Fig1G-H

Analysis/Figure1G.py

@@ -0,0 +1,219 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 14 10:29:12 2023
+
+@author: dhruv
+"""
+
+#import dependencies
+import numpy as np 
+import pandas as pd
+import scipy.io
+from Dependencies.functions import *
+from Dependencies.wrappers import *
+import os
+from Dependencies import neuroseries as nts 
+import matplotlib.pyplot as plt 
+from scipy.stats import kendalltau, wilcoxon, mannwhitneyu
+import seaborn as sns
+
+#%% Data organization
+
+data_directory = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Data/AdrianPoSub/###AllPoSub'
+datasets = np.genfromtxt(os.path.join(data_directory,'dataset_Hor_DM.list'), delimiter = '\n', dtype = str, comments = '#')
+
+readpath = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Projects/PoSub-UPstate/Data'
+writepath = '/home/dhruv/Code/MehrotraLevenstein_2023/Analysis/OutputFiles'
+
+allcoefs_up_ex = []
+allcoefs_dn_ex = []
+
+for s in datasets:
+
+    print(s)
+    name = s.split('/')[-1]
+    path = os.path.join(data_directory, s)
+    rawpath = os.path.join(readpath,s)
+
+###Loading the data
+    spikes, shank = loadSpikeData(path)
+    n_channels, fs, shank_to_channel = loadXML(rawpath)
+
+###Load cell depths
+    filepath = os.path.join(path, 'Analysis')
+    listdir = os.listdir(filepath)
+    file = [f for f in listdir if 'CellDepth' in f]
+    celldepth = scipy.io.loadmat(os.path.join(filepath,file[0]))
+    depth = celldepth['cellDep']
+
+###Load cell types 
+    file = [f for f in listdir if 'CellTypes' in f]
+    celltype = scipy.io.loadmat(os.path.join(filepath,file[0]))
+
+    pyr = []
+    interneuron = []
+    hd = []
+
+    for i in range(len(spikes)):
+        if celltype['ex'][i] == 1 and celltype['gd'][i] == 1:
+            pyr.append(i)
+
+    for i in range(len(spikes)):
+        if celltype['fs'][i] == 1 and celltype['gd'][i] == 1:
+            interneuron.append(i)
+
+    for i in range(len(spikes)):
+        if celltype['hd'][i] == 1 and celltype['gd'][i] == 1:
+            hd.append(i)    
+
+###Load UP and DOWN states
+    file = os.path.join(writepath, name +'.evt.py.dow')
+    if os.path.exists(file):
+        tmp = np.genfromtxt(file)[:,0]
+        tmp = tmp.reshape(len(tmp)//2,2)/1000
+        down_ep = nts.IntervalSet(start = tmp[:,0], end = tmp[:,1], time_units = 's')
+
+    file = os.path.join(writepath, name +'.evt.py.upp')
+    if os.path.exists(file):
+        tmp = np.genfromtxt(file)[:,0]
+        tmp = tmp.reshape(len(tmp)//2,2)/1000
+        up_ep = nts.IntervalSet(start = tmp[:,0], end = tmp[:,1], time_units = 's')
+
+#%% Compute Peri-event time Histogram (PETH) for UP onset
+
+    binsize = 5
+    nbins = 1000        
+    neurons = list(spikes.keys())
+    times = np.arange(0, binsize*(nbins+1), binsize) - (nbins*binsize)/2        
+    cc = pd.DataFrame(index = times, columns = neurons)
+    tsd_up = up_ep.as_units('ms').start.values
+
+    rates = []
+
+    for i in neurons:
+        spk2 = spikes[i].restrict(up_ep).as_units('ms').index.values
+        tmp = crossCorr(tsd_up, spk2, binsize, nbins)
+
+        tmp = pd.DataFrame(tmp)
+        tmp = tmp.rolling(window=8, win_type='gaussian',center=True,min_periods=1).mean(std = 2)
+
+        #Normalize PETH by mean rate
+        fr = len(spk2)/up_ep.tot_length('s')
+        rates.append(fr)
+        cc[i] = tmp.values
+        cc[i] = tmp.values/fr
+        dd = cc[0:150]
+
+    #Only EX cells
+    ee = dd[pyr]
+
+#%% Compute UP state Onset 
+
+    indexplot_ex = []
+    depths_keeping_ex = []
+
+    for i in range(len(ee.columns)):
+        a = np.where(ee.iloc[:,i] > 0.5)
+        if len(a[0]) > 0:
+            depths_keeping_ex.append(depth.flatten()[ee.columns[i]])
+            res = ee.iloc[:,i].index[a]
+            indexplot_ex.append(res[0])
+
+    #Onset v/s depth correlation
+    coef_ex, p_ex = kendalltau(indexplot_ex, depths_keeping_ex)
+    allcoefs_up_ex.append(coef_ex)
+
+#%% Compute PETH for DOWN onset
+
+    binsize = 5
+    nbins = 1000        
+    neurons = list(spikes.keys())
+    times = np.arange(0, binsize*(nbins+1), binsize) - (nbins*binsize)/2        
+    cc = pd.DataFrame(index = times, columns = neurons)
+    tsd_dn = down_ep.as_units('ms').start.values
+
+    rates = []
+
+    for i in neurons:
+        spk2 = spikes[i].restrict(up_ep).as_units('ms').index.values
+        tmp = crossCorr(tsd_dn, spk2, binsize, nbins)
+        tmp = pd.DataFrame(tmp)
+        tmp = tmp.rolling(window=8, win_type='gaussian',center=True,min_periods=1).mean(std = 2)
+
+        #Normalize PETH by mean rate
+        fr = len(spk2)/up_ep.tot_length('s')
+        rates.append(fr)
+        cc[i] = tmp.values
+        cc[i] = tmp.values/fr
+
+        dd = cc[-250:250]
+
+    #Only EX cells 
+    ee = dd[pyr]
+
+#%% Compute DOWN state onset 
+
+    tmp_ex = ee.loc[5:] > 0.5
+
+    tokeep_ex = tmp_ex.columns[tmp_ex.sum(0) > 0]
+    ends_ex = np.array([tmp_ex.index[np.where(tmp_ex[i])[0][0]] for i in tokeep_ex])
+    es_ex = pd.Series(index = tokeep_ex, data = ends_ex)
+
+    tmp2_ex = ee.loc[-150:-5] > 0.5
+
+    tokeep2_ex = tmp2_ex.columns[tmp2_ex.sum(0) > 0]
+    start_ex = np.array([tmp2_ex.index[np.where(tmp2_ex[i])[0][-1]] for i in tokeep2_ex])
+    st_ex = pd.Series(index = tokeep2_ex, data = start_ex)
+
+    ix_ex = np.intersect1d(tokeep_ex,tokeep2_ex)
+    ix_ex = [int(i) for i in ix_ex]
+
+    depths_keeping_ex = depth[ix_ex]
+
+    #Onset v/s depth correlation
+    coef_ex, p_ex = kendalltau(st_ex[ix_ex], depths_keeping_ex)
+    allcoefs_dn_ex.append(coef_ex)
+
+#%% Summary data 
+
+DUcorr = pd.DataFrame(allcoefs_up_ex)
+UDcorr = pd.DataFrame(allcoefs_dn_ex)
+DUtype = pd.DataFrame(['DU' for x in range(len(allcoefs_up_ex))])
+UDtype = pd.DataFrame(['UD' for x in range(len(allcoefs_dn_ex))])
+
+summary = pd.DataFrame()
+summary['corr'] = pd.concat([DUcorr,UDcorr])
+summary['type'] = pd.concat([DUtype,UDtype])
+
+#%% Plotting 
+
+sns.set_style('white')
+palette = ['royalblue', 'lightsteelblue']
+ax = sns.violinplot( x = summary['type'], y= summary['corr'] , data = summary, dodge = False,
+                    palette = palette,cut = 2,
+                    scale="width", inner=None)
+ax.tick_params(bottom=True, left=True)
+xlim = ax.get_xlim()
+ylim = ax.get_ylim()
+for violin in ax.collections:
+    x0, y0, width, height = violin.get_paths()[0].get_extents().bounds
+    violin.set_clip_path(plt.Rectangle((x0, y0), width / 2, height, transform=ax.transData))
+sns.boxplot(x = summary['type'], y = summary['corr'] , data = summary, saturation = 1, showfliers = False,
+            width = 0.3, boxprops = {'zorder': 3, 'facecolor': 'none'}, ax = ax)
+old_len_collections = len(ax.collections)
+sns.swarmplot(x = summary['type'], y = summary['corr'], data = summary, color = 'k', dodge = False, ax = ax)
+
+for dots in ax.collections[old_len_collections:]:
+    dots.set_offsets(dots.get_offsets())
+ax.set_xlim(xlim)
+ax.set_ylim(ylim)
+plt.axhline(0, color = 'silver')
+plt.ylabel('Delay v/s depth (R)')
+ax.set_box_aspect(1)
+
+#%% Stats 
+
+w_up, p_up = wilcoxon(np.array(allcoefs_up_ex)-0)
+w_dn, p_dn = wilcoxon(np.array(allcoefs_dn_ex)-0)
+t, p = mannwhitneyu(allcoefs_up_ex, allcoefs_dn_ex)

Analysis/Figure1H.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 14 16:45:10 2023
+
+@author: dhruv
+"""
+
+#import dependencies
+import numpy as np 
+import pandas as pd
+import scipy.io
+from Dependencies.functions import *
+from Dependencies.wrappers import *
+import os
+from Dependencies import neuroseries as nts 
+import matplotlib.pyplot as plt 
+from scipy.stats import wilcoxon
+
+#%% Data organization
+
+data_directory = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Data/AdrianPoSub/###AllPoSub'
+datasets = np.genfromtxt(os.path.join(data_directory,'dataset_Hor_DM.list'), delimiter = '\n', dtype = str, comments = '#')
+
+readpath = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Projects/PoSub-UPstate/Data'
+writepath = '/home/dhruv/Code/MehrotraLevenstein_2023/Analysis/OutputFiles'
+
+dur_D = []
+dur_V = []
+
+for s in datasets:
+
+    print(s)
+    name = s.split('/')[-1]
+    path = os.path.join(data_directory, s)
+    rawpath = os.path.join(readpath,s)
+
+###Loading the data
+    spikes, shank = loadSpikeData(path)
+    n_channels, fs, shank_to_channel = loadXML(rawpath)
+
+###Load cell depths
+    filepath = os.path.join(path, 'Analysis')
+    listdir = os.listdir(filepath)
+    file = [f for f in listdir if 'CellDepth' in f]
+    celldepth = scipy.io.loadmat(os.path.join(filepath,file[0]))
+    depth = celldepth['cellDep']
+
+###Load cell types 
+    file = [f for f in listdir if 'CellTypes' in f]
+    celltype = scipy.io.loadmat(os.path.join(filepath,file[0]))
+
+###Load global UP and DOWN states
+    file = os.path.join(writepath, name +'.evt.py.dow')
+    if os.path.exists(file):
+        tmp = np.genfromtxt(file)[:,0]
+        tmp = tmp.reshape(len(tmp)//2,2)/1000
+        down_ep = nts.IntervalSet(start = tmp[:,0], end = tmp[:,1], time_units = 's')
+
+    file = os.path.join(writepath, name +'.evt.py.upp')
+    if os.path.exists(file):
+        tmp = np.genfromtxt(file)[:,0]
+        tmp = tmp.reshape(len(tmp)//2,2)/1000
+        up_ep = nts.IntervalSet(start = tmp[:,0], end = tmp[:,1], time_units = 's')
+
+#%% Determine which units are in dorsal or ventral portion 
+
+    data = pd.DataFrame()   
+    data['depth'] = np.reshape(depth,(len(spikes.keys())),)
+    data['level'] = pd.cut(data['depth'],2, precision=0, labels=[1,0]) #0 is dorsal, 1 is ventral
+    data['celltype'] = np.nan
+    data['gd'] = 0
+
+    for i in range(len(spikes)):
+        if celltype['gd'][i] == 1:
+            data.loc[i,'gd'] = 1
+
+    data = data[data['gd'] == 1]
+
+#%% Split population activity into dorsal and ventral 
+
+    mua = {}
+
+    latency_dorsal = []
+    latency_ventral = []
+
+    # define mua for dorsal and ventral
+    for i in range(2):
+        mua[i] = []        
+        for n in data[data['level'] == i].index:            
+            mua[i].append(spikes[n].index.values)
+        mua[i] = nts.Ts(t = np.sort(np.hstack(mua[i])))
+
+#%% Compute PETH of dorsal and ventral MUA around global DOWN state
+### And determine the threshold crossing
+
+    binsize = 5
+    nbins = 1000        
+    neurons = list(mua.keys())
+    times = np.arange(0, binsize*(nbins+1), binsize) - (nbins*binsize)/2        
+    cc = pd.DataFrame(index = times, columns = neurons)
+    tsd_dn = down_ep.as_units('ms').start.values
+
+    rates = []
+
+    ddur = []
+    vdur = []
+
+    for i in neurons:
+        spk2 = mua[i].restrict(up_ep).as_units('ms').index.values
+        tmp = crossCorr(tsd_dn, spk2, binsize, nbins)
+        tmp = pd.DataFrame(tmp)
+        tmp = tmp.rolling(window=8, win_type='gaussian',center=True,min_periods=1).mean(std = 2)
+
+        #Normalize PETH by mean rate
+        fr = len(spk2)/up_ep.tot_length('s')
+        rates.append(fr)
+        cc[i] = tmp.values
+        cc[i] = tmp.values/fr
+
+        dd = cc[-250:250]
+
+        #Threshold crossing
+        tmp = dd[i].loc[5:] > 0.2 
+        ends = tmp.where(tmp == True).first_valid_index()
+
+        tmp2 = dd[i].loc[-150:-5] > 0.2 
+        start = tmp2.where(tmp2 == True).last_valid_index()
+
+        #Categorize the durations by anatomical position         
+        if i == 0: 
+            ddur.append(ends - start)
+        else: 
+            vdur.append(ends - start)
+
+    #Compute dorsal and ventral DOWN state durations for all datasets         
+    dur_D.append(ddur[0])
+    dur_V.append(vdur[0])
+
+#%% Plotting 
+
+plt.scatter(dur_D, dur_V, color = 'k', zorder = 3) 
+plt.gca().axline((min(min(dur_D),min(dur_V)),min(min(dur_D),min(dur_V)) ), slope=1, color = 'silver', linestyle = '--')
+plt.xlabel('Dorsal DOWN duration (ms)')
+plt.ylabel('Ventral DOWN duration (ms)')
+plt.axis('square')
+
+#%% Stats 
+
+W,p = wilcoxon(dur_D,dur_V)
+
+
+
+