat (0.13%)", - "rabbit (0.12%)", - "cow (0.11%)", - "corn (0.10%)", - "chimpanzee (0.08%)", - "c. elegans (0.07%)", - "sheep (0.05%)", - "marmoset (0.03%)" - ] + "values": { + "bdata": "MEKOAmmOCQLjmcIBmyWKAdlRawGijUkB7woDAZ/xvgCxA6MA8tKgAFifkQAzUn0AN0BJALuFRwCAX0cAAJEyBQ==", + "dtype": "i4" + } } ], "layout": { @@ -981,7 +993,7 @@ } }, "title": { - "text": "Species in scPRINT-2" + "text": "Tissues in scPRINT-2" } } } @@ -991,24 +1003,29 @@ } ], "source": [ - "import plotly.express as px\n", - "import plotly.io as pio\n", - "\n", "# Create a camembert plot\n", "fig = px.pie(\n", - " names=list(species.keys()),\n", - " values=[f\"{k} ({v/sum(species.values())*100:.2f}%)\" for k, v in species.items()],\n", - " title=\"Species in scPRINT-2\",\n", - " hole=0.3, # for a donut-style camembert\n", + " names=list(tissues.keys()),\n", + " values=tissues.values(),\n", + " title=\"Tissues in scPRINT-2\",\n", + " hole=0.3 # for a donut-style camembert\n", ")\n", + "\n", "fig.show()\n", "# Export to PDF\n", - "pio.write_image(fig, \"species_pie_plotly.pdf\")" + "# pio.write_image(fig, \"tissues_pie_plotly.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With no labels:" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1032,23 +1049,30 @@ "hole": 0.3, "hovertemplate": "label=%{label}
value=%{value}", "labels": [ - "10x 3", - "10x 5", - "10x multiome", - "sciRNA-seq", - "slide-seq", - "scalebio", - "microwell", - "sciPlex", - "drop-seq", - "other (17 assays)" + "lung - 12.30%", + "blood - 9.83%", + "brain - 8.49%", + "colon - 7.43%", + "unknown - 6.85%", + "pancreas - 6.21%", + "embryo - 4.88%", + "skin - 3.60%", + "liver - 3.07%", + "breast - 3.03%", + "marrow - 2.75%", + "kidney - 2.36%", + "heart - 1.38%", + "immune system - 1.35%", + "eye - 1.35%", + "other (310 tissues) - 25.10%" ], "legendgroup": "", "name": "", "showlegend": true, + "textinfo": "none", "type": "pie", "values": { - "bdata": "mBqzEHedMAL8xJoALLnjABZ+HADgcwoAoyUJAGjfCACn5gcAkrgVAA==", + "bdata": "MEKOAmmOCQLjmcIBmyWKAdlRawGijUkB7woDAZ/xvgCxA6MA8tKgAFifkQAzUn0AN0BJALuFRwCAX0cAAJEyBQ==", "dtype": "i4" } } @@ -1834,34 +1858,6625 @@ } }, "title": { - "text": "Assays in scPRINT-2" + "text": "Tissues in scPRINT-2" } } } }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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0lung - 12.30%4287748812.333059
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" + ], + "text/plain": [ + " label count percent\n", + "15 other (310 tissues) - 25.10% 87200000 25.081757\n", + "0 lung - 12.30% 42877488 12.333059\n", + "1 blood - 9.83% 34180713 9.831564\n", + "2 brain - 8.49% 29530595 8.494028\n", + "3 colon - 7.43% 25830811 7.429841\n", + "4 unknown - 6.85% 23810521 6.848735\n", + "5 pancreas - 6.21% 21597602 6.212223\n", + "6 embryo - 4.88% 16976623 4.883068\n", + "7 skin - 3.60% 12513695 3.599375\n", + "8 liver - 3.07% 10683313 3.072893\n", + "9 breast - 3.03% 10539762 3.031603\n", + "10 marrow - 2.75% 9543512 2.745046\n", + "11 kidney - 2.36% 8213043 2.362357\n", + "12 heart - 1.38% 4800567 1.380810\n", + "13 immune system - 1.35% 4687291 1.348228\n", + "14 eye - 1.35% 4677504 1.345413" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import plotly.express as px\n", - "import plotly.io as pio\n", - "\n", "# Create a camembert plot\n", "fig = px.pie(\n", - " names=list(assays.keys()),\n", - " values=assays.values(),\n", - " title=\"Assays in scPRINT-2\",\n", - " hole=0.3, # for a donut-style camembert\n", + " names=list(tissues.keys()),\n", + " values=tissues.values(),\n", + " title=\"Tissues in scPRINT-2\",\n", + " hole=0.3 # for a donut-style camembert\n", ")\n", + "\n", + "fig.update_traces(textinfo=\"none\") \n", "fig.show()\n", "# Export to PDF\n", - "pio.write_image(fig, \"assays_pie_plotly.pdf\")" + "# pio.write_image(fig, \"tissues_pie_plotly.pdf\")\n", + "\n", + "labels = list(fig.data[0].labels)\n", + "values = list(fig.data[0].values)\n", + "total = sum(values)\n", + "\n", + "df_pct = pd.DataFrame({\n", + " \"label\": labels,\n", + " \"count\": values,\n", + " \"percent\": [v / total * 100 for v in values],\n", + "}).sort_values(\"percent\", ascending=False)\n", + "df_pct" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With top 3 labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hole": 0.3, + "hovertemplate": "label=%{label}
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plus grandes parts\n", + "top3_idx = set(np.argsort(values)[-3:])\n", + "\n", + "# texte seulement pour top3 (label + %)\n", + "custom_text = []\n", + "for i, (lab, val) in enumerate(zip(labels, values)):\n", + " if i in top3_idx:\n", + " pct = 100 * val / values.sum()\n", + " custom_text.append(f\"{lab}
{pct:.2f}%\")\n", + " else:\n", + " custom_text.append(\"\")\n", + "\n", + "fig.update_traces(\n", + " text=custom_text,\n", + " texttemplate=\"%{text}\",\n", + " textinfo=\"none\", # on utilise uniquement le texte custom\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Specie distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With all labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hole": 0.3, + "hovertemplate": "label=%{label}
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labelcountpercent
0human - 66.39%23082233766.394026
1mouse - 29.56%10275383829.556243
2macaque - 0.91%31611790.909285
3zebrafish - 0.90%31172370.896646
4pig - 0.47%16411790.472071
5thale cress - 0.45%15647110.450075
6drosophila - 0.45%15644600.450003
7chicken - 0.17%6066800.174506
8nake mole rat - 0.13%4410420.126862
9rabbit - 0.12%4170910.119973
10cow - 0.11%3906750.112374
11corn - 0.10%3361660.096695
12chimpanzee - 0.08%2934720.084415
13c. elegans - 0.07%2517590.072416
14sheep - 0.05%1778710.051163
15marmoset - 0.03%1155840.033247
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" + ], + "text/plain": [ + " label count percent\n", + "0 human - 66.39% 230822337 66.394026\n", + "1 mouse - 29.56% 102753838 29.556243\n", + "2 macaque - 0.91% 3161179 0.909285\n", + "3 zebrafish - 0.90% 3117237 0.896646\n", + "4 pig - 0.47% 1641179 0.472071\n", + "5 thale cress - 0.45% 1564711 0.450075\n", + "6 drosophila - 0.45% 1564460 0.450003\n", + "7 chicken - 0.17% 606680 0.174506\n", + "8 nake mole rat - 0.13% 441042 0.126862\n", + "9 rabbit - 0.12% 417091 0.119973\n", + "10 cow - 0.11% 390675 0.112374\n", + "11 corn - 0.10% 336166 0.096695\n", + "12 chimpanzee - 0.08% 293472 0.084415\n", + "13 c. elegans - 0.07% 251759 0.072416\n", + "14 sheep - 0.05% 177871 0.051163\n", + "15 marmoset - 0.03% 115584 0.033247" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a camembert plot\n", + "fig = px.pie(\n", + " names=list(species.keys()),\n", + " values=species.values(),\n", + " title=\"Species in scPRINT-2\",\n", + " hole=0.3, # for a donut-style camembert\n", + ")\n", + "fig.update_traces(textinfo=\"none\")\n", + "fig.show()\n", + "# Export to PDF\n", + "# pio.write_image(fig, \"species_pie_plotly.pdf\")\n", + "\n", + "labels = list(fig.data[0].labels)\n", + "values = list(fig.data[0].values)\n", + "total = sum(values)\n", + "\n", + "df_pct = pd.DataFrame({\n", + " \"label\": labels,\n", + " \"count\": values,\n", + " \"percent\": [v / total * 100 for v in values],\n", + "}).sort_values(\"percent\", ascending=False)\n", + "df_pct" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With top 3 labels: " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hole": 0.3, + "hovertemplate": "label=%{label}
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"paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + 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scPRINT-2\",\n", + " hole=0.3, # for a donut-style camembert\n", + ")\n", + "# indices des 3 plus grandes parts\n", + "top3_idx = set(np.argsort(values)[-3:])\n", + "\n", + "# texte seulement pour top3 (label + %)\n", + "custom_text = []\n", + "for i, (lab, val) in enumerate(zip(labels, values)):\n", + " if i in top3_idx:\n", + " pct = 100 * val / values.sum()\n", + " custom_text.append(f\"{lab}
{pct:.2f}%\")\n", + " else:\n", + " custom_text.append(\"\")\n", + "\n", + "fig.update_traces(\n", + " text=custom_text,\n", + " texttemplate=\"%{text}\",\n", + " textinfo=\"none\", # on utilise uniquement le texte custom\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assay distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With all labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hole": 0.3, + "hovertemplate": "label=%{label}
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labelcountpercent
010x 328017320880.589372
110x 53674047110.568075
3sciRNA-seq149240764.292780
210x multiome101429722.917537
4slide-seq18672860.537108
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5scalebio6850240.197041
6microwell5994590.172429
7sciPlex5814800.167258
8drop-seq5177990.148940
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"baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Assays in scPRINT-2" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "labels = list(assays.keys())\n", + "values = np.array(list(assays.values()), dtype=float)\n", + "\n", + "fig = px.pie(\n", + " names=labels,\n", + " values=values,\n", + " title=\"Assays in scPRINT-2\",\n", + " hole=0.3,\n", + ")\n", + "\n", + "# indices des 3 plus grandes parts\n", + "top3_idx = set(np.argsort(values)[-3:])\n", + "\n", + "# texte seulement pour top3 (label + %)\n", + "custom_text = []\n", + "for i, (lab, val) in enumerate(zip(labels, values)):\n", + " if i in top3_idx:\n", + " pct = 100 * val / values.sum()\n", + " custom_text.append(f\"{lab}
{pct:.2f}%\")\n", + " else:\n", + " custom_text.append(\"\")\n", + "\n", + "fig.update_traces(\n", + " text=custom_text,\n", + " texttemplate=\"%{text}\",\n", + " textinfo=\"none\", # on utilise uniquement le texte custom\n", + ")\n", + "\n", + "fig.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1893,7 +8508,7 @@ ], "metadata": { "kernelspec": { - "display_name": "scprint2", + "display_name": "scPRINT_env (3.11.14)", "language": "python", "name": "python3" }, @@ -1907,7 +8522,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.11.14" } }, "nbformat": 4,