Generate regulator representations from ChromBERT¶
Note: The remaining examples show Bash command-line usage only for extracting regulator embeddings.
embed_region subcommand: Generate regulator embeddings for input regulators across user-specified regions.
For the Python API, see `examples/api/embed_regulator.ipynb <../api/embed_regulator.ipynb>`__.
If you need to use Apptainer container, please refer to the `apptainer_use.ipynb <apptainer_use.ipynb>`__ tutorial for detailed instructions on using apptainer exec with chrombert-tools.
For more details, please refer to the `embed_regulator <https://chrombert-tools.readthedocs.io/en/latest/commands/embed_regulator.html>`__ command documentation.
[1]:
### options parameter
!chrombert-tools embed_regulator -h
Usage: chrombert-tools embed_regulator [OPTIONS]
Extract regulator embeddings on specified regions. Supports both general and
cell-specific modes.
Options:
--region FILE Region file. [required]
--regulator TEXT Regulators of interest, e.g. EZH2 or
EZH2;BRD4. Use ';' to separate multiple
regulators. [required]
--cell-type-bw FILE Cell type accessibility BigWig file. Used
for cell-specific mode.
--cell-type-peak FILE Cell type accessibility Peak BED file. Used
for cell-specific mode.
--ft-ckpt FILE Fine-tuned checkpoint. If provided, use
cell-specific model and skip fine-tuning.
--odir DIRECTORY Output directory. [default: ./output]
--oname TEXT Output name of the regulator embeddings.
[default: regulator_emb]
--genome [hg38|mm10] Genome. [default: hg38]
--resolution [1kb|200bp|2kb|4kb]
Resolution. [default: 1kb]
--mode [fast|full] Used when training cell-specific model.
[default: fast]
--batch-size INTEGER Batch size. [default: 4]
--num-workers INTEGER Dataloader workers. [default: 8]
--chrombert-cache-dir DIRECTORY
ChromBERT cache dir (contains config/
checkpoint/ etc). [default:
~/.cache/chrombert/data]
-h, --help Show this message and exit.
Generate regulator embeddings (pre-trained and general)¶
[ ]:
%%bash
# --region: focus regions
# --regulator: focus regulators
# --odir: output directory
# --genome: genome
# --resolution: resolution
chrombert-tools embed_regulator \
--region '../data/CTCF_ENCFF664UGR_sample100.bed' \
--regulator "EZH2;BRD4;CTCF;FOXA3;myod1;myF5" \
--odir "./output_emb_regulator_1kb" \
--genome "hg38" \
--resolution "1kb"
Region summary - total: 100, overlapping with ChromBERT: 100 (one region may overlap multiple ChromBERT regions, we keep overlaps with ≥50% coverage of either the ChromBERT bin or the input region), non-overlapping: 0
Note: All regulator names were converted to lowercase for matching.
Regulator count summary - requested: 6, matched in ChromBERT: 5, not found: 1, not found regulator: ['foxa3']
ChromBERT regulators: /mnt/Storage/home/chenqianqian/.cache/chrombert/data/config/hg38_6k_regulators_list.txt
Load pretrained ckpt /mnt/Storage/home/chenqianqian/.cache/chrombert/data/checkpoint/hg38_6k_1kb_pretrain.ckpt successfully!
Your supervised_file does not contain the 'label' column. Please verify whether ground truth column ('label') is required. If it is not needed, you may disregard this message.
Your supervised_file does not contain the 'label' column. Please verify whether ground truth column ('label') is required. If it is not needed, you may disregard this message.
Computing regulator embeddings: 100%|██████████| 25/25 [00:05<00:00, 4.91it/s]
Finished!
Focus region summary - total: 100, overlapping with ChromBERT: 100, non-overlapping: 0
Overlapping regions BED file: ./output_emb_regulator_1kb/overlap_region.bed
Non-overlapping regions BED file: ./output_emb_regulator_1kb/no_overlap_region.bed
Mean regulator embeddings saved to: ./output_emb_regulator_1kb/mean_regulator_emb.pkl
Region-aware regulator embeddings saved to: ./output_emb_regulator_1kb/region_aware_regulator_emb.hdf5
Embedding type: general
[ ]:
# regulator_emb_mean.pkl: one 768-dim vector per regulator (averaged across all regions)
import pickle
with open("./output_emb_regulator_1kb/mean_regulator_emb.pkl", "rb") as f:
mean_regulator_emb_dict = pickle.load(f)
for key, value in mean_regulator_emb_dict.items():
print(key, value.shape)
ezh2 (768,)
myod1 (768,)
brd4 (768,)
ctcf (768,)
myf5 (768,)
[6]:
# Python dictionary mapping each matched regulator to its Region-aware regulator embeddings
# shape (N_regions, 768)
import h5py
with h5py.File("./output_emb_regulator_1kb/region_aware_regulator_emb.hdf5", "r") as f:
print(f.keys())
print(f['emb'].keys())
for i in f['emb'].keys():
print(i, f['emb'][i].shape)
<KeysViewHDF5 ['emb', 'region']>
<KeysViewHDF5 ['brd4', 'ctcf', 'ezh2', 'myf5', 'myod1']>
brd4 (100, 768)
ctcf (100, 768)
ezh2 (100, 768)
myf5 (100, 768)
myod1 (100, 768)
Generate cell-type-specific embeddings using a fine-tuned checkpoint¶
We use a fine-tuned checkpoint as input.
The embed_regulator subcommand uses this checkpoint to generate context-specific embeddings.
[ ]:
# # Download example data
# # Myoblast and fibroblast data: ATAC-seq bigWig and peak files
# import subprocess
# import os
# if not os.path.exists('../data/myoblast_ENCFF647RNC_peak.bed'):
# cmd = f'wget https://www.encodeproject.org/files/ENCFF647RNC/@@download/ENCFF647RNC.bed.gz -O ../data/myoblast_ENCFF647RNC_peak.bed.gz'
# subprocess.run(cmd, shell=True)
# cmd = f"gzip -d ../data/myoblast_ENCFF647RNC_peak.bed.gz"
# subprocess.run(cmd, shell=True)
# if not os.path.exists('../data/myoblast_ENCFF149ERN_signal.bigwig'):
# cmd = f'wget https://www.encodeproject.org/files/ENCFF149ERN/@@download/ENCFF149ERN.bigWig -O ../data/myoblast_ENCFF149ERN_signal.bigwig'
# subprocess.run(cmd, shell=True)
## fine-tuned a cell-type-specific model
# '''
# --odir: output directory
# --acc_signal1: cell-type-specific accessibility signal
# --acc_peak1: cell-type-specific peak
# --genome: genome
# --resolution: resolution
# '''
# !chrombert-tools region_activity_regression \
# --odir "./output_cell_specific_emb_train" \
# --acc_signal1 "../data/myoblast_ENCFF149ERN_signal.bigwig" \
# --acc_peak1 "../data/myoblast_ENCFF647RNC_peak.bed" \
# --genome "hg38" \
# --resolution "1kb"
[ ]:
import glob
ft_ckpt_dir = "./output_cell_specific_emb_train/train/**/*.ckpt" # Use checkpoints from embed_region.ipynb if available; otherwise, run the code above first
ft_ckpt = glob.glob(ft_ckpt_dir, recursive=True)[0]
ft_ckpt
'./output_cell_specific_emb_train/train/try_00_seed_55/lightning_logs/lightning_logs/version_0/checkpoints/epoch=0-step=37.ckpt'
[ ]:
# --region: focus regions
# --regulator: focus regulators
# --odir: output directory
# --genome: genome
# --resolution: resolution
# --ft-ckpt: path to the fine-tuned checkpoint
!chrombert-tools embed_regulator \
--region '../data/CTCF_ENCFF664UGR_sample100.bed' \
--regulator "EZH2;BRD4;CTCF;FOXA3;myod1;myF5" \
--odir "./output_emb_regulator_1kb_load_ft" \
--genome "hg38" \
--resolution "1kb" \
--ft-ckpt {ft_ckpt}
Region summary - total: 100, overlapping with ChromBERT: 100 (one region may overlap multiple ChromBERT regions, we keep overlaps with ≥50% coverage of either the ChromBERT bin or the input region), non-overlapping: 0
Note: All regulator names were converted to lowercase for matching.
Regulator count summary - requested: 6, matched in ChromBERT: 5, not found: 1, not found regulator: ['foxa3']
ChromBERT regulators: /mnt/Storage/home/chenqianqian/.cache/chrombert/data/config/hg38_6k_regulators_list.txt
Using provided fine-tuned checkpoint: ./output_cell_specific_emb_train/train/try_00_seed_55/lightning_logs/lightning_logs/version_0/checkpoints/epoch=0-step=37.ckpt
Load pretrained ckpt /mnt/Storage/home/chenqianqian/.cache/chrombert/data/checkpoint/hg38_6k_1kb_pretrain.ckpt successfully!
Loading checkpoint from ./output_cell_specific_emb_train/train/try_00_seed_55/lightning_logs/lightning_logs/version_0/checkpoints/epoch=0-step=37.ckpt
Loading from pl module, remove prefix 'model.'
Loading from pl module, replace 'pretrain_model' with 'pretrain_model.chrombert'
Loaded 111/111 parameters
Your supervised_file does not contain the 'label' column. Please verify whether ground truth column ('label') is required. If it is not needed, you may disregard this message.
Your supervised_file does not contain the 'label' column. Please verify whether ground truth column ('label') is required. If it is not needed, you may disregard this message.
Computing regulator embeddings: 100%|███████████| 25/25 [00:05<00:00, 4.72it/s]
Finished!
Focus region summary - total: 100, overlapping with ChromBERT: 100, non-overlapping: 0
Overlapping regions BED file: ./output_emb_regulator_1kb_load_ft/overlap_region.bed
Non-overlapping regions BED file: ./output_emb_regulator_1kb_load_ft/no_overlap_region.bed
Mean regulator embeddings saved to: ./output_emb_regulator_1kb_load_ft/mean_regulator_emb.pkl
Region-aware regulator embeddings saved to: ./output_emb_regulator_1kb_load_ft/region_aware_regulator_emb.hdf5
Embedding type: cell-specific
[10]:
# regulator_emb_mean.pkl: one 768-dim vector per regulator (averaged across all regions)
import pickle
with open("./output_emb_regulator_1kb_load_ft/mean_regulator_emb.pkl", "rb") as f:
mean_regulator_emb_dict2 = pickle.load(f)
for key, value in mean_regulator_emb_dict2.items():
print(key, value.shape)
myod1 (768,)
brd4 (768,)
ezh2 (768,)
myf5 (768,)
ctcf (768,)
[16]:
mean_regulator_emb_dict["myod1"]
[16]:
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[15]:
mean_regulator_emb_dict2["myod1"]
[15]:
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[7]:
# Python dictionary mapping each matched regulator to its Region-aware regulator embeddings
# shape (N_regions, 768)
import h5py
with h5py.File("./output_emb_regulator_1kb_load_ft/region_aware_regulator_emb.hdf5", "r") as f:
print(f.keys())
print(f['emb'].keys())
for i in f['emb'].keys():
print(i, f['emb'][i].shape)
<KeysViewHDF5 ['emb', 'region']>
<KeysViewHDF5 ['brd4', 'ctcf', 'ezh2', 'myf5', 'myod1']>
brd4 (100, 768)
ctcf (100, 768)
ezh2 (100, 768)
myf5 (100, 768)
myod1 (100, 768)
Generate cell-type-specific embeddings¶
We use cell-type-specific chromatin accessibility peak and signal files as input.
The embed_regulator subcommand uses these data to build a cell-type-specific model and generate cell-type-specific embeddings.
[ ]:
# --region: focus regions
# --regulator: focus regulators
# --odir: output directory
# --genome: genome
# --resolution: resolution
# --cell-type-bw: Cell-type-specific bigwig file
# --cell-type-peak: Cell-type-specific peak file
!chrombert-tools embed_regulator \
--region '../data/CTCF_ENCFF664UGR_sample100.bed' \
--regulator "EZH2;BRD4;CTCF;FOXA3;myod1;myF5" \
--odir "./output_emb_regulator_1kb_load_ft" \
--genome "hg38" \
--resolution "1kb" \
--cell-type-bw ../data/myoblast_ENCFF149ERN_signal.bigwig \
--cell-type-peak ../data/myoblast_ENCFF647RNC_peak.bed