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Cedric (Huchuan) Xia committed Mar 8, 2018
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76 changes: 76 additions & 0 deletions .ipynb_checkpoints/Cedric_gene_functions-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook Cedric_gene_functions.ipynb to script\n",
"[NbConvertApp] Writing 5930 bytes to Cedric_gene_functions.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script Cedric_gene_functions.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## geneMNItoSchaefer"
]
},
{
"cell_type": "code",
"execution_count": 76,
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" print 'Saved variables at ' + variables_filename + '\\n'\n",
" print 'Saved csv at ' + csv_filename + '\\n'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## selectProbe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def selectProbe (donor):\n",
" import os\n",
" import pandas as pd\n",
" \n",
" # Set up the working directory\n",
" # All paths after this are relative path to the root of chead\n",
"\n",
" wk_dir = '/Users/hxia/Desktop/BBL/' # this is where I have mounted chead to on my laptop. \n",
" # Change the above to an empty string if working directly on chead \n",
"\n",
" # Set up the project directory\n",
" project_path = os.path.join(wk_dir,'data/joy/BBL/projects/prsConnectivity')\n",
" \n",
" # set paths\n",
" donor_path = os.path.join(project_path,'ABI','normalized_microarray_donor%s' % donor)\n",
" probes_info = pd.read_csv(os.path.join(donor_path,'Probes.csv'))\n",
" gene_expr = pd.read_csv(os.path.join(donor_path,'MicroarrayExpression.csv'),header=None)\n",
" sample_info = pd.read_csv(os.path.join(donor_path,'SampleAnnot.csv'))\n",
" \n",
" # get probes with available gene names\n",
" valid_probes = probes_info['entrez_id']>0\n",
" gene_expr = gene_expr[valid_probes]\n",
" probes_info = probes_info[valid_probes]\n",
" \n",
" # collapse rows\n",
" probe_names = probes_info['gene_symbol'][probes_info['probe_id'] == gene_expr[0]]\n",
" datCollapsed = gene_expr.groupby(probe_names).max()\n",
" \n",
" # create column names\n",
" gene_col_names = ['probe_id']\n",
" structure_ids = list(sample_info['structure_id'].astype(str))\n",
" gene_col_names.extend(structure_ids)\n",
" datCollapsed.columns = gene_col_names\n",
" \n",
" # output\n",
" return datCollapsed"
]
}
],
"metadata": {
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6 changes: 6 additions & 0 deletions .ipynb_checkpoints/Select_Probes-checkpoint.ipynb
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
200 changes: 200 additions & 0 deletions .ipynb_checkpoints/forOne_PlotGenesOnBrain-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%gui qt4"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import os.path as op\n",
"import numpy as np\n",
"from numpy import arange\n",
"from numpy.random import permutation\n",
"import nibabel as nib\n",
"from surfer import Brain\n",
"import pickle\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Set up the working directory\n",
"# All paths after this are relative path to the root of chead\n",
"\n",
"wk_dir = '/Users/hxia/Desktop/BBL/' # this is where I have mounted chead to on my laptop. \n",
"# Change the above to an empty string if working directly on chead \n",
"\n",
"# Set up the project directory\n",
"project_path = os.path.join(wk_dir,'data/joy/BBL/projects/prsConnectivity')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"donor = 15496\n",
"numParcel = 100\n",
"numNetwork = 7\n",
"mm = 1\n",
"parcel_annot_file = os.path.join(wk_dir,'data/joy/BBL/studies/pnc/template/Schaefer/FreeSurfer5.3/fsaverage/label',\\\n",
" 'lh.Schaefer2018_%sParcels_%sNetworks_order.annot' % (numParcel,numNetwork))\n",
"gene_coord_file = os.path.join(project_path,'ABI','gene_mapping','%sdonor_%sParcels_%sNetwork_%smm.pkl'\\\n",
" % (donor, numParcel, numNetwork, mm))\n",
"gene_coord = pickle.load( open( gene_coord_file, \"rb\" ) )\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Cont', 'Default', 'DorsAttn', 'Limbic', 'NotAssigned',\n",
" 'SalVentAttn', 'SomMot', 'Vis'], dtype='|S11')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"community_list = np.unique(gene_coord[4].values())\n",
"community_color_list = ['orange','red','green','lightgreen','black',\\\n",
" 'violet','blue','purple']\n",
"\n",
"#community_color_list = ['orange','red','green','lightgreen',\\\n",
"# 'violet','blue','purple','orchid','salmon','black'\\\n",
"# 'teal','gold','darkblue','crimson','beige','plum','silver']\n",
"community_list"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"brain = Brain('fsaverage', \"lh\", \"inflated\",background='white',alpha=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"brain = Brain('fsaverage','lh', \"inflated\",views=['lat','med'], background='white',alpha=1)"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {},
"outputs": [],
"source": [
"brain = Brain('fsaverage', \"lh\", \"sphere\", background='white',alpha=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"for idx, community in enumerate(community_list):\n",
" if community != 'NotAssigned':\n",
" filtered_dict = {k:v for k,v in gene_coord[4].iteritems() if community in v}\n",
" brain.add_foci(pd.DataFrame.as_matrix(gene_coord[0].loc[filtered_dict.keys()]),hemi='lh',map_surface='pial',\\\n",
" color=community_color_list[idx],scale_factor=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {},
"outputs": [],
"source": [
"brain = Brain('fsaverage', \"lh\", \"sphere\",background='white')\n",
"for idx, community in enumerate(community_list):\n",
" if community != 'NotAssigned':\n",
" filtered_dict = {k:v for k,v in gene_coord[4].iteritems() if community in v}\n",
" brain.add_foci(pd.DataFrame.as_matrix(gene_coord[0].loc[filtered_dict.keys()]),map_surface='sphere',\\\n",
" color=community_color_list[idx],scale_factor=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [],
"source": [
"brain.add_annotation(parcel_annot_file, borders=False,alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"brain.add_annotation(parcel_annot_file, borders=False,alpha=0.5, hemi='lh')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"brain = Brain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "myenv2",
"language": "python",
"name": "myenv2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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