Tutorial 9: ISH-based STARmap dataset

Here we present our re-analysis of the ISH-based mouse visual cortex STARmap dataset (See Fig. S16 of the STAGATE paper for details).

The raw data are available at https://www.dropbox.com/sh/f7ebheru1lbz91s/AADm6D54GSEFXB1feRy6OSASa/visual_1020/20180505_BY3_1kgenes?dl=0&subfolder_nav_tracking=1.

The annotation information and the processed SCANPY object are provided at https://drive.google.com/drive/folders/1I1nxheWlc2RXSdiv24dex3YRaEh780my?usp=sharing.

Preparation

[1]:

import pandas as pd
import numpy as np
import scanpy as sc
import os
import sys
import matplotlib.pyplot as plt
import seaborn as sns
import gc
import warnings
warnings.filterwarnings("ignore")

[2]:

import STAGATE

[3]:

adata = sc.read('STARmap_20180505_BY3_1k.h5ad')

[4]:

adata.obs.head()

[4]:

	Total_counts	X	Y	label
Cell_9	310	4980.777664	-49.771247	L6
Cell_10	533	8729.177425	-53.473114	L4
Cell_13	452	11547.566359	-46.315493	L2/3
Cell_15	288	3280.980264	-37.612811	L6
Cell_16	457	7582.793601	-45.350312	L4

[5]:

sc.pp.highly_variable_genes(adata, flavor="seurat_v3", n_top_genes=3000)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

Constructing the spatial network

[6]:

STAGATE.Cal_Spatial_Net(adata, rad_cutoff=400)
STAGATE.Stats_Spatial_Net(adata)

------Calculating spatial graph...
The graph contains 7838 edges, 1207 cells.
6.4938 neighbors per cell on average.

Running STAGATE

[7]:

adata = STAGATE.train_STAGATE(adata, alpha=0)

Size of Input:  (1207, 1020)
WARNING:tensorflow:From /home/dkn/anaconda3/envs/STAGATE_tf/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

100%|██████████| 500/500 [00:21<00:00, 22.79it/s]

[8]:

sc.pp.neighbors(adata, use_rep='STAGATE')
sc.tl.umap(adata)

[9]:

adata = STAGATE.mclust_R(adata, used_obsm='STAGATE', num_cluster=7)

R[write to console]:                    __           __
   ____ ___  _____/ /_  _______/ /_
  / __ `__ \/ ___/ / / / / ___/ __/
 / / / / / / /__/ / /_/ (__  ) /_
/_/ /_/ /_/\___/_/\__,_/____/\__/   version 5.4.10
Type 'citation("mclust")' for citing this R package in publications.

fitting ...
  |======================================================================| 100%

[ ]:

[10]:

sc.pl.umap(adata, color='mclust')

[11]:

plt.rcParams["figure.figsize"] = (4, 2)
sc.pl.embedding(adata, basis="spatial", color='mclust', s=20, show=False)#, legend_loc=False)

[11]:

<AxesSubplot:title={'center':'mclust'}, xlabel='spatial1', ylabel='spatial2'>

[14]:

# layer annotation
plt.rcParams["figure.figsize"] = (4, 2)
sc.pl.embedding(adata, basis="spatial", color='label', s=20, show=False)#, legend_loc=False)

[14]:

<AxesSubplot:title={'center':'label'}, xlabel='spatial1', ylabel='spatial2'>

Calculate ARI

[15]:

from sklearn.metrics.cluster import adjusted_rand_score

[16]:

adjusted_rand_score(adata.obs['label'], adata.obs['mclust'])

[16]:

0.5441835384954468