Step by step tutorial¶
This tutorial showing the step-by-step process of running ONTraC using stereo-seq mouse midbrain dataset.
Prepare¶
The hardware environment used for this tutorial¶
CPU¶
we only use 4 CPU here.
%%bash
lscpu
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8568Y+
CPU family: 6
Model: 207
Thread(s) per core: 1
Core(s) per socket: 48
Socket(s): 2
Stepping: 2
Frequency boost: enabled
CPU(s) scaling MHz: 100%
CPU max MHz: 2301.0000
CPU min MHz: 800.0000
BogoMIPS: 4600.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
L1d cache: 4.5 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 192 MiB (96 instances)
L3 cache: 600 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-47
NUMA node1 CPU(s): 48-95
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
GPU¶
%%bash
nvidia-smi
Wed Jan 15 12:46:15 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA H100 80GB HBM3 On | 00000000:3D:00.0 Off | Off |
| N/A 26C P0 67W / 700W | 1MiB / 81559MiB | 0% E. Process |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
install ONTraC¶
Skip this if you’ve installed ONTraC already.
%%bash
source ~/.bash_profile # change it to "source ~/.zshrc" if you are using zsh
conda activate ONTraC # ONTraC is the conda environment name
pip install "ONTraC[analysis]==1.*"
Show code cell output
The following have been reloaded with a version change:
1) gcc/14.2.0 => gcc/11.2.0
Collecting ONTraC==1.* (from ONTraC[analysis]==1.*)
Downloading ONTraC-1.1.3-py3-none-any.whl.metadata (6.6 kB)
Requirement already satisfied: pyyaml==6.0.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (6.0.1)
Requirement already satisfied: pandas==2.2.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.2.1)
Requirement already satisfied: torch==2.2.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.2.1)
Requirement already satisfied: torch-geometric==2.5.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (2.5.0)
Requirement already satisfied: session-info in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC==1.*->ONTraC[analysis]==1.*) (1.0.0)
Requirement already satisfied: seaborn in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from ONTraC[analysis]==1.*) (0.13.2)
Requirement already satisfied: numpy<2,>=1.23.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (1.26.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from pandas==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2024.1)
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Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.105)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.105)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.105)
Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (8.9.2.26)
Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.3.1)
Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (11.0.2.54)
Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (10.3.2.106)
Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (11.4.5.107)
Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.0.106)
Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2.19.3)
Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (12.1.105)
Requirement already satisfied: triton==2.2.0 in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch==2.2.1->ONTraC==1.*->ONTraC[analysis]==1.*) (2.2.0)
Requirement already satisfied: tqdm in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric==2.5.0->ONTraC==1.*->ONTraC[analysis]==1.*) (4.66.5)
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Requirement already satisfied: aiohttp in /sc/arion/work/wangw32/conda-env/envs/ONTraC/lib/python3.11/site-packages (from torch-geometric==2.5.0->ONTraC==1.*->ONTraC[analysis]==1.*) (3.9.5)
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Downloading ONTraC-1.1.3-py3-none-any.whl (66 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 66.8/66.8 kB 10.0 MB/s eta 0:00:00
?25hInstalling collected packages: ONTraC
Successfully installed ONTraC-1.1.3
Load Modules¶
# fake options
from optparse import Values
# ONTraC running modules
from ONTraC.run.processes import niche_trajectory_construct, gnn, load_parameters, niche_network_construct
from ONTraC.utils import write_version_info
from ONTraC.integrate.general_control import options_valid
# ONTraC analysis modules
from ONTraC.analysis.data import AnaData
# visualization modules
import matplotlib as mpl
import numpy as np
import pandas as pd
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['font.family'] = 'Arial'
import matplotlib.pyplot as plt
import seaborn as sns
create directories¶
import os
os.makedirs('figures', exist_ok=True)
os.makedirs('log', exist_ok=True)
os.makedirs('output', exist_ok=True)
validate ONTraC version info¶
write_version_info()
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version: 1.1.3
##################################################################################
Download dataset¶
import requests
# URL of the file
url = "https://zenodo.org/records/14519865/files/Stereo_seq_data.zip"
# Local file path to save the file
file_path = "./Stereo_seq_data.zip"
try:
# Send a GET request to the URL
response = requests.get(url)
response.raise_for_status() # Check if the request was successful
# Write the content to the file
with open(file_path, "wb") as file:
file.write(response.content)
print(f"File downloaded and saved to {file_path}")
except requests.exceptions.RequestException as e:
print(f"An error occurred: {e}")
File downloaded and saved to ./Stereo_seq_data.zip
import zipfile
# Path to the zip file
zip_file_path = "Stereo_seq_data.zip"
# Directory where files will be extracted
extract_to_path = "./"
try:
# Open the zip file
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
# Extract all files to the specified directory
zip_ref.extractall(extract_to_path)
print(f"Files extracted to '{extract_to_path}'")
except zipfile.BadZipFile:
print("The file is not a valid zip file.")
Files extracted to './'
Input data¶
input_file = './Stereo_seq_data/preprocessing/stereo_input.csv'
meta_df = pd.read_csv(input_file, index_col=0)
meta_df.head()
| Sample | Cell_Type | x | y | |
|---|---|---|---|---|
| Cell_ID | ||||
| E12_E1S3_100034 | E12_E1S3 | Fibro | 15940.0 | 18584.0 |
| E12_E1S3_100035 | E12_E1S3 | Fibro | 15942.0 | 18623.0 |
| E12_E1S3_100191 | E12_E1S3 | Endo | 15965.0 | 18619.0 |
| E12_E1S3_100256 | E12_E1S3 | Fibro | 15969.0 | 18717.0 |
| E12_E1S3_100264 | E12_E1S3 | Fibro | 15974.0 | 18692.0 |
Input data visualization¶
from ONTraC.analysis.cell_type import plot_spatial_cell_type_distribution_dataset
fig, ax = plot_spatial_cell_type_distribution_dataset(meta_df)
fig.savefig('figures/Spatial_cell_type.png', dpi=300)
set up options¶
run_options = Values()
run_options.NN_dir = './output/stereo_seq_NN'
run_options.GNN_dir = './output/stereo_seq_GNN'
run_options.NT_dir = './output/stereo_seq_NT'
run_options.meta_input = input_file
run_options.n_neighbors = 50
run_options.device = 'cuda'
run_options.epochs = 1000
run_options.hidden_feats = 4
run_options.n_gcn_layers = 2
run_options.k = 6
run_options.modularity_loss_weight = 0.3
run_options.purity_loss_weight = 300.0
run_options.regularization_loss_weight = 0.1
run_options.beta = 0.03
run_options.trajectory_construct = 'BF'
run_options = options_valid(run_options)
12:51:08 --- WARNING: The directory (./output/stereo_seq_NN) you given already exists. It will be overwritten.
12:51:08 --- WARNING: The directory (./output/stereo_seq_GNN) you given already exists. It will be overwritten.
12:51:08 --- WARNING: The directory (./output/stereo_seq_NT) you given already exists. It will be overwritten.
12:51:08 --- INFO: ------------------ RUN params memo ------------------
12:51:08 --- INFO: -------- I/O options -------
12:51:08 --- INFO: Niche network output directory: ./output/stereo_seq_NN
12:51:08 --- INFO: GNN output directory: ./output/stereo_seq_GNN
12:51:08 --- INFO: Niche trajectory output directory: ./output/stereo_seq_NT
12:51:08 --- INFO: Meta data file: ./Stereo_seq_data/preprocessing/stereo_input.csv
12:51:08 --- INFO: -------- niche net constr options -------
12:51:08 --- INFO: n_cpu: 4
12:51:08 --- INFO: n_neighbors: 50
12:51:08 --- INFO: n_local: 20
12:51:08 --- INFO: -------- train options -------
12:51:08 --- INFO: device: cuda
12:51:08 --- INFO: epochs: 1000
12:51:08 --- INFO: batch_size: 0
12:51:08 --- INFO: patience: 100
12:51:08 --- INFO: min_delta: 0.001
12:51:08 --- INFO: min_epochs: 50
12:51:08 --- INFO: seed: 42
12:51:08 --- INFO: lr: 0.03
12:51:08 --- INFO: hidden_feats: 4
12:51:08 --- INFO: n_gcn_layers: 2
12:51:08 --- INFO: k: 6
12:51:08 --- INFO: modularity_loss_weight: 0.3
12:51:08 --- INFO: purity_loss_weight: 300.0
12:51:08 --- INFO: regularization_loss_weight: 0.1
12:51:08 --- INFO: beta: 0.03
12:51:08 --- INFO: ---------------- Niche trajectory options ----------------
12:51:08 --- INFO: Niche trajectory construction method: BF
12:51:08 --- INFO: --------------- RUN params memo end -----------------
vis_options = Values()
vis_options.NN_dir = run_options.NN_dir
vis_options.GNN_dir = run_options.GNN_dir
vis_options.NT_dir = run_options.NT_dir
vis_options.output = None
vis_options.reverse = True
niche network construction¶
niche_network_construct(options=run_options)
12:51:10 --- INFO: ------------- Niche network construct ---------------
12:51:11 --- INFO: Constructing niche network for sample: E12_E1S3.
12:51:11 --- INFO: Building KNN network for sample: E12_E1S3...
12:51:11 --- INFO: Calculating edge index for sample: E12_E1S3...
12:51:11 --- INFO: Calculating niche weight matrix for sample: E12_E1S3...
12:51:11 --- INFO: Calculating cell type composition for sample: E12_E1S3...
12:51:13 --- INFO: Constructing niche network for sample: E14_E1S3.
12:51:13 --- INFO: Building KNN network for sample: E14_E1S3...
12:51:13 --- INFO: Calculating edge index for sample: E14_E1S3...
12:51:13 --- INFO: Calculating niche weight matrix for sample: E14_E1S3...
12:51:13 --- INFO: Calculating cell type composition for sample: E14_E1S3...
12:51:14 --- INFO: Constructing niche network for sample: E16_E1S3.
12:51:14 --- INFO: Building KNN network for sample: E16_E1S3...
12:51:14 --- INFO: Calculating edge index for sample: E16_E1S3...
12:51:14 --- INFO: Calculating niche weight matrix for sample: E16_E1S3...
12:51:14 --- INFO: Calculating cell type composition for sample: E16_E1S3...
12:51:18 --- INFO: Constructing niche network for sample: E16_E2S6.
12:51:18 --- INFO: Building KNN network for sample: E16_E2S6...
12:51:18 --- INFO: Calculating edge index for sample: E16_E2S6...
12:51:18 --- INFO: Calculating niche weight matrix for sample: E16_E2S6...
12:51:18 --- INFO: Calculating cell type composition for sample: E16_E2S6...
12:51:21 --- INFO: Constructing niche network for sample: E16_E2S7.
12:51:21 --- INFO: Building KNN network for sample: E16_E2S7...
12:51:21 --- INFO: Calculating edge index for sample: E16_E2S7...
12:51:21 --- INFO: Calculating niche weight matrix for sample: E16_E2S7...
12:51:21 --- INFO: Calculating cell type composition for sample: E16_E2S7...
12:51:25 --- INFO: Generating samples.yaml file.
12:51:25 --- INFO: ------------ Niche network construct end ------------
niche network visulization¶
ana_data = AnaData(vis_options)
from ONTraC.analysis.spatial import plot_cell_type_composition_dataset_from_anadata
fig, axes = plot_cell_type_composition_dataset_from_anadata(ana_data)
fig.savefig('figures/Spatial_cell_type_composition.png', dpi=300)
This figure shows cell type composition of niches across samples. Rows represent samples, and columns denote specific cell types. The position of each niche is based on its anchor cell. Cell type compositions within each niche are normalized to a total of 1.
GNN training¶
# It's will run much faster using GPU
gnn(options=run_options)
Show code cell output
12:51:44 --- INFO: ------------------------ GNN ------------------------
12:51:44 --- INFO: Loading dataset.
12:51:44 --- INFO: Maximum number of cell in one sample is: 7300.
12:51:44 --- INFO: Processing sample 1 of 5: E12_E1S3
12:51:44 --- INFO: Processing sample 2 of 5: E14_E1S3
12:51:44 --- INFO: Processing sample 3 of 5: E16_E1S3
12:51:44 --- INFO: Processing sample 4 of 5: E16_E2S6
12:51:44 --- INFO: Processing sample 5 of 5: E16_E2S7
Processing...
12:51:44 --- INFO: Processing sample 1 of 5: E12_E1S3
12:51:44 --- INFO: Processing sample 2 of 5: E14_E1S3
12:51:44 --- INFO: Processing sample 3 of 5: E16_E1S3
12:51:44 --- INFO: Processing sample 4 of 5: E16_E2S6
12:51:44 --- INFO: Processing sample 5 of 5: E16_E2S7
Done!
12:51:48 --- INFO: epoch: 1, batch: 1, loss: 9.778472900390625, modularity_loss: -5.301945293467725e-06, purity_loss: 9.703614234924316, regularization_loss: 0.074863962829113
12:51:49 --- INFO: epoch: 2, batch: 1, loss: 9.77786922454834, modularity_loss: -1.015456837194506e-05, purity_loss: 9.703018188476562, regularization_loss: 0.07486078143119812
12:51:49 --- INFO: epoch: 3, batch: 1, loss: 9.776715278625488, modularity_loss: -1.7354162991978228e-05, purity_loss: 9.701872825622559, regularization_loss: 0.07485990971326828
12:51:49 --- INFO: epoch: 4, batch: 1, loss: 9.774765968322754, modularity_loss: -2.81233496934874e-05, purity_loss: 9.699932098388672, regularization_loss: 0.07486207038164139
12:51:50 --- INFO: epoch: 5, batch: 1, loss: 9.771647453308105, modularity_loss: -4.370275200926699e-05, purity_loss: 9.696822166442871, regularization_loss: 0.07486861944198608
12:51:50 --- INFO: epoch: 6, batch: 1, loss: 9.766881942749023, modularity_loss: -6.566625233972445e-05, purity_loss: 9.69206714630127, regularization_loss: 0.07488040626049042
12:51:50 --- INFO: epoch: 7, batch: 1, loss: 9.75985050201416, modularity_loss: -9.631682769395411e-05, purity_loss: 9.685049057006836, regularization_loss: 0.07489747554063797
12:51:51 --- INFO: epoch: 8, batch: 1, loss: 9.749622344970703, modularity_loss: -0.00013897198368795216, purity_loss: 9.674840927124023, regularization_loss: 0.07492003589868546
12:51:51 --- INFO: epoch: 9, batch: 1, loss: 9.734915733337402, modularity_loss: -0.0001976090861717239, purity_loss: 9.660161972045898, regularization_loss: 0.07495173066854477
12:51:51 --- INFO: epoch: 10, batch: 1, loss: 9.715461730957031, modularity_loss: -0.00027284130919724703, purity_loss: 9.640742301940918, regularization_loss: 0.074991874396801
12:51:52 --- INFO: epoch: 11, batch: 1, loss: 9.690521240234375, modularity_loss: -0.00036775501212105155, purity_loss: 9.615846633911133, regularization_loss: 0.07504197955131531
12:51:52 --- INFO: epoch: 12, batch: 1, loss: 9.658707618713379, modularity_loss: -0.0004875881422776729, purity_loss: 9.584090232849121, regularization_loss: 0.07510457187891006
12:51:52 --- INFO: epoch: 13, batch: 1, loss: 9.618454933166504, modularity_loss: -0.0006379465921781957, purity_loss: 9.543914794921875, regularization_loss: 0.07517780363559723
12:51:53 --- INFO: epoch: 14, batch: 1, loss: 9.568153381347656, modularity_loss: -0.0008247985388152301, purity_loss: 9.493718147277832, regularization_loss: 0.07526051253080368
12:51:53 --- INFO: epoch: 15, batch: 1, loss: 9.506158828735352, modularity_loss: -0.0010548006976023316, purity_loss: 9.431863784790039, regularization_loss: 0.07535027712583542
12:51:53 --- INFO: epoch: 16, batch: 1, loss: 9.430818557739258, modularity_loss: -0.0013352627865970135, purity_loss: 9.356709480285645, regularization_loss: 0.07544431835412979
12:51:54 --- INFO: epoch: 17, batch: 1, loss: 9.340622901916504, modularity_loss: -0.001673881895840168, purity_loss: 9.266755104064941, regularization_loss: 0.07554177939891815
12:51:54 --- INFO: epoch: 18, batch: 1, loss: 9.234286308288574, modularity_loss: -0.002078694524243474, purity_loss: 9.160722732543945, regularization_loss: 0.07564238458871841
12:51:54 --- INFO: epoch: 19, batch: 1, loss: 9.110861778259277, modularity_loss: -0.002558174543082714, purity_loss: 9.037674903869629, regularization_loss: 0.07574526965618134
12:51:55 --- INFO: epoch: 20, batch: 1, loss: 8.969585418701172, modularity_loss: -0.0031219245865941048, purity_loss: 8.896857261657715, regularization_loss: 0.0758499801158905
12:51:55 --- INFO: epoch: 21, batch: 1, loss: 8.81017017364502, modularity_loss: -0.0037797922268509865, purity_loss: 8.737991333007812, regularization_loss: 0.07595886290073395
12:51:55 --- INFO: epoch: 22, batch: 1, loss: 8.632827758789062, modularity_loss: -0.004541211295872927, purity_loss: 8.561293601989746, regularization_loss: 0.0760757178068161
12:51:56 --- INFO: epoch: 23, batch: 1, loss: 8.438493728637695, modularity_loss: -0.005415359046310186, purity_loss: 8.367706298828125, regularization_loss: 0.07620313763618469
12:51:56 --- INFO: epoch: 24, batch: 1, loss: 8.228599548339844, modularity_loss: -0.006413841620087624, purity_loss: 8.158673286437988, regularization_loss: 0.0763397142291069
12:51:56 --- INFO: epoch: 25, batch: 1, loss: 8.005273818969727, modularity_loss: -0.007548750843852758, purity_loss: 7.936337471008301, regularization_loss: 0.07648524641990662
12:51:57 --- INFO: epoch: 26, batch: 1, loss: 7.771193027496338, modularity_loss: -0.00882952380925417, purity_loss: 7.703376770019531, regularization_loss: 0.07664582133293152
12:51:57 --- INFO: epoch: 27, batch: 1, loss: 7.529590606689453, modularity_loss: -0.01026308722794056, purity_loss: 7.463024616241455, regularization_loss: 0.07682904601097107
12:51:57 --- INFO: epoch: 28, batch: 1, loss: 7.28435754776001, modularity_loss: -0.011850944720208645, purity_loss: 7.219165802001953, regularization_loss: 0.07704265415668488
12:51:58 --- INFO: epoch: 29, batch: 1, loss: 7.039899826049805, modularity_loss: -0.013589639216661453, purity_loss: 6.976195812225342, regularization_loss: 0.07729386538267136
12:51:58 --- INFO: epoch: 30, batch: 1, loss: 6.80087947845459, modularity_loss: -0.015468196012079716, purity_loss: 6.738758087158203, regularization_loss: 0.07758945971727371
12:51:59 --- INFO: epoch: 31, batch: 1, loss: 6.571942329406738, modularity_loss: -0.017466377466917038, purity_loss: 6.511473178863525, regularization_loss: 0.07793539762496948
12:51:59 --- INFO: epoch: 32, batch: 1, loss: 6.357494831085205, modularity_loss: -0.01955432817339897, purity_loss: 6.298713684082031, regularization_loss: 0.07833549380302429
12:51:59 --- INFO: epoch: 33, batch: 1, loss: 6.161476135253906, modularity_loss: -0.021691210567951202, purity_loss: 6.104376316070557, regularization_loss: 0.07879083603620529
12:52:00 --- INFO: epoch: 34, batch: 1, loss: 5.986982822418213, modularity_loss: -0.023828385397791862, purity_loss: 5.931512832641602, regularization_loss: 0.07929838448762894
12:52:00 --- INFO: epoch: 35, batch: 1, loss: 5.836049556732178, modularity_loss: -0.0259120911359787, purity_loss: 5.782110214233398, regularization_loss: 0.07985144853591919
12:52:00 --- INFO: epoch: 36, batch: 1, loss: 5.709449291229248, modularity_loss: -0.027889791876077652, purity_loss: 5.656899452209473, regularization_loss: 0.08043980598449707
12:52:01 --- INFO: epoch: 37, batch: 1, loss: 5.606629371643066, modularity_loss: -0.029716169461607933, purity_loss: 5.555294513702393, regularization_loss: 0.08105113357305527
12:52:01 --- INFO: epoch: 38, batch: 1, loss: 5.525762557983398, modularity_loss: -0.031357843428850174, purity_loss: 5.4754486083984375, regularization_loss: 0.08167177438735962
12:52:01 --- INFO: epoch: 39, batch: 1, loss: 5.464176177978516, modularity_loss: -0.03279896453022957, purity_loss: 5.41468620300293, regularization_loss: 0.08228884637355804
12:52:02 --- INFO: epoch: 40, batch: 1, loss: 5.418673515319824, modularity_loss: -0.03403877094388008, purity_loss: 5.369821071624756, regularization_loss: 0.0828910768032074
12:52:02 --- INFO: epoch: 41, batch: 1, loss: 5.3860063552856445, modularity_loss: -0.03508828952908516, purity_loss: 5.337625026702881, regularization_loss: 0.08346948027610779
12:52:02 --- INFO: epoch: 42, batch: 1, loss: 5.3631367683410645, modularity_loss: -0.03596808761358261, purity_loss: 5.315087795257568, regularization_loss: 0.08401711285114288
12:52:03 --- INFO: epoch: 43, batch: 1, loss: 5.34743070602417, modularity_loss: -0.03670331835746765, purity_loss: 5.299604892730713, regularization_loss: 0.08452928811311722
12:52:03 --- INFO: epoch: 44, batch: 1, loss: 5.336722373962402, modularity_loss: -0.03732036426663399, purity_loss: 5.289039611816406, regularization_loss: 0.08500336110591888
12:52:03 --- INFO: epoch: 45, batch: 1, loss: 5.329352855682373, modularity_loss: -0.037844639271497726, purity_loss: 5.281759262084961, regularization_loss: 0.08543812483549118
12:52:04 --- INFO: epoch: 46, batch: 1, loss: 5.324151039123535, modularity_loss: -0.03829912841320038, purity_loss: 5.27661657333374, regularization_loss: 0.08583375811576843
12:52:04 --- INFO: epoch: 47, batch: 1, loss: 5.320250988006592, modularity_loss: -0.03870343416929245, purity_loss: 5.272763252258301, regularization_loss: 0.0861911028623581
12:52:04 --- INFO: epoch: 48, batch: 1, loss: 5.317051410675049, modularity_loss: -0.03907221928238869, purity_loss: 5.26961088180542, regularization_loss: 0.08651276677846909
12:52:05 --- INFO: epoch: 49, batch: 1, loss: 5.314214706420898, modularity_loss: -0.03941727429628372, purity_loss: 5.266829967498779, regularization_loss: 0.08680206537246704
12:52:05 --- INFO: epoch: 50, batch: 1, loss: 5.311525344848633, modularity_loss: -0.03974740207195282, purity_loss: 5.264210224151611, regularization_loss: 0.08706275373697281
12:52:05 --- INFO: epoch: 51, batch: 1, loss: 5.308882236480713, modularity_loss: -0.04006868600845337, purity_loss: 5.261651992797852, regularization_loss: 0.08729889988899231
12:52:06 --- INFO: epoch: 52, batch: 1, loss: 5.306251049041748, modularity_loss: -0.040385372936725616, purity_loss: 5.259121417999268, regularization_loss: 0.08751503378152847
12:52:06 --- INFO: epoch: 53, batch: 1, loss: 5.303623676300049, modularity_loss: -0.04070049896836281, purity_loss: 5.256608486175537, regularization_loss: 0.08771568536758423
12:52:06 --- INFO: epoch: 54, batch: 1, loss: 5.301036357879639, modularity_loss: -0.04101535305380821, purity_loss: 5.254146099090576, regularization_loss: 0.08790537714958191
12:52:07 --- INFO: epoch: 55, batch: 1, loss: 5.298506259918213, modularity_loss: -0.04133124276995659, purity_loss: 5.251749038696289, regularization_loss: 0.08808865398168564
12:52:07 --- INFO: epoch: 56, batch: 1, loss: 5.296057224273682, modularity_loss: -0.04164900258183479, purity_loss: 5.249436378479004, regularization_loss: 0.08826977759599686
12:52:07 --- INFO: epoch: 57, batch: 1, loss: 5.293705940246582, modularity_loss: -0.04196925461292267, purity_loss: 5.247222423553467, regularization_loss: 0.08845263719558716
12:52:08 --- INFO: epoch: 58, batch: 1, loss: 5.291446208953857, modularity_loss: -0.04229258745908737, purity_loss: 5.245098114013672, regularization_loss: 0.08864080160856247
12:52:08 --- INFO: epoch: 59, batch: 1, loss: 5.289272308349609, modularity_loss: -0.04261954873800278, purity_loss: 5.243054389953613, regularization_loss: 0.0888376235961914
12:52:08 --- INFO: epoch: 60, batch: 1, loss: 5.2871575355529785, modularity_loss: -0.04295065626502037, purity_loss: 5.241062164306641, regularization_loss: 0.08904586732387543
12:52:09 --- INFO: epoch: 61, batch: 1, loss: 5.285057067871094, modularity_loss: -0.043286845088005066, purity_loss: 5.239076137542725, regularization_loss: 0.08926776051521301
12:52:09 --- INFO: epoch: 62, batch: 1, loss: 5.282928943634033, modularity_loss: -0.04362910985946655, purity_loss: 5.237052917480469, regularization_loss: 0.08950535207986832
12:52:09 --- INFO: epoch: 63, batch: 1, loss: 5.280729293823242, modularity_loss: -0.04397837817668915, purity_loss: 5.234947681427002, regularization_loss: 0.08975996822118759
12:52:10 --- INFO: epoch: 64, batch: 1, loss: 5.27841329574585, modularity_loss: -0.04433612897992134, purity_loss: 5.232717037200928, regularization_loss: 0.09003251045942307
12:52:10 --- INFO: epoch: 65, batch: 1, loss: 5.275956153869629, modularity_loss: -0.04470367729663849, purity_loss: 5.2303361892700195, regularization_loss: 0.0903235450387001
12:52:11 --- INFO: epoch: 66, batch: 1, loss: 5.273321628570557, modularity_loss: -0.04508255794644356, purity_loss: 5.227771282196045, regularization_loss: 0.09063313156366348
12:52:11 --- INFO: epoch: 67, batch: 1, loss: 5.2704854011535645, modularity_loss: -0.04547438770532608, purity_loss: 5.224998474121094, regularization_loss: 0.09096122533082962
12:52:11 --- INFO: epoch: 68, batch: 1, loss: 5.267446517944336, modularity_loss: -0.04588046669960022, purity_loss: 5.222019195556641, regularization_loss: 0.09130784124135971
12:52:12 --- INFO: epoch: 69, batch: 1, loss: 5.264194011688232, modularity_loss: -0.0463024340569973, purity_loss: 5.2188239097595215, regularization_loss: 0.0916723981499672
12:52:12 --- INFO: epoch: 70, batch: 1, loss: 5.260708808898926, modularity_loss: -0.04674188047647476, purity_loss: 5.215396404266357, regularization_loss: 0.09205438941717148
12:52:12 --- INFO: epoch: 71, batch: 1, loss: 5.256988048553467, modularity_loss: -0.04720042273402214, purity_loss: 5.211735725402832, regularization_loss: 0.09245292097330093
12:52:13 --- INFO: epoch: 72, batch: 1, loss: 5.253030776977539, modularity_loss: -0.047679875046014786, purity_loss: 5.20784330368042, regularization_loss: 0.09286712110042572
12:52:13 --- INFO: epoch: 73, batch: 1, loss: 5.248831272125244, modularity_loss: -0.04818228259682655, purity_loss: 5.203717231750488, regularization_loss: 0.0932961255311966
12:52:13 --- INFO: epoch: 74, batch: 1, loss: 5.244379997253418, modularity_loss: -0.048708975315093994, purity_loss: 5.199349880218506, regularization_loss: 0.09373932331800461
12:52:14 --- INFO: epoch: 75, batch: 1, loss: 5.239683628082275, modularity_loss: -0.049262333661317825, purity_loss: 5.19474983215332, regularization_loss: 0.0941958948969841
12:52:14 --- INFO: epoch: 76, batch: 1, loss: 5.234729766845703, modularity_loss: -0.049844030290842056, purity_loss: 5.189908504486084, regularization_loss: 0.09466540068387985
12:52:14 --- INFO: epoch: 77, batch: 1, loss: 5.2295002937316895, modularity_loss: -0.0504552461206913, purity_loss: 5.184808254241943, regularization_loss: 0.09514731913805008
12:52:15 --- INFO: epoch: 78, batch: 1, loss: 5.224000453948975, modularity_loss: -0.05109749361872673, purity_loss: 5.1794562339782715, regularization_loss: 0.09564150869846344
12:52:15 --- INFO: epoch: 79, batch: 1, loss: 5.21821928024292, modularity_loss: -0.05177196115255356, purity_loss: 5.1738433837890625, regularization_loss: 0.09614794701337814
12:52:15 --- INFO: epoch: 80, batch: 1, loss: 5.212143421173096, modularity_loss: -0.05247960239648819, purity_loss: 5.167956352233887, regularization_loss: 0.09666658192873001
12:52:16 --- INFO: epoch: 81, batch: 1, loss: 5.2057719230651855, modularity_loss: -0.05322173982858658, purity_loss: 5.1617960929870605, regularization_loss: 0.09719744324684143
12:52:16 --- INFO: epoch: 82, batch: 1, loss: 5.199120044708252, modularity_loss: -0.053999029099941254, purity_loss: 5.155378341674805, regularization_loss: 0.09774089604616165
12:52:16 --- INFO: epoch: 83, batch: 1, loss: 5.192173480987549, modularity_loss: -0.054811351001262665, purity_loss: 5.148687362670898, regularization_loss: 0.09829740226268768
12:52:17 --- INFO: epoch: 84, batch: 1, loss: 5.18492317199707, modularity_loss: -0.05565829202532768, purity_loss: 5.141714096069336, regularization_loss: 0.09886721521615982
12:52:17 --- INFO: epoch: 85, batch: 1, loss: 5.177361011505127, modularity_loss: -0.056539375334978104, purity_loss: 5.1344499588012695, regularization_loss: 0.09945060312747955
12:52:17 --- INFO: epoch: 86, batch: 1, loss: 5.169497013092041, modularity_loss: -0.05745353922247887, purity_loss: 5.126903057098389, regularization_loss: 0.10004764050245285
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12:57:17 --- INFO: epoch: 966, batch: 1, loss: 2.124150037765503, modularity_loss: -0.12841588258743286, purity_loss: 2.1557106971740723, regularization_loss: 0.0968552827835083
12:57:18 --- INFO: epoch: 967, batch: 1, loss: 2.1239798069000244, modularity_loss: -0.12841959297657013, purity_loss: 2.1555445194244385, regularization_loss: 0.09685491025447845
12:57:18 --- INFO: epoch: 968, batch: 1, loss: 2.1238105297088623, modularity_loss: -0.1284237653017044, purity_loss: 2.1553807258605957, regularization_loss: 0.09685366600751877
12:57:18 --- INFO: epoch: 969, batch: 1, loss: 2.123641014099121, modularity_loss: -0.12842881679534912, purity_loss: 2.1552186012268066, regularization_loss: 0.09685129672288895
12:57:19 --- INFO: epoch: 970, batch: 1, loss: 2.1234729290008545, modularity_loss: -0.12843453884124756, purity_loss: 2.155059576034546, regularization_loss: 0.09684788435697556
12:57:19 --- INFO: epoch: 971, batch: 1, loss: 2.123305082321167, modularity_loss: -0.12844054400920868, purity_loss: 2.1549017429351807, regularization_loss: 0.09684385359287262
12:57:19 --- INFO: epoch: 972, batch: 1, loss: 2.1231377124786377, modularity_loss: -0.12844642996788025, purity_loss: 2.1547443866729736, regularization_loss: 0.09683964401483536
12:57:20 --- INFO: epoch: 973, batch: 1, loss: 2.1229705810546875, modularity_loss: -0.1284520924091339, purity_loss: 2.1545870304107666, regularization_loss: 0.09683558344841003
12:57:20 --- INFO: epoch: 974, batch: 1, loss: 2.122804641723633, modularity_loss: -0.12845724821090698, purity_loss: 2.1544301509857178, regularization_loss: 0.0968317911028862
12:57:20 --- INFO: epoch: 975, batch: 1, loss: 2.1226389408111572, modularity_loss: -0.12846186757087708, purity_loss: 2.1542718410491943, regularization_loss: 0.09682900458574295
12:57:21 --- INFO: epoch: 976, batch: 1, loss: 2.122474193572998, modularity_loss: -0.1284659057855606, purity_loss: 2.1541128158569336, regularization_loss: 0.09682735055685043
12:57:21 --- INFO: epoch: 977, batch: 1, loss: 2.122310161590576, modularity_loss: -0.12846966087818146, purity_loss: 2.1539535522460938, regularization_loss: 0.09682634472846985
12:57:21 --- INFO: epoch: 978, batch: 1, loss: 2.1221463680267334, modularity_loss: -0.12847359478473663, purity_loss: 2.153794527053833, regularization_loss: 0.09682553261518478
12:57:22 --- INFO: epoch: 979, batch: 1, loss: 2.121983528137207, modularity_loss: -0.12847808003425598, purity_loss: 2.153637647628784, regularization_loss: 0.09682396054267883
12:57:22 --- INFO: epoch: 980, batch: 1, loss: 2.1218209266662598, modularity_loss: -0.12848322093486786, purity_loss: 2.153482675552368, regularization_loss: 0.09682149440050125
12:57:22 --- INFO: epoch: 981, batch: 1, loss: 2.1216585636138916, modularity_loss: -0.12848885357379913, purity_loss: 2.1533291339874268, regularization_loss: 0.09681829810142517
12:57:23 --- INFO: epoch: 982, batch: 1, loss: 2.121497392654419, modularity_loss: -0.12849435210227966, purity_loss: 2.1531763076782227, regularization_loss: 0.09681539237499237
12:57:23 --- INFO: epoch: 983, batch: 1, loss: 2.1213364601135254, modularity_loss: -0.12849989533424377, purity_loss: 2.1530239582061768, regularization_loss: 0.09681239724159241
12:57:23 --- INFO: epoch: 984, batch: 1, loss: 2.121175527572632, modularity_loss: -0.12850527465343475, purity_loss: 2.1528713703155518, regularization_loss: 0.09680943191051483
12:57:24 --- INFO: epoch: 985, batch: 1, loss: 2.121015787124634, modularity_loss: -0.1285107433795929, purity_loss: 2.1527199745178223, regularization_loss: 0.09680651128292084
12:57:24 --- INFO: epoch: 986, batch: 1, loss: 2.1208560466766357, modularity_loss: -0.12851594388484955, purity_loss: 2.1525683403015137, regularization_loss: 0.0968036875128746
12:57:24 --- INFO: epoch: 987, batch: 1, loss: 2.120697021484375, modularity_loss: -0.12852081656455994, purity_loss: 2.152416706085205, regularization_loss: 0.09680116921663284
12:57:25 --- INFO: epoch: 988, batch: 1, loss: 2.1205384731292725, modularity_loss: -0.12852561473846436, purity_loss: 2.1522650718688965, regularization_loss: 0.09679898619651794
12:57:25 --- INFO: epoch: 989, batch: 1, loss: 2.120380401611328, modularity_loss: -0.12853023409843445, purity_loss: 2.152114152908325, regularization_loss: 0.09679648280143738
12:57:25 --- INFO: epoch: 990, batch: 1, loss: 2.120222806930542, modularity_loss: -0.12853510677814484, purity_loss: 2.1519641876220703, regularization_loss: 0.09679370373487473
12:57:26 --- INFO: epoch: 991, batch: 1, loss: 2.120065450668335, modularity_loss: -0.12853939831256866, purity_loss: 2.151813268661499, regularization_loss: 0.0967915877699852
12:57:26 --- INFO: epoch: 992, batch: 1, loss: 2.1199095249176025, modularity_loss: -0.1285426765680313, purity_loss: 2.1516613960266113, regularization_loss: 0.0967908650636673
12:57:27 --- INFO: epoch: 993, batch: 1, loss: 2.119753122329712, modularity_loss: -0.12854547798633575, purity_loss: 2.151507616043091, regularization_loss: 0.0967910960316658
12:57:27 --- INFO: epoch: 994, batch: 1, loss: 2.119597911834717, modularity_loss: -0.12854856252670288, purity_loss: 2.151355504989624, regularization_loss: 0.09679096192121506
12:57:27 --- INFO: epoch: 995, batch: 1, loss: 2.119443416595459, modularity_loss: -0.12855219841003418, purity_loss: 2.1512057781219482, regularization_loss: 0.09678995609283447
12:57:28 --- INFO: epoch: 996, batch: 1, loss: 2.1192891597747803, modularity_loss: -0.1285567581653595, purity_loss: 2.1510579586029053, regularization_loss: 0.09678801894187927
12:57:28 --- INFO: epoch: 997, batch: 1, loss: 2.1191353797912598, modularity_loss: -0.12856188416481018, purity_loss: 2.150912046432495, regularization_loss: 0.09678509831428528
12:57:28 --- INFO: epoch: 998, batch: 1, loss: 2.1189820766448975, modularity_loss: -0.12856729328632355, purity_loss: 2.1507675647735596, regularization_loss: 0.09678174555301666
12:57:29 --- INFO: epoch: 999, batch: 1, loss: 2.1188294887542725, modularity_loss: -0.12857240438461304, purity_loss: 2.150623321533203, regularization_loss: 0.09677863121032715
12:57:29 --- INFO: epoch: 1000, batch: 1, loss: 2.1186773777008057, modularity_loss: -0.12857726216316223, purity_loss: 2.1504788398742676, regularization_loss: 0.09677580744028091
12:57:29 --- INFO: Training process end.
12:57:29 --- INFO: Evaluating process start.
12:57:29 --- INFO: Evaluate loss, {'modularity_loss': -0.12858177721500397, 'purity_loss': 2.150334119796753, 'regularization_loss': 0.0967731848359108, 'total_loss': 2.118525505065918}
12:57:29 --- INFO: Evaluating process end.
12:57:29 --- INFO: Predicting process start.
12:57:29 --- INFO: Generating prediction results for E12_E1S3.
12:57:30 --- INFO: Generating prediction results for E14_E1S3.
12:57:30 --- INFO: Generating prediction results for E16_E1S3.
12:57:31 --- INFO: Generating prediction results for E16_E2S6.
12:57:31 --- INFO: Generating prediction results for E16_E2S7.
12:57:33 --- INFO: Predicting process end.
12:57:34 --- INFO: --------------------- GNN end ----------------------
GNN results visualization¶
ana_data = AnaData(vis_options)
spatial niche cluster loadings¶
from ONTraC.analysis.niche_cluster import plot_niche_cluster_loadings_dataset_from_anadata
fig, axes = plot_niche_cluster_loadings_dataset_from_anadata(ana_data=ana_data)
fig.savefig('figures/Spatial_niche_clustering_loadings.png', dpi=100)
12:57:34 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
niche cluster proportions¶
from ONTraC.analysis.niche_cluster import plot_cluster_proportion_from_anadata
fig, ax = plot_cluster_proportion_from_anadata(ana_data=ana_data)
fig.savefig('figures/Pie_niche_cluster_proportions.png', dpi=100)
12:57:42 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
niche cluster connectivities¶
from ONTraC.analysis.niche_cluster import plot_niche_cluster_connectivity_from_anadata
fig, axes = plot_niche_cluster_connectivity_from_anadata(ana_data=ana_data)
fig.savefig('figures/Graph_niche_cluster_conn.png', dpi=100)
12:57:42 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
cell type loadings in each niche cluster¶
from ONTraC.analysis.cell_type import plot_cell_type_loading_in_niche_clusters_from_anadata
g = plot_cell_type_loading_in_niche_clusters_from_anadata(ana_data=ana_data)
g.savefig('figures/Hist_cell_type_loadings_in_niche_cluster.png', dpi=100)
12:57:43 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
from ONTraC.analysis.cell_type import plot_cell_type_com_in_niche_clusters_from_anadata
fig, ax = plot_cell_type_com_in_niche_clusters_from_anadata(ana_data=ana_data)
fig.savefig('figures/Heatmap_cell_type_composition_in_each_niche_cluster.png', dpi=100)
12:57:44 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
This heatmap show the cell type composition within each niche cluster. Sum of each row equals to 1.
from ONTraC.analysis.cell_type import plot_cell_type_dis_across_niche_cluster_from_anadata
fig, ax = plot_cell_type_dis_across_niche_cluster_from_anadata(ana_data=ana_data)
fig.savefig('figures/Heatmap_cell_type_distribution_across_niche_clusters.png', dpi=100)
12:57:44 --- WARNING: Cannot find niche cluster score file: ./output/stereo_seq_NT/niche_cluster_score.csv.
This heatmap show the cell type distribution across niche clusters. Sum of each column equals to 1.
niche trajectory construction¶
construct niche trajectory¶
niche_trajectory_construct(options=run_options)
12:57:44 --- INFO: ----------------- Niche trajectory ------------------
12:57:44 --- INFO: Loading consolidate s_array and out_adj_array...
12:57:44 --- INFO: Maximum number of cell in one sample is: 7300.
12:57:44 --- INFO: Processing sample 1 of 5: E12_E1S3
12:57:44 --- INFO: Processing sample 2 of 5: E14_E1S3
12:57:44 --- INFO: Processing sample 3 of 5: E16_E1S3
12:57:44 --- INFO: Processing sample 4 of 5: E16_E2S6
Processing...
12:57:44 --- INFO: Processing sample 5 of 5: E16_E2S7
12:57:45 --- INFO: Processing sample 1 of 5: E12_E1S3
12:57:45 --- INFO: Processing sample 2 of 5: E14_E1S3
12:57:45 --- INFO: Processing sample 3 of 5: E16_E1S3
12:57:45 --- INFO: Processing sample 4 of 5: E16_E2S6
12:57:45 --- INFO: Processing sample 5 of 5: E16_E2S7
Done!
12:57:45 --- INFO: Calculating NTScore for each niche.
12:57:45 --- INFO: Finding niche trajectory with maximum connectivity using Brute Force.
12:57:45 --- INFO: Calculating NTScore for each niche cluster based on the trajectory path.
12:57:45 --- INFO: Projecting NTScore from niche-level to cell-level.
12:57:45 --- INFO: Output NTScore tables.
12:57:46 --- INFO: --------------- Niche trajectory end ----------------
niche trajectory based visualization¶
ana_data = AnaData(vis_options)
spatial NT scores¶
#cell-level NT score
from ONTraC.analysis.spatial import plot_cell_NT_score_dataset_from_anadata
fig, ax = plot_cell_NT_score_dataset_from_anadata(ana_data)
fig.savefig('figures/Spatial_cell_NT_score.png', dpi=100)
cell-level NT score distribution for each cell type¶
from ONTraC.analysis.cell_type import plot_violin_cell_type_along_NT_score_from_anadata
fig, ax = plot_violin_cell_type_along_NT_score_from_anadata(ana_data=ana_data,
order=['RGC', 'GlioB', 'NeuB', 'GluNeuB', 'GluNeu', 'GABA', 'Ery', 'Endo', 'Fibro', 'Basal'], # change based on your own dataset or remove this line
)
fig.savefig('figures/cell_type_along_NT_score_violin.png', dpi=100)
cell type propotion along cell-level NT score¶
from ONTraC.analysis.cell_type import plot_kde_cell_type_along_NT_score_from_anadata
fig, ax = plot_kde_cell_type_along_NT_score_from_anadata(ana_data=ana_data)
fig.savefig('figures/cell_type_along_NT_score_kde.png', dpi=100)
from ONTraC.analysis.cell_type import plot_hist_cell_type_along_NT_score_from_anadata
fig, ax = plot_hist_cell_type_along_NT_score_from_anadata(ana_data=ana_data)
fig.savefig('figures/cell_type_along_NT_score_hist.png', dpi=100)
We can reorder niche clusters by their position in trajectory¶
spatial niche cluster loadings¶
from ONTraC.analysis.niche_cluster import plot_niche_cluster_loadings_dataset_from_anadata
fig, axes = plot_niche_cluster_loadings_dataset_from_anadata(ana_data=ana_data)
fig.savefig('figures/Spatial_niche_clustering_loadings_with_order.png', dpi=100)
niche cluster connectivities¶
from ONTraC.analysis.niche_cluster import plot_niche_cluster_connectivity_from_anadata
fig, axes = plot_niche_cluster_connectivity_from_anadata(ana_data=ana_data)
fig.savefig('figures/Graph_niche_cluster_conn_with_order.png', dpi=100)
cell type loadings in each niche cluster¶
from ONTraC.analysis.cell_type import plot_cell_type_loading_in_niche_clusters_from_anadata
g = plot_cell_type_loading_in_niche_clusters_from_anadata(ana_data=ana_data)
g.savefig('figures/Hist_cell_type_loadings_in_niche_cluster_with_order.png', dpi=100)
from ONTraC.analysis.cell_type import plot_cell_type_com_in_niche_clusters_from_anadata
fig, ax = plot_cell_type_com_in_niche_clusters_from_anadata(ana_data=ana_data)
fig.savefig('figures/Heatmap_cell_type_composition_in_each_niche_cluster_with_order.png', dpi=100)
This heatmap show the cell type composition within each niche cluster. Sum of each row equals to 1.
from ONTraC.analysis.cell_type import plot_cell_type_dis_across_niche_cluster_from_anadata
fig, ax = plot_cell_type_dis_across_niche_cluster_from_anadata(ana_data=ana_data)
fig.savefig('figures/Heatmap_cell_type_distribution_across_niche_clusters_with_order.png', dpi=100)
This heatmap show the cell type distribution across niche clusters. Sum of each column equals to 1.