Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment.

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[1] § Results › FOXG1 is dynamically regulated during the early phases of NPC differentiation ↔ notebooks/Figure2_Spatial_A-P_gene_patterning.ipynb, lines 2334–2372 · score 0.51 · LMX1A, OTX2, SIX3, EN1, PAX6, FOXG1

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The authors' code

Jupyter notebook · 2,410 lines · 88 KB · BSD-2-Clause · 1 match

# %%
#Import FISHscale
import sys
#If you did not pip install FISHscale
sys.path.insert(0, "/home/lars/storage/Documents/FISHscale/")
from FISHscale.utils import dataset
from FISHscale.utils.inside_polygon import inside_simple_polygons
#Otherwise you can just do: `import FISHscale'
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle as pkl
import math
import shapely
from shapely.geometry import Polygon, LineString, MultiLineString
from shapely.geometry import LineString, MultiPolygon, Polygon, Point
from shapely.ops import polygonize
from numpy.lib.stride_tricks import sliding_window_view
import napari
from matplotlib.patches import Polygon as mplPolygon
from matplotlib.collections import PatchCollection
from matplotlib.lines import Line2D
from scipy.ndimage import median_filter
import json
from FISHscale.utils.inside_polygon import close_polygon, inside_simple_polygons, is_inside_sm_parallel, inside_multi_polygons
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Basic functions
# %%
def get_line(x, y):
slope = (y[0] - y[1]) / (x[0] - x[1])
b = (x[0]*y[1] - x[1]*y[0]) / (x[0]-x[1])
return slope, b
def middle(x, y):
mx = x[0] + (0.5 * (x[1] - x[0]))
my = y[0] + (0.5 * (y[1] - y[0]))
return mx, my
def perpendicular_a(x, y):
return (x[1] - x[0]) / (y[0] - y[1])
def perpendicular_b(a, x, y):
return y - (a * x)
def line(a, b, x):
return a * x + b
def get_angle(x, y):
dx = x[1] - x[0]
dy = y[1] - y[0]
a = math.atan2(dy, dx)
return a
def make_perpendicular(x, y, l=1, direction='right'):
a,b = get_line(x,y)
mx, my = middle(x, y)
pa = perpendicular_a(x,y)
pb = perpendicular_b(pa, mx, my)
a = get_angle(x, y)
if direction == 'right':
sign=1
elif direction == 'left':
sign=-1
else:
raise Exception(f'Direction: {direction} not understood. Choose from "right" or "left".')
dy = y[1] - y[0]
dx = x[1] - x[0]
#Up and right
if dy >= 0 and dx >= 0:
qx = np.sin(a) * l * sign
qx = mx + qx
#Up and left
elif dy >= 0 and dx <= 0:
qx = np.sin(np.pi - a) * l * sign
qx = mx + qx
#Down and right
elif dy <= 0 and dx >= 0:
qx = np.sin(a) * l * sign
qx = mx + qx
#Down and left
elif dy <= 0 and dx <= 0:
qx = np.sin(np.pi - a) * l * sign
qx = mx + qx
else:
print('found straight line')
y1 = line(pa, pb, qx)
if abs(y1) > 10000:
y1 = 0
return [mx, qx], [my, y1]
# %% [markdown]
# # EEL data
# %%
#Change to your folder
folder = '/home/lars/Share/Dropbox (Linnarsson Group)/Linnarsson Group/Projects/EEL/Data/Human_embryo_5week/'
#RNA data of the most medial section
fname_970 = folder + 'LBEXP20211113_EEL_HE_5w_970um_RNA_transformed_assigned.parquet'
#Brain polygons
#pol_970 = np.load(folder + 'Brain_polygon_970.npy')
# %% [markdown]
# ## Run the below colapsed cell to load `pol_970`:
# %%
pol_970 = np.array([[4529.18440782, 3320.15563159],
[4442.64405105, 3431.25473825],
[4360.7815514 , 3520.13402359],
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# %%
#Load data using FISHscale
d970 = dataset.Dataset(fname_970,
gene_label = 'decoded_genes',
x_label = 'r_transformed',
y_label = 'c_transformed',
pixel_size='0.27 micrometer',
polygon = pol_970,
z = 970,
reparse=False)
# %%
folder = '/home/lars/storage/Documents/EEL/Human_embryo/5_week/Data/'
points_970 = pd.read_parquet(folder + '/970um/LBEXP20211113_EEL_HE_5w_970um_RNA_transformed_assigned.parquet')
# %% [markdown]
# # Draw subdivision lines (Already done, load results in next section)
# %%
viewer = napari.Viewer()
for g in ['SOX2', 'SOX9','PAX6', 'STMN2']:
xy = d970.get_gene(g)
_ = viewer.add_points(xy, face_color=[d970.color_dict[g]], edge_width=0, name=g, opacity=0.5)
for n in pol_970_names[::1]:
xy = polygons[970][n]
xy = np.flip(xy, axis=1)
xy = (xy * (1/0.27)) / 4
origin = np.array(np.flip(z_970.shape))/2
origin = origin / 2
xy = rotate(xy, origin=origin, degrees=180) #/ 5 #np.array(np.flip(z_970.shape))/2
xy = xy + np.array([-12000, -5000])
_ = viewer.add_shapes(xy, shape_type='polygon', edge_width=5, edge_color='coral', opacity=0.2)
_ = viewer.add_points(pol_970, face_color=[1, 0, 0], edge_width=0, name='brain_polygon', opacity=1, size=15)
# %%
lines_970 = viewer.layers['Lines'].data
pol_970_new = viewer.layers['brain_polygon'].data
# %%
#pkl.dump(lines_970, open('polygons/subdivision_lines_970', 'wb'))
#pkl.dump(pol_970_new, open('polygons/Ventricular_zone_970', 'wb'))
# %%
lines_970 = pkl.load(open('polygons/subdivision_lines_970', 'rb'))
pol_970_new = pkl.load(open('polygons/Ventricular_zone_970', 'rb'))
# %% [markdown]
# # Run the 2 collapsed cells below to load data
# %%
pol_970_new = np.array([[4472.7747222 , 3451.07754822],
[4428.97201769, 3546.30013274],
[4391.62877798, 3604.19775172],
[4371.25190742, 3647.07310406],
[4364.80745649, 3695.45577425],
[4368.47508388, 3743.85997891],
[4358.12741406, 3797.95227043],
[4330.3754801 , 3834.71991509],
[4319.85030158, 3875.65116491],
[4305.81673021, 3922.42973614],
[4287.10530172, 3962.19152168],
[4263.7160161 , 4030.02044996],
[4234.47940909, 4090.83259256],
[4250.85190902, 4161.0004494 ],
[4288.274766 , 4254.55759185],
[4304.64726593, 4311.86134161],
[4317.51137302, 4343.43687719],
[4322.18923014, 4386.70705557],
[4361.95101568, 4433.4856268 ],
[4404.05172979, 4507.16187648],
[4449.66083673, 4586.68544757],
[4498.77833652, 4652.17544728],
[4552.57369343, 4709.47919704],
[4595.84387182, 4764.44401823],
[4633.2667288 , 4792.51116097],
[4691.73994283, 4808.8836609 ],
[4691.73994283, 4808.8836609 ],
[4740.85744262, 4792.51116097],
[4796.99172809, 4792.51116097],
[4842.60083504, 4804.20580377],
[4861.31226353, 4826.42562511],
[4857.80387069, 4856.8316964 ],
[4885.87101343, 4900.10187479],
[4904.58244192, 4944.54151745],
[4908.09083476, 4988.98116012],
[4927.97172753, 5015.87883858],
[4944.34422746, 5061.48794552],
[4956.03887027, 5128.14740952],
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# %%
from numpy import array
lines_970 = np.array([array([[ 842.25749665, 3082.56013913],
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[6019.99595355, 3293.48643903]]),
array([[5723.69472275, 3429.08191753],
[5894.44458457, 3238.24383667]]),
array([[5653.38595612, 3383.88342469],
[5814.09170842, 3162.91301528]]),
array([[5583.07718949, 3353.75109614],
[5713.65061323, 3097.62630341]]),
array([[5472.59198478, 3373.83931518],
[5743.78294179, 2831.45740116]]),
array([[5412.32732767, 3308.5526033 ],
[5482.6360943 , 3042.38370106]]),
array([[5336.99650628, 3278.42027475],
[5347.0406158 , 3027.31753678]]),
array([[5231.53335633, 3303.53054854],
[5211.4451373 , 3047.40575582]]),
array([[5141.13637066, 3328.64082234],
[5085.89376831, 3077.53808437]]),
array([[5085.89376831, 3403.97164373],
[4925.18801601, 2891.72205828]]),
array([[5000.5188374 , 3393.92753421],
[4895.05568746, 3177.97917956]]),
array([[4890.0336327 , 3439.12602705],
[4809.68075655, 3203.08945336]]),
array([[4794.61459227, 3474.28041036],
[4724.30582564, 3203.08945336]]),
array([[4659.01911377, 3429.08191753],
[4583.68829238, 3002.20726298]]),
array([[4653.99705901, 3519.4789032 ],
[4176.90185687, 3243.26589143]]),
array([[4598.75445665, 3589.78766983],
[4322.54144489, 3459.21424608]]),
array([[4578.66623762, 3700.27287453],
[4297.43117109, 3579.74356031]]),
array([[4563.60007334, 3800.71396972],
[4257.25473302, 3710.31698405]]),
array([[4568.6221281 , 3916.22122919],
[4237.16651398, 3820.80218876]]),
array([[4523.42363526, 4041.77259817],
[4202.01213066, 3931.28739346]]),
array([[4463.15897815, 4147.23574812],
[4186.94596639, 4051.81670769]]),
array([[4538.48979954, 4227.58862427],
[4051.35048788, 4177.36807667]]),
array([[4443.07075911, 4302.91944566],
[4242.18856874, 4328.02971945]]),
array([[4483.24719719, 4418.42670512],
[4297.43117109, 4458.6031432 ]]),
array([[4538.48979954, 4498.77958127],
[4372.76199248, 4589.17656694]]),
array([[4804.65870179, 4343.09588373],
[4407.9163758 , 4790.05875731]]),
array([[4734.34993516, 4564.06629314],
[4563.60007334, 4800.10286683]]),
array([[4824.74692082, 4584.15451218],
[4679.1073328 , 4860.36752395]]),
array([[4925.18801601, 4649.44122405],
[4769.50431847, 4855.34546919]]),
array([[5020.60705644, 4800.10286683],
[4824.74692082, 4905.56601678]]),
array([[5045.71733024, 4940.72040009],
[4854.87924938, 4980.89683817]]),
array([[5090.91582307, 5071.29382384],
[4890.0336327 , 5086.35998812]]),
array([[4754.43815419, 5176.75697378],
[5231.53335633, 5302.30834277]]),
array([[4854.87924938, 5277.19806897],
[5050.739385 , 5427.85971175]]),
array([[4814.70281131, 5332.44067132],
[4925.18801601, 5503.19053314]]),
array([[4719.28377088, 5362.57299988],
[4759.46020895, 5563.45519026]]),
array([[4623.86473045, 5372.6171094 ],
[4623.86473045, 5578.52135453]]),
array([[4543.5118543 , 5347.5068356 ],
[4458.13692339, 5573.49929977]]),
array([[4508.35747098, 5302.30834277],
[4307.47528061, 5468.03614983]]),
array([[4528.44569002, 5161.69080951],
[4091.52692596, 5412.79354747]]),
array([[4438.04870435, 5091.38204288],
[4222.1003497 , 5231.99957614]]),
array([[4377.78404724, 4995.96300245],
[4171.87980211, 5181.77902854]]),
array([[4297.43117109, 4915.6101263 ],
[4076.46076168, 5161.69080951]]),
array([[4222.1003497 , 4870.41163346],
[4011.17404981, 5086.35998812]]),
array([[4156.81363783, 4805.12492159],
[3930.82117366, 5036.13944052]]),
array([[4091.52692596, 4719.74999068],
[3945.88733794, 4870.41163346]]),
array([[4016.19610457, 4649.44122405],
[3880.60062606, 4795.08081207]]),
array([[3965.97555697, 4589.17656694],
[3800.24774992, 4744.86026448]]),
array([[3890.64473558, 4468.64725272],
[3503.94651911, 4800.10286683]]),
array([[3885.62268082, 4433.4928694 ],
[3659.63021665, 4483.71341699]]),
array([[3895.66679034, 4287.85328138],
[3669.67432617, 4398.33848608]]),
array([[3770.11542136, 4172.34602191],
[3624.47583334, 4323.00766469]]),
array([[3760.07131184, 3996.57410534],
[3413.54953344, 4448.55903368]]),
array([[3599.36555954, 4031.72848865],
[3448.70391676, 4202.47835047]]),
array([[3508.96857387, 3971.46383154],
[3363.32898585, 4142.21369336]]),
array([[3463.77008104, 3830.84629828],
[3132.31446692, 4262.74300758]]),
array([[3363.32898585, 3845.91246255],
[3252.84378115, 3966.44177678]]),
array([[3313.10843826, 3765.5595864 ],
[3207.64528831, 3891.11095539]]),
array([[3237.77761687, 3695.25081977],
[3122.2703574 , 3850.93451731]]),
array([[3187.55706927, 3619.91999838],
[2896.27789323, 3981.50794106]]),
array([[3077.07186457, 3564.67739603],
[2946.49844082, 3730.40520309]]),
array([[2966.58665986, 3489.34657464],
[2871.16761943, 3690.22876501]]),
array([[2846.05734564, 3474.28041036],
[2775.74857901, 3665.11849122]]),
array([[2820.94707184, 3419.03780801],
[2574.86638863, 3845.91246255]]),
array([[2735.57214093, 3373.83931518],
[2579.88844339, 3584.76561507]]),
array([[2635.13104574, 3313.57465806],
[2544.73406008, 3534.54506747]]),
array([[2519.62378628, 3273.39821999],
[2469.40323869, 3529.52301272]]),
array([[2444.29296489, 3248.28794619],
[2379.00625302, 3529.52301272]]),
array([[2389.05036254, 3238.24383667],
[2173.10200788, 3755.51547689]]),
array([[2298.65337687, 3233.22178191],
[2188.16817216, 3424.05986277]]),
array([[2198.21228168, 3177.97917956],
[2102.79324125, 3363.79520566]]),
array([[2107.81529601, 3112.69246769],
[2007.37420082, 3298.50849378]]),
array([[2022.4403651 , 3067.49397485],
[1921.99926992, 3248.28794619]]),
array([[1921.99926992, 2987.1410987 ],
[1816.53611997, 3172.9571248 ]]),
array([[1831.60228425, 2916.83233207],
[1726.1391343 , 3107.67041293]]),
array([[1751.2494081 , 2871.63383924],
[1645.78625815, 3077.53808437]]),
array([[1650.80831291, 2821.41329165],
[1570.45543676, 3052.42781058]]),
array([[1575.47749152, 2771.19274405],
[1475.03639633, 3017.27342726]]),
array([[1477.18419773, 2724.9527946 ],
[1384.63941066, 2957.00877015]]),
array([[1330.59769618, 2906.03023769],
[1336.34618644, 2653.09666638]]),
array([[1267.36430335, 2888.78476692],
[1218.50213617, 2581.24053817]]),
array([[1224.25062643, 2885.91052179],
[1043.17318333, 2503.6359197 ]]),
array([[1195.50817514, 2940.52117923],
[ 925.32913306, 2558.24657714]]),
array([[1115.02931154, 2992.25759154],
[ 859.22149511, 2635.85119561]]),
array([[1020.1792223 , 3009.50306232],
[ 798.86234741, 2693.33609818]]),
array([[1000.77743454, 3092.91696153],
[ 515.02556184, 2548.8748641 ]]),
array([[ 890.83819152, 3041.11975873],
[ 672.39556176, 2860.04231563]]),
array([[ 847.7245146 , 3055.49098437],
[ 681.01829714, 2975.01212077]]),
array([[ 795.98810228, 3078.4849454 ],
[ 390.11793743, 3012.42093691]])])
# %% [markdown]
# # Subdivide ventricular zone and quantify gene expression
# %%
#Plot subdivision lines on ventricular zone outline
plt.figure(figsize=(10,10))
outer = pol_970_new[:289,:]
inner = pol_970_new[289:,:]
plt.scatter(inner[:,0], inner[:,1], s=8)
plt.scatter(outer[:,0], outer[:,1], s=8)
for ll in lines_970:
plt.plot(ll[:,0], ll[:,1], c='k')
plt.gca().set_aspect('equal')
# %%
#Make the inner polygon more dens
inner_dense = []
for ii in sliding_window_view(inner, [2,2]):
ii = ii[0]
inner_dense.append(ii[0])
mid = np.array(middle(ii[:,0], ii[:,1]))
inner_dense.append(mid)
inner_dense = np.stack(inner_dense)
plt.figure()
plt.scatter(inner_dense[:,0], inner_dense[:,1], s=1)
plt.scatter(inner[:,0], inner[:,1], s=1)
# %%
#Make a Shapely polygon out of `inner_dense`
pol = Polygon(outer, [inner_dense])
pol.exterior
# %%
#Subdivide polygon
#Subdivision lines to use
lines = lines_970
#Make polygons that subdivide the brain
ls_list = [LineString(ll) for ll in lines]
# make a complex geometry
input_p = pol
#make list of lines as Shapely LineString
ls_list = [LineString(ll) for ll in lines]
# and a complex intersecting line
input_l = MultiLineString(ls_list)
# union the exterior lines of the polygon with the dividing linestring
unioned = input_p.boundary.union(input_l)
# use polygonize geos operator and filter out poygons ouside of origina input polygon
keep_polys = [poly for poly in polygonize(unioned) if poly.representative_point().within(input_p)]
# remaining polygons are the split polys of original shape
mp = MultiPolygon(keep_polys)
plt.figure(figsize=(6,6))
coord_dict = {}
for i, p in enumerate(mp.geoms):
xy = p.exterior.coords.xy
coord_dict[i] = np.stack(xy, axis=1)
plt.plot(xy[0], xy[1])
def PolyArea(x,y):
return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1)))
#Area in um2
areas = []
for i in range(len(coord_dict)):
xy = coord_dict[i]
areas.append(PolyArea(xy[:,0], xy[:,1]))
plt.gca().set_aspect('equal')
# %%
#Count genes in polygons
df = pd.DataFrame(data=np.zeros((d970.unique_genes.shape[0], len(coord_dict))), index=d970.unique_genes)
df_gene = pd.DataFrame(data=d970.df.g, columns=['gene'])
points = d970.df.loc[:, ['x', 'y']].compute().to_numpy()
for l, filt in inside_simple_polygons(coord_dict, points):
df.loc[d970.unique_genes, l] = df_gene[filt].groupby('gene').size()
del points
df = df.fillna(0)
df_raw = df.copy()
#Correct for area in mm2
df = df / (np.array(areas) / 1e6)
# %%
#Plot gene count example to inspect results
fig, ax = plt.subplots(figsize=(6,6))
g = 'FGF17'
col = df.loc[g, :]
col = col / col.max()
col = plt.cm.viridis(col)
for i in range(len(coord_dict)):
xy = coord_dict[i]
p = mplPolygon(xy, facecolor=col[i])
ax.add_patch(p)
plt.xlim(0, 7000)
plt.ylim(2000, 7000)
plt.title(g)
plt.gca().set_aspect('equal')
# %%
#Plot gene count example to inspect results
fig, ax = plt.subplots(figsize=(6,6))
g = 'WNT1'
col = df.loc[g, :]
col = col / col.max()
col = plt.cm.inferno(col)
p = mplPolygon(outer, linewidth=6, facecolor='silver', ec='silver')
ax.add_patch(p )
for i in range(len(coord_dict)):
xy = coord_dict[i]
p = mplPolygon(xy, facecolor=col[i])
ax.add_patch(p)
plt.xlim(0, 7000)
plt.ylim(2000, 7000)
plt.title(g)
plt.gca().set_aspect('equal')
# %%
selected_genes = ['SIX6', 'OLIG3', 'FOXG1', 'RSPO3', 'SIX3','FEZF2', 'EMX2', 'EN1', 'EN2', 'PAX8', 'FGF17', 'PAX5']
fig, axes = plt.subplots(ncols=6, nrows=2, figsize=(14,5))
for i, g in enumerate(selected_genes):
ax = axes[int(i/6), i%6]
col = df.loc[g, :]
col = col / col.max() #(col.max() + col.max() * 0.2)
col = plt.cm.inferno(col)
#Add background
p = mplPolygon(outer, linewidth=6, facecolor='silver', ec='silver')
ax.add_patch(p )
for i in range(len(coord_dict)):
xy = coord_dict[i]
p = mplPolygon(xy, facecolor=col[i])
ax.add_patch(p)
ax.set_xlim(500, 6300)
ax.set_ylim(2500, 6300)
ax.set_aspect('equal')
ax.set_axis_off()
ax.set_title(g, fontsize=14)
ax.invert_xaxis()
plt.tight_layout()
plt.savefig('results/examples_AP_patterning.pdf')
# %%
a = np.array([[0,1]])
plt.figure(figsize=(0.6, 4))
img = plt.imshow(a, cmap="inferno")
plt.gca().set_visible(False)
cax = plt.axes([0.1, 0.2, 0.8, 0.6])
plt.colorbar(orientation="vertical", cax=cax)
plt.axis('off')
plt.savefig("results/colorbar_inferno.pdf")
# %%
#Find the roll to have the start of the polygons somwhere else
fig, ax = plt.subplots(figsize=(6,6))
col = np.roll(df.columns, 77)
col = col / col.max()
col = plt.cm.viridis(col)
for i in range(len(coord_dict)):
xy = coord_dict[i]
p = mplPolygon(xy, facecolor=col[i])
ax.add_patch(p)
plt.xlim(0, 7000)
plt.ylim(2000, 7000)
plt.gca().set_aspect('equal')
# %%
#Plot the new ordering numbers
plt.figure(figsize=(10,10))
for i, ll in enumerate(lines):
plt.plot(ll[:,0], ll[:,1], c='gray')
x, y = ll[1]
plt.text(x, y, i)
plt.gca().set_aspect('equal')
# %%
border_col = ['#548F80', '#D648F2', '#61D23E', '#53B039', '#FFE41B', '#C3313C', '#DF4927', '#E27A3A', '#804344', '#96623E', '#BB6C54', '#DBD06C',
'#63AA54', '#9F49B5', '#AE5ACF', '#C274D8', '#6E38C5', '#7751C6', '#4784C6','#5D92C8','#4FAB95', '#548F80','#548F80']
# %%
#Plot the colors of the polygons acording to the Allen brain atlas prosomeric model
fig, ax = plt.subplots(figsize=(6,6))
col_order = np.roll(df.columns, -77)
col = np.zeros(df.shape[1])
borders = [0, 36, 43, 46, 60, 78, 93, 98, 102, 103, 110, 114, 121, 128, 133, 137, 141, 144, 148, 152, 157, 174, 180] #177,
for i, view in enumerate(sliding_window_view(borders, 2)):
col[view[0] : view[1]] = i/len(borders)
elin_polygon_colors = {'ROI_Pall': '#C3313C', 'ROI_Pall_VZ': '#C3313C','ROI_Pall_MZ': '#C74150', 'ROI_AM_CP': '#DF4927', 'ROI_AM_CP_VZ': '#DF4927',
'ROI_AM_CP_MZ': '#FD6843',
'ROI_SPall': '#E27A3A','ROI_SPall_VZ': '#E27A3A', 'ROI_SPall_SVZ': '#E69D68','ROI_SPall_MZ': '#F2B880',
'ROI_THy': '#96623E','ROI_THy_VZ': '#96623E','ROI_THy_SVZ': '#A97751', 'ROI_THy_MZ': '#B2845B', 'ROI_optic_recess': '#9C5223','ROI_lamina_terminalis': '#804344',
'ROI_PHy': '#BB6C54','ROI_PHy_VZ': '#BB6C54','ROI_PHy_SVZ': '#C27D6A','ROI_PHy_MZ': '#D78A74',
'ROI_D_dorsal': '#FFE41B','ROI_D_dorsal_VZ': '#FFE41B','ROI_D_dorsal_MZ': '#FCF2B7','ROI_D_ventral': '#DBD06C','ROI_D_ventral_VZ': '#DBD06C',
'ROI_D_ventral_MZ': '#F1E177',
'ROI_MTt': '#53B039','ROI_MTg': '#63AA54','ROI_m2': '#61D23E','ROI_isthmus': '#D648F2',
'ROI_r1': '#9F49B5','ROI_r1_VZ': '#9F49B5','ROI_r1_MZ': '#C25BDC','ROI_r1-r2': '#AE5ACF','ROI_r1-r2_VZ': '#AE5ACF','ROI_r1-r2_SVZ': '#C75BDE',
'ROI_r1-r2_MZ': '#DB7CFA',
'ROI_r2_VZ': '#C274D8','ROI_r2_SVZ': '#D27EEC','ROI_r2_MZ': '#E5A1F9','ROI_r3_VZ': '#6E38C5','ROI_r3_SVZ': '#7C41DE','ROI_r3_MZ': '#9B61FA',
'ROI_r4_VZ': '#7751C6','ROI_r4_SVZ': '#875BE0','ROI_r4_MZ': '#A57BFF','ROI_r5_VZ': '#4784C6','ROI_r5_SVZ': '#4F94E0','ROI_r5_MZ': '#7EB8F3',
'ROI_r6_VZ': '#5D92C8','ROI_r6_SVZ': '#6AA4E1','ROI_r6_MZ': '#94C7F6','ROI_r7_VZ': '#4FAB95','ROI_r7_SVZ': '#59BFA7','ROI_r7_MZ': '#8CE0D0',
'ROI_r7_roof_plate': '#548F80','ROI_H_roof_plate': '#548F80'
}
border_col = ['#548F80', '#D648F2', '#61D23E', '#53B039', '#FFE41B', '#C3313C', '#DF4927', '#E27A3A', '#804344', '#96623E', '#BB6C54', '#DBD06C',
'#63AA54', '#9F49B5', '#AE5ACF', '#C274D8', '#6E38C5', '#7751C6', '#4784C6','#5D92C8','#4FAB95', '#548F80'] ##548F80',
border_col_arange = np.zeros(df.shape[1], dtype='object')
for i, view in enumerate(sliding_window_view(borders, 2, )):
border_col_arange[view[0] : view[1]] = border_col[i]
for j,i in enumerate(col_order):
xy = coord_dict[i]
p = mplPolygon(xy, facecolor=border_col_arange[j])
ax.add_patch(p)
plt.xlim(0, 7000)
plt.ylim(2000, 7000)
plt.gca().set_aspect('equal')
plt.axis('off')
#plt.savefig('results/ABA_colors.pdf')
# %% [markdown]
# # Anterior - posterior patterning
# %%
#Split prosomers
n_anatomical_regions = len(border_col)
ref_spacing = np.array([36, 7, 3, 14, 18, 15, 5, 4, 1, 7, 4, 7, 7, 5, 4, 4, 3, 4, 4, 5, 17, 6]) #3
diff = np.ediff1d(borders)
x = []
x_cont = []
gap = 0
stop = 0
for r, d in zip(ref_spacing, diff):
if d == 1:
d=2
start = stop + gap
stop = start + r
xi = np.linspace(start, stop, d)
x.append(xi)
x_cont.append(xi[:-1])
gap = 0.7
# %%
#Plot patterning histogram of relevant genes
plt.figure(figsize=(12, 12))
selected = ['ARX', 'ASCL1', 'BARHL1', 'BMP7', 'BOC', 'CALM1', 'CLU', 'CRABP1', 'DBX1', 'DCC', 'DLK1', 'DNAJB1', 'EFNA5', 'ELN', 'EMX2', 'EN1', 'EN2',
'ENC1', 'FABP7', 'FEZF1', 'FEZF2', 'FGF8', 'FGF17', 'FGFBP3', 'FOXA2', 'FOXG1', 'FOXP4', 'FRZB', 'FST', 'GLI3', 'HES5', 'ID1', 'IGFBP5',
'ISL1', 'LAMA2', 'LAMP5', 'LHX2', 'LMX1A', 'LMX1B', 'MEIS2', 'MSX1','NEFL', 'NKX2-1', 'NKX2-2', 'NR2E1', 'NR2F2', 'NTN1', 'OLIG2', 'OLIG3',
'OTX2', 'PAX2', 'PAX3', 'PAX5', 'PAX6', 'PAX8', 'PHOX2B', 'PTN', 'ROBO3', 'RSPO2', 'RSPO3', 'SFRP1', 'SFRP2', 'SHH', 'SIX3', 'SIX6', 'SLIT2',
'SOX2', 'SOX3', 'SOX9', 'SPON1', 'SULF1', 'TFAP2A', 'TTYH1', 'WNT1', 'WNT2B', 'ZIC1', 'ZIC2']
filt = np.isin(df.index, selected)
ap = df.loc[filt]
selected_order = ap.index
ap = np.roll(ap.to_numpy(), -77, axis=1)
sort = np.argsort(np.argmax(ap, axis=1))
selected_order = selected_order[sort]
for y, g in enumerate(selected_order[::-1]):
data = df.loc[g, col_order].to_numpy()
data = median_filter(data, footprint=np.ones(2))
data[data<0] = 0
data = (data / data.max()) * 0.7
data2 = data
data = np.split(data, borders[1:])
for i, (xi, d) in enumerate(zip(x, data)):
plt.fill_between(xi, y, y+d, color=border_col[i])
if len(d) == 1:
d = np.array([d, d])
plt.plot(xi, y+d, c='gray', linewidth=1)
if y % 2 == 1:
plt.plot([xi[-1]+1, xi[-1]+14], [y+0.25, y+0.25], c='silver', linewidth=1)
plt.text(xi[-1]+15, y-0.25, g)
else:
plt.text(xi[-1]+1, y-0.25, g)
plt.axis('off');
#plt.savefig('results/Patterning_970.pdf')
# %%
#Plot all genes
#Note that some genes are lowly expressed but due to normaization to their maximum they look equal to other genes.
#Always check raw gene expression to see if results are relevant.
plt.figure(figsize=(12, 12*5))
selected = d970.unique_genes
filt = np.isin(df.index, selected)
ap = df.loc[filt]
selected_order = ap.index
ap = np.roll(ap.to_numpy(), -77, axis=1)
sort = np.argsort(np.argmax(ap, axis=1))
selected_order = selected_order[sort]
for y, g in enumerate(selected_order[::-1]):
data = df.loc[g, col_order].to_numpy()
data = median_filter(data, footprint=np.ones(2))
data[data<0] = 0
#Normalization
data = (data / data.max()) * 0.7
data2 = data
data = np.split(data, borders[1:])
for i, (xi, d) in enumerate(zip(x, data)):
plt.fill_between(xi, y, y+d, color=border_col[i])
if len(d) == 1:
d = np.array([d, d])
plt.plot(xi, y+d, c='gray', linewidth=1)
if y % 2 == 1:
plt.plot([xi[-1]+1, xi[-1]+14], [y+0.25, y+0.25], c='silver', linewidth=1)
plt.text(xi[-1]+15, y-0.25, g)
else:
plt.text(xi[-1]+1, y-0.25, g)
plt.axis('off');

Figure2_Spatial_A-P_gene_patterning.ipynb at commit 1f5f002, under BSD-2-Clause · at the source

Overview

Authors: Lisa Hamerlinck^1,2, Eva D’haene^1,2, Michael B Vaughan^1,2, Nore Van Loon^1,2,3, María del Rocío Pérez Baca^1,2, Sebastian Leimbacher^1,2, Michael Kosicki⁴, Lara Colombo^1,2, Lukas Genbrugge^1,2, Lies Vantomme^1,2, Esperanza Daal^1,2, Luiza Lorena Pires Ramos^5,6, Daniela Mircheva Avdjieva-Tzavella⁷, Himanshu Goel^8,9, Koen Devriendt¹⁰, Albena Jordanova^5,6,11, Annelies Dheedene^1,2, Axel Visel^4,12,13, Björn Menten^1,2, Bert Callewaert^1,2, Sarah Vergult^1,2

ORCID iDs: Lisa Hamerlinck, Eva D’haene, Michael B Vaughan, María del Rocío Pérez Baca, Michael Kosicki, Esperanza Daal, Luiza Lorena Pires Ramos, Himanshu Goel, Koen Devriendt, Axel Visel, Björn Menten, Bert Callewaert, Sarah Vergult

13 affiliations

Institutions: Ghent University Hospital (Belgium); Ghent University (Belgium); Lawrence Berkeley National Laboratory (United States); University of Antwerp (Belgium); VIB-UAntwerp Center for Molecular Neurology (Belgium); Medical University of Sofia (Bulgaria); Hunter Genetics (Australia); University of Newcastle Australia (Australia); Universitair Ziekenhuis Leuven (Belgium); University of California, Merced (United States); Joint Genome Institute (United States)

Journal: Nature communications, volume 17, issue 1, article 7197

Dates: received 25 March 2025; accepted 14 May 2026; published online 3 June 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41467-026-73770-1 · PMID 42236689 · PMCID PMC13396769 · OpenAlex W7163334608

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), other condition (population), developmental (subfield)

Methods: Evoked potentials, Statistics, fMRI & imaging

Keywords: Gene regulation, Neurodevelopmental disorders, Disease genetics

MeSH: Forkhead Transcription Factors*, Gene Expression Regulation, Developmental*, Nerve Tissue Proteins*, Animals, Cell Differentiation, Enhancer Elements, Genetic, Humans, Neural Stem Cells, Neurodevelopment, Prosencephalon, Transcription, Genetic (* major topic)

Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders) (G063423N, 1520518N, G055422N); NHGRI NIH HHS (R01 HG003988); Bijzonder Onderzoeksfonds (Special Research Fund) (BOF.BAF.2024.0263.01, BOF.STG.2019.0103.01, BOF.BAF.2025.0095.01); Wellcome Trust (WT223718/Z/21/Z)

Citations: cited by 1 paper (Europe PMC); 70 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 5653535

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the text, “CUT&RUN analysis”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: pandas (4 files), NumPy (3 files), Matplotlib (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

10 files

bin/calc_frag_hist.py, Python, 141 lines
bin/check_samplesheet.py, Python, 313 lines
bin/find_unique_reads.py, Python, 126 lines
bin/igv_files_to_session.py, Python, 209 lines
bin/markdown_to_html.py, Python, 116 lines
bin/peak_reproducibility.py, Python, 149 lines
bin/plot_consensus_peaks.py, Python, 108 lines
bin/reports.py, Python, 120 lines
LICENSE, License, 21 lines
README.md, Text, 130 lines

At the source:

linnarsson-lab/developing-human-brain

License: BSD-2-Clause

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 1f5f0026d0ebe34ca895e2ce09f72cdd230b09f1, 10 September 2026

Languages: Jupyter (7)

Size: 12 files, 7 scripts

Software Heritage: archived

Found in: “Data availability”

Holds: README, license file, 7 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Matplotlib (5 files), NumPy (5 files), SciPy (5 files), pandas (3 files), napari (2 files), scikit-learn (2 files), rpy2 (1 file), Scanpy (1 file), scikit-image (1 file), scVelo (1 file), seaborn (1 file), statannotations (1 file), UMAP (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

7 files

notebooks/Figure1-2_Overview.ipynb, Jupyter, 397 lines
notebooks/Figure2_Spatial.ipynb, Jupyter, 170 lines
notebooks/Figure2_Spatial_A-P_gene_patterning.ipynb, Jupyter, 2,410 lines, 1 match
notebooks/Figure2_Spatial_mapping_of_clusters.ipynb, Jupyter, 342 lines
notebooks/Figure3_pallium_lineage.ipynb, Jupyter, 2,505 lines
repository limit reached (2,000 files or 30 MB): the rest is at the source (2 files)
LICENSE, License, 25 lines
README.md, Text, 71 lines

At the source: github.com/linnarsson-lab/developing-human-brain

nf-core/cutandrun

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 6e1125d4fee4ea7c8b70ed836bb0e92a89e3305f, 1 February 2024

Languages: Python (8)

Size: 290 files, 8 scripts

Software Heritage: archived

Found in: the Zenodo archive record

Holds: README, license file, environment (pyproject.toml, .devcontainer/devcontainer.json, dev/docker/master_local_module_image/Dockerfile, dev/docker/master_local_module_image/environment.yml), continuous integration, documentation

Not found: CITATION.cff, tests

Tools: pandas (4 files), NumPy (3 files), Matplotlib (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

10 files

bin/calc_frag_hist.py, Python, 141 lines
bin/check_samplesheet.py, Python, 313 lines
bin/find_unique_reads.py, Python, 126 lines
bin/igv_files_to_session.py, Python, 209 lines
bin/markdown_to_html.py, Python, 116 lines
bin/peak_reproducibility.py, Python, 149 lines
bin/plot_consensus_peaks.py, Python, 108 lines
bin/reports.py, Python, 120 lines
LICENSE, License, 21 lines
README.md, Text, 130 lines

At the source: github.com/nf-core/cutandrun/tree/3.2.2

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
21 scripts, each with its path and the digest of its content;
1 match between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

geo:GSE292880, at NCBI GEO; found in “Data availability”

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: NCBI GEO GSE292880
it points to the authors' code: linnarsson-lab/developing-human-brain
it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-73770-1.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 3 keywords, 11 MeSH terms, 4 funders, 70 references.

Cite

This paper

Hamerlinck, L., D’haene, E., Vaughan, M. B., Van Loon, N., Pérez Baca, M. d. R., Leimbacher, S., Kosicki, M., Colombo, L., Genbrugge, L., Vantomme, L., Daal, E., Ramos, L. L. P., Avdjieva-Tzavella, D. M., Goel, H., Devriendt, K., Jordanova, A., Dheedene, A., Visel, A., Menten, B., . . . Vergult, S. (2026). Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment. Nature communications, 17(1), 7197. https://doi.org/10.1038/s41467-026-73770-1

BibTeX

@article{hamerlinck2026non, author = {Hamerlinck, Lisa and D’haene, Eva and Vaughan, Michael B and Van Loon, Nore and Pérez Baca, María del Rocío and Leimbacher, Sebastian and Kosicki, Michael and Colombo, Lara and Genbrugge, Lukas and Vantomme, Lies and Daal, Esperanza and Ramos, Luiza Lorena Pires and Avdjieva-Tzavella, Daniela Mircheva and Goel, Himanshu and Devriendt, Koen and Jordanova, Albena and Dheedene, Annelies and Visel, Axel and Menten, Björn and Callewaert, Bert and Vergult, Sarah}, title = {{Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment}}, journal = {Nature communications}, year = {2026}, month = jun, volume = {17}, number = {1}, pages = {7197}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-73770-1}, url = {https://doi.org/10.1038/s41467-026-73770-1}, pmid = {42236689}, pmcid = {PMC13396769} }

RIS

TY - JOUR AU - Hamerlinck, Lisa AU - D’haene, Eva AU - Vaughan, Michael B AU - Van Loon, Nore AU - Pérez Baca, María del Rocío AU - Leimbacher, Sebastian AU - Kosicki, Michael AU - Colombo, Lara AU - Genbrugge, Lukas AU - Vantomme, Lies AU - Daal, Esperanza AU - Ramos, Luiza Lorena Pires AU - Avdjieva-Tzavella, Daniela Mircheva AU - Goel, Himanshu AU - Devriendt, Koen AU - Jordanova, Albena AU - Dheedene, Annelies AU - Visel, Axel AU - Menten, Björn AU - Callewaert, Bert AU - Vergult, Sarah TI - Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/06/03 VL - 17 IS - 1 SP - 7197 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-73770-1 UR - https://doi.org/10.1038/s41467-026-73770-1 LA - en ER -

CSL-JSON

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Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment.

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Zenodo 5653535

linnarsson-lab/developing-human-brain

nf-core/cutandrun

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