Geneformer gene embeddings extraction and building a gene co-expression/regulation network (GRN)¶
Here we show how to extract gene embeddings from the pre-trained Geneformer 10M model, and how to build a gene co-expression network from gene embeddings for selected genes.
The single cell sequencing data set is from CellxGene, the same as used in the cell type annotation tutorial. The gene co-expression network is adapted from a scGPT tutorial.
First, we download the dataset from czi that we are interested in, and then create the requisite sc_memmap dataset object. Then we extract gene embeddings using infer_geneformer. After that, we can get the gene embedding values to build gene co-expression network based on cosine similarity.
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# NBVAL_CHECK_OUTPUT
import cellxgene_census
CENSUS_VERSION = "2023-12-15"
with cellxgene_census.open_soma(census_version=CENSUS_VERSION) as census:
adata = cellxgene_census.get_anndata(
census,
"Homo sapiens",
obs_value_filter='dataset_id=="8e47ed12-c658-4252-b126-381df8d52a3d"',
)
uq_cells = sorted(adata.obs["cell_type"].unique().tolist())
uq_cells
# NBVAL_CHECK_OUTPUT
import cellxgene_census
CENSUS_VERSION = "2023-12-15"
with cellxgene_census.open_soma(census_version=CENSUS_VERSION) as census:
adata = cellxgene_census.get_anndata(
census,
"Homo sapiens",
obs_value_filter='dataset_id=="8e47ed12-c658-4252-b126-381df8d52a3d"',
)
uq_cells = sorted(adata.obs["cell_type"].unique().tolist())
uq_cells
Out[20]:
['B cell', 'CD4-positive, alpha-beta T cell', 'CD8-positive, alpha-beta T cell', 'IgA plasma cell', 'IgG plasma cell', 'M cell of gut', 'T follicular helper cell', 'activated CD4-positive, alpha-beta T cell, human', 'conventional dendritic cell', 'dendritic cell, human', 'endothelial cell of artery', 'endothelial cell of lymphatic vessel', 'enterocyte', 'enteroendocrine cell', 'fibroblast', 'gamma-delta T cell', 'glial cell', 'intestinal crypt stem cell', 'intestinal tuft cell', 'intestine goblet cell', 'mast cell', 'memory B cell', 'monocyte', 'myeloid cell', 'myofibroblast cell', 'pericyte', 'plasma cell', 'plasmacytoid dendritic cell', 'regulatory T cell', 'transit amplifying cell', 'vein endothelial cell']
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# NBVAL_CHECK_OUTPUT
import random
from contextlib import contextmanager
@contextmanager
def random_seed(seed: int):
"""Context manager to set the random seed for reproducibility."""
state = random.getstate()
random.seed(seed)
try:
yield
finally:
# Go back to previous state
random.setstate(state)
with random_seed(32):
indices = list(range(len(adata)))
random.shuffle(indices)
micro_batch_size: int = 32
num_steps: int = 256
selection = sorted(indices[: micro_batch_size * num_steps])
# NOTE: there's a current constraint that predict_step needs to be a function of micro-batch-size.
# this is something we are working on fixing. A quick hack is to set micro-batch-size=1, but this is
# slow. In this notebook we are going to use mbs=32 and subsample the anndata.
adata = adata[selection].copy() # so it's not a view
adata.shape
# NBVAL_CHECK_OUTPUT
import random
from contextlib import contextmanager
@contextmanager
def random_seed(seed: int):
"""Context manager to set the random seed for reproducibility."""
state = random.getstate()
random.seed(seed)
try:
yield
finally:
# Go back to previous state
random.setstate(state)
with random_seed(32):
indices = list(range(len(adata)))
random.shuffle(indices)
micro_batch_size: int = 32
num_steps: int = 256
selection = sorted(indices[: micro_batch_size * num_steps])
# NOTE: there's a current constraint that predict_step needs to be a function of micro-batch-size.
# this is something we are working on fixing. A quick hack is to set micro-batch-size=1, but this is
# slow. In this notebook we are going to use mbs=32 and subsample the anndata.
adata = adata[selection].copy() # so it's not a view
adata.shape
Out[21]:
(8192, 60664)
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import shutil
from bionemo.core import BIONEMO_CACHE_DIR
cleanup: bool = True
notebook_workdir = BIONEMO_CACHE_DIR / "notebook_tutorials" / "geneformer_geneembeddings-GRN"
if cleanup and notebook_workdir.exists():
shutil.rmtree(notebook_workdir)
notebook_workdir.mkdir(parents=True, exist_ok=True)
input_dir = notebook_workdir / "celltype-bench-dataset-input"
data_dir = notebook_workdir / "celltype-bench-dataset"
input_dir.mkdir(parents=True, exist_ok=True)
h5ad_outfile = input_dir / "hs-celltype-bench.h5ad"
adata.write_h5ad(h5ad_outfile)
import shutil
from bionemo.core import BIONEMO_CACHE_DIR
cleanup: bool = True
notebook_workdir = BIONEMO_CACHE_DIR / "notebook_tutorials" / "geneformer_geneembeddings-GRN"
if cleanup and notebook_workdir.exists():
shutil.rmtree(notebook_workdir)
notebook_workdir.mkdir(parents=True, exist_ok=True)
input_dir = notebook_workdir / "celltype-bench-dataset-input"
data_dir = notebook_workdir / "celltype-bench-dataset"
input_dir.mkdir(parents=True, exist_ok=True)
h5ad_outfile = input_dir / "hs-celltype-bench.h5ad"
adata.write_h5ad(h5ad_outfile)
Your results are saved in this directory.
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print(notebook_workdir)
print(notebook_workdir)
/root/.cache/bionemo/notebook_tutorials/geneformer_geneembeddings-GRN
Create the scmemmap object, check outputs¶
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!convert_h5ad_to_scdl --data-path {input_dir} --save-path {data_dir}
!convert_h5ad_to_scdl --data-path {input_dir} --save-path {data_dir}
Importantly, the .npy files are used by BioNeMo dataset object. features.csv contains the metadata requested, in this case cell_type. It's important that the output of our model has the same order as features.csv, as this contains the labels used in the following benchmark.
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# NBVAL_CHECK_OUTPUT
from glob import glob
files = sorted(
[f.split("/")[-1] for f in glob(str(data_dir / "*"))]
) # strip off the directory name and sort for the test
files
# NBVAL_CHECK_OUTPUT
from glob import glob
files = sorted(
[f.split("/")[-1] for f in glob(str(data_dir / "*"))]
) # strip off the directory name and sort for the test
files
Out[25]:
['col_ptr.npy', 'data.npy', 'features', 'metadata.json', 'row_ptr.npy', 'version.json']
Import geneformer-10M model for infer geneformer to retrieve gene embeddings
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from bionemo.core.data.load import load
# 10m checkpoint
geneformer_10m = load("geneformer/10M_240530:2.0")
from bionemo.core.data.load import load
# 10m checkpoint
geneformer_10m = load("geneformer/10M_240530:2.0")
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result_path_10m = notebook_workdir / "results_10m.pt"
result_path_10m = notebook_workdir / "results_10m.pt"
Execute inference¶
We run inference on the Geneformer model downloaded by load(...) function in a previous cell. We have a one-off inference script for geneformer that is installed as part of the bionemo-geneformer package. See the pyproject.toml in the source directory if you are curious or want to use this as a template to make your own inference scripts. This script should work for any sc_memmap converted geneformer dataset, and geneformer bionemo2 model checkpoint though.
In order to extract gene embeddings, you also need to specify --include-input-ids and --include-hiddens
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!infer_geneformer \
--data-dir {data_dir} \
--checkpoint-path {geneformer_10m} \
--results-path {result_path_10m} \
--micro-batch-size {micro_batch_size} \
--seq-len 2048 \
--num-dataset-workers 10 \
--num-gpus 1 \
--include-input-ids \
--include-hiddens \
--include-gene-embeddings
!infer_geneformer \
--data-dir {data_dir} \
--checkpoint-path {geneformer_10m} \
--results-path {result_path_10m} \
--micro-batch-size {micro_batch_size} \
--seq-len 2048 \
--num-dataset-workers 10 \
--num-gpus 1 \
--include-input-ids \
--include-hiddens \
--include-gene-embeddings
Could not find the bitsandbytes CUDA binary at PosixPath('/usr/local/lib/python3.12/dist-packages/bitsandbytes/libbitsandbytes_cuda129.so')
The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
[NeMo W 2025-06-02 14:12:30 nemo_logging:405] Tokenizer vocab file: /root/.cache/bionemo/d8e3ea569bc43768c24aa651aff77722df202078415528497c22394046b08cc3-singlecell-scdltestdata-20241203.tar.gz.untar/cellxgene_2023-12-15_small_processed_scdl/train/geneformer.vocab already exists. Overwriting...
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] No checksum provided, filename exists. Assuming it is complete.
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Resource already exists, skipping download: https://huggingface.co/ctheodoris/Geneformer/resolve/main/geneformer/gene_dictionaries_30m/gene_name_id_dict_gc30M.pkl?download=true
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] No checksum provided, filename exists. Assuming it is complete.
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] No checksum provided, filename exists. Assuming it is complete.
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Resource already exists, skipping download: https://huggingface.co/ctheodoris/Geneformer/resolve/main/geneformer/gene_dictionaries_30m/gene_median_dictionary_gc30M.pkl?download=true
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] No checksum provided, filename exists. Assuming it is complete.
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] *************** Preprocessing Finished ************
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Fixing mis-match between ddp-config & mcore-optimizer config
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has data parallel group : [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has combined group of data parallel and context parallel : [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All data parallel group ranks with context parallel combined: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Ranks 0 has data parallel rank: 0
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has context parallel group: [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All context parallel group ranks: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Ranks 0 has context parallel rank: 0
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has model parallel group: [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All model parallel group ranks: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has tensor model parallel group: [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All tensor model parallel group ranks: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has tensor model parallel rank: 0
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has pipeline model parallel group: [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has embedding group: [0]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All pipeline model parallel group ranks: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has pipeline model parallel rank 0
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] All embedding group ranks: [[0]]
[NeMo I 2025-06-02 14:12:30 nemo_logging:393] Rank 0 has embedding rank: 0
2025-06-02 14:12:30 - nemo.lightning.pytorch.strategies.megatron_strategy - INFO - Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
2025-06-02 14:12:31 - /usr/local/lib/python3.12/dist-packages/bionemo/llm/model/config.py - WARNING - Loading /root/.cache/bionemo/a27061ee347f453b1bf175e288df31e9813903ebcb4924a77ac50dccc730889d-geneformer_10M_240530_nemo2.tar.gz.untar
[NeMo W 2025-06-02 14:12:31 nemo_logging:405] Deprecated parameters to drop from <root>: ['first_pipeline_num_layers', 'last_pipeline_num_layers']
[NeMo I 2025-06-02 14:12:31 nemo_logging:393] Padded vocab_size: 25472, original vocab_size: 25429, dummy tokens: 43.
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
[NeMo W 2025-06-02 14:12:31 nemo_logging:405] Could not copy Trainer's 'max_steps' to LR scheduler's 'max_steps'. If you are not using an LR scheduler, this warning can safely be ignored.
2025-06-02 14:12:31 - megatron.core.num_microbatches_calculator - INFO - setting number of microbatches to constant 1
[NeMo I 2025-06-02 14:12:31 nemo_logging:393] > number of parameters on (tensor, pipeline) model parallel rank (0 ,0): 10300032
2025-06-02 14:12:31 - megatron.core.distributed.distributed_data_parallel - INFO - Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=True, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=True, num_distributed_optimizer_instances=1, check_for_nan_in_grad=True, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False)
2025-06-02 14:12:31 - megatron.core.distributed.param_and_grad_buffer - INFO - Number of buckets for gradient all-reduce / reduce-scatter: 1
Params for bucket 1 (10300032 elements, 10300032 padded size):
module.encoder.layers.3.mlp.linear_fc1.weight
module.encoder.layers.1.self_attention.linear_qkv.layer_norm_weight
module.embedding.word_embeddings.weight
module.encoder.layers.2.mlp.linear_fc2.weight
module.encoder.layers.3.self_attention.linear_qkv.layer_norm_bias
module.encoder.layers.1.mlp.linear_fc2.bias
module.encoder.layers.0.mlp.linear_fc1.bias
module.lm_head.layer_norm.bias
module.encoder.layers.5.mlp.linear_fc1.layer_norm_weight
module.encoder.layers.5.self_attention.linear_qkv.layer_norm_weight
module.encoder.layers.1.mlp.linear_fc1.bias
module.encoder.layers.2.mlp.linear_fc1.weight
module.encoder.layers.4.self_attention.linear_proj.weight
module.encoder.layers.1.mlp.linear_fc2.weight
module.encoder.layers.5.mlp.linear_fc1.bias
module.encoder.layers.4.mlp.linear_fc1.bias
module.encoder.layers.2.self_attention.linear_qkv.layer_norm_bias
module.encoder.layers.3.self_attention.linear_qkv.bias
module.encoder.layers.0.mlp.linear_fc1.weight
module.encoder.layers.5.mlp.linear_fc2.weight
module.encoder.layers.5.self_attention.linear_proj.weight
module.encoder.layers.1.mlp.linear_fc1.weight
module.encoder.layers.3.self_attention.linear_proj.weight
module.encoder.layers.0.self_attention.linear_qkv.layer_norm_bias
module.output_layer.bias
module.encoder.layers.4.mlp.linear_fc1.layer_norm_weight
module.encoder.layers.1.self_attention.linear_qkv.layer_norm_bias
module.encoder.layers.5.mlp.linear_fc1.weight
module.encoder.layers.4.self_attention.linear_qkv.bias
module.encoder.layers.2.self_attention.linear_qkv.bias
module.encoder.layers.2.self_attention.linear_proj.bias
module.encoder.layers.3.self_attention.linear_qkv.weight
module.encoder.layers.0.mlp.linear_fc2.weight
module.encoder.final_layernorm.bias
module.encoder.layers.5.self_attention.linear_qkv.layer_norm_bias
module.encoder.layers.3.self_attention.linear_proj.bias
module.encoder.layers.2.self_attention.linear_proj.weight
module.embedding.position_embeddings.weight
module.encoder.layers.0.self_attention.linear_qkv.bias
module.encoder.layers.3.mlp.linear_fc1.layer_norm_weight
module.encoder.layers.4.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.1.self_attention.linear_qkv.bias
module.encoder.layers.2.self_attention.linear_qkv.weight
module.encoder.layers.0.self_attention.linear_proj.weight
module.encoder.layers.1.mlp.linear_fc1.layer_norm_weight
module.encoder.final_layernorm.weight
module.encoder.layers.5.self_attention.linear_qkv.bias
module.encoder.layers.4.mlp.linear_fc1.weight
module.encoder.layers.2.mlp.linear_fc1.layer_norm_weight
module.encoder.layers.1.self_attention.linear_proj.weight
module.encoder.layers.0.self_attention.linear_qkv.weight
module.encoder.layers.3.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.4.self_attention.linear_qkv.layer_norm_weight
module.encoder.layers.1.self_attention.linear_qkv.weight
module.encoder.layers.0.mlp.linear_fc1.layer_norm_weight
module.encoder.layers.5.mlp.linear_fc2.bias
module.encoder.layers.2.mlp.linear_fc2.bias
module.lm_head.dense.weight
module.encoder.layers.5.self_attention.linear_qkv.weight
module.encoder.layers.4.self_attention.linear_qkv.weight
module.encoder.layers.2.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.1.self_attention.linear_proj.bias
module.encoder.layers.4.self_attention.linear_proj.bias
module.encoder.layers.3.self_attention.linear_qkv.layer_norm_weight
module.encoder.layers.5.self_attention.linear_proj.bias
module.encoder.layers.4.mlp.linear_fc2.weight
module.encoder.layers.4.mlp.linear_fc2.bias
module.encoder.layers.0.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.1.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.3.mlp.linear_fc1.bias
module.lm_head.dense.bias
module.encoder.layers.2.self_attention.linear_qkv.layer_norm_weight
module.encoder.layers.0.self_attention.linear_proj.bias
module.encoder.layers.3.mlp.linear_fc2.weight
module.encoder.layers.0.mlp.linear_fc2.bias
module.lm_head.layer_norm.weight
module.encoder.layers.5.mlp.linear_fc1.layer_norm_bias
module.encoder.layers.4.self_attention.linear_qkv.layer_norm_bias
module.encoder.layers.3.mlp.linear_fc2.bias
module.encoder.layers.0.self_attention.linear_qkv.layer_norm_weight
module.encoder.layers.2.mlp.linear_fc1.bias
2025-06-02 14:12:31 - megatron.core.optimizer - INFO - Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0001, min_lr=None, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.01, fp16=False, bf16=True, params_dtype=torch.float32, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=True, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=0.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=False, timers=None, config_logger_dir='')
2025-06-02 14:12:31 - root - INFO - Instantiating MegatronPretrainingSampler with total_samples: 8192 and consumed_samples: 0
2025-06-02 14:12:52 - root - INFO - Calculating gene embeddings.
2025-06-02 14:12:52 - root - INFO - hidden_states: torch.Size([8192, 2048]); input_ids: torch.Size([8192, 2048])
2025-06-02 14:13:16 - root - INFO - Number of unique gene embeddings: 17178
2025-06-02 14:13:16 - root - INFO - Finished calculating gene embeddings.
2025-06-02 14:13:22 - root - INFO - Inference predictions are stored in /root/.cache/bionemo/notebook_tutorials/geneformer_geneembeddings-GRN/results_10m.pt/predictions__rank_0.pt
dict_keys(['token_logits', 'binary_logits', 'hidden_states', 'input_ids', 'embeddings', 'gene_embeddings', 'gene_counts', 'ensembl_IDs', 'gene_symbols'])
Load gene embeddings¶
Load gene embeddings and add gene names. If executed correctly, you should be able to see gene_embeddings in predictions.keys().
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import torch
predictions = torch.load(result_path_10m / "predictions__rank_0.pt", weights_only=False)
print(predictions.keys())
import torch
predictions = torch.load(result_path_10m / "predictions__rank_0.pt", weights_only=False)
print(predictions.keys())
dict_keys(['token_logits', 'binary_logits', 'hidden_states', 'input_ids', 'embeddings', 'gene_embeddings', 'gene_counts', 'ensembl_IDs', 'gene_symbols'])
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predictions["gene_embeddings"].keys()
predictions["gene_embeddings"].keys()
Out[30]:
dict_keys([8525, 17317, 14183, 18632, 18594, 8662, 5215, 2912, 6864, 3085, 17546, 13275, 6684, 9948, 3144, 12033, 8211, 4723, 17324, 6209, 14128, 7614, 8568, 11035, 3667, 6430, 2340, 13061, 8208, 20337, 10604, 5796, 4040, 10416, 11788, 5823, 10642, 17980, 10822, 8677, 10251, 16983, 8933, 8060, 11322, 17718, 2288, 3042, 460, 1617, 4831, 972, 245, 4390, 16740, 19478, 20002, 11546, 8700, 16300, 4777, 8922, 6299, 4481, 16916, 7382, 6422, 8956, 8459, 272, 9218, 14986, 17054, 2386, 3925, 4550, 5337, 14172, 7058, 5541, 2260, 10363, 7505, 19826, 3505, 5040, 12431, 8259, 3478, 6241, 3039, 6767, 11051, 8433, 14040, 22565, 5850, 1473, 21022, 15279, 7160, 12079, 7830, 2990, 255, 14959, 21179, 3655, 19904, 461, 4842, 625, 13919, 6153, 16391, 6063, 12575, 1150, 8839, 9503, 10975, 6739, 3547, 13774, 8234, 11411, 7728, 9145, 8168, 14682, 13757, 855, 8011, 5549, 4353, 17227, 5909, 11555, 15334, 13040, 1132, 7355, 2654, 4340, 6061, 570, 2165, 10236, 17074, 1170, 12246, 18585, 1528, 3194, 1914, 11999, 3018, 5357, 5669, 1459, 9602, 3983, 24273, 13083, 11643, 13646, 4506, 4173, 6433, 1903, 11122, 9136, 13065, 16157, 3795, 10503, 6098, 12512, 12463, 9066, 16624, 3553, 12185, 3793, 9032, 10355, 271, 4625, 17141, 5778, 2505, 12073, 13404, 5153, 9943, 6732, 338, 3620, 6716, 5869, 8920, 975, 2631, 12958, 4068, 13417, 15643, 17788, 8781, 18986, 13308, 661, 3333, 2807, 3084, 17949, 5037, 13780, 4983, 7348, 10412, 10045, 3967, 18744, 16322, 11804, 12532, 676, 16575, 3372, 10930, 5929, 12540, 11653, 8930, 9551, 13707, 15492, 1652, 17157, 9223, 12268, 6930, 20526, 12763, 5616, 11058, 11845, 14788, 5170, 7289, 4182, 17908, 3412, 2815, 6504, 10732, 19766, 2198, 16788, 4722, 10514, 10233, 13131, 11614, 16395, 9455, 2225, 15832, 9461, 12696, 3023, 22671, 12208, 3937, 11869, 9863, 3485, 4206, 353, 12458, 2887, 6781, 14621, 24510, 2920, 5809, 5552, 11818, 1391, 2913, 6458, 9046, 13410, 9952, 8337, 11984, 2931, 8639, 3166, 9227, 1809, 859, 8428, 10534, 1601, 14497, 16339, 12548, 16611, 4246, 17073, 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24193, 4833, 24259, 16969, 13362, 11781, 15215, 20275, 12478, 19627, 6681, 8565, 6660, 13962, 4913, 19733, 7264, 8636, 369, 1785, 24061, 15265, 11872, 1333, 20939, 9892, 18772, 8809, 12477, 15646, 5500, 16766, 18575, 16376, 24078, 11475, 6446, 13578, 14987, 12066, 21215, 10527, 16821, 12461, 16186, 2015, 10011, 21161, 1581, 8355, 23781, 12687, 648, 23756, 18543, 1847, 18664, 15728, 16442, 15097, 9376, 14443, 10951, 9830, 1129, 4774, 10424, 10077, 16509, 12538, 3207, 10592, 15114, 12616, 16885, 17973, 1241, 19421, 11293, 3622, 17898, 6475, 10689, 11033, 6931, 16007, 10191, 18001, 9091, 1829, 9829, 10798, 15390, 10275, 3572, 7868, 15714, 13591, 3659, 14419, 3378, 20541, 7256, 12907, 12722, 6343, 4762, 12488, 21342, 21132, 16705, 2772, 16034, 7775, 20363, 10116, 24664, 4066, 7000, 11463, 2180, 23671, 15447, 18769, 2060, 13587, 10579, 18554, 14372, 19913, 12827, 21033, 1975, 12855, 8974, 10422, 5493, 13224, 1888, 8214, 16283, 15627, 14894, 5511, 10790, 11279, 10988, 11173, 16285, 17981, 7525, 12186, 15708, 23101, 12592, 14533, 8859, 3449, 8369, 15270, 1644, 18818, 5887, 12483, 1153, 4772, 14235, 2368, 13803, 7986, 5513, 4856, 15358, 13034, 15618, 9090, 7797, 17825, 12688, 20801, 13037, 1635, 23562, 8642, 17488, 16223, 8744, 13801, 20601, 16371, 9580, 4933, 14583, 17120, 15201, 23105, 1309, 13044, 15021, 10525, 14068, 13794, 20101, 14698, 8893, 8057, 8761, 1983, 14242, 11182, 14073, 11916, 12324, 17784, 9061, 1501, 11767, 13539, 10901, 16014, 12792, 9806, 566, 23368, 16062, 18690, 10873, 16587, 19674, 20268, 16237, 20528, 3669, 11808, 8948, 8512, 20917, 10483, 12260, 15569, 8791, 20872, 21488, 12529, 19498, 942, 20790, 10833, 4794, 9788, 14537, 12758, 6081, 15849, 4809, 11830, 10058, 2582, 638, 15658, 21260, 17717, 7658, 11323, 10768, 11263, 9414, 18791, 4967, 10651, 1608, 2040, 18468, 14249, 6290, 18957, 24508, 19875, 18648, 712, 19775, 3641, 10258, 10257, 6488, 2524, 13031, 11223, 15359, 10429, 3791, 6038, 15536, 20017, 24486, 9295, 15862, 1530, 12562, 8205, 11704, 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16159, 4981, 11069, 15412, 19796, 14120, 1027, 761, 12402])
Use gene symbols as the index
In [31]:
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import pandas as pd
gene_embeddings = torch.stack(list(predictions["gene_embeddings"].values())).to(torch.float32).cpu().numpy()
gene_embeddings_df = pd.DataFrame(gene_embeddings, index=predictions["gene_symbols"].values())
gene_embeddings_df
import pandas as pd
gene_embeddings = torch.stack(list(predictions["gene_embeddings"].values())).to(torch.float32).cpu().numpy()
gene_embeddings_df = pd.DataFrame(gene_embeddings, index=predictions["gene_symbols"].values())
gene_embeddings_df
Out[31]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NBEAL1 | 0.042725 | 0.167969 | -0.043945 | -0.061523 | -0.091309 | 0.021484 | 0.083984 | -0.083984 | 0.047607 | 0.167969 | ... | 0.077148 | -0.086426 | -0.015320 | 0.167969 | 0.083984 | -0.047852 | -0.128906 | 0.083984 | -0.084473 | -0.146484 |
| RPL39 | 0.066406 | -0.132812 | -0.033203 | -0.033203 | 0.132812 | 0.066406 | -0.066406 | 0.037109 | 0.066406 | -0.066406 | ... | 0.066406 | -0.054932 | 0.033203 | -0.016479 | -0.034424 | 0.016602 | -0.066406 | 0.034424 | 0.033203 | 0.033936 |
| RPS27 | 0.104004 | -0.127930 | 0.054443 | 0.063965 | -0.029175 | 0.063965 | 0.018677 | -0.066406 | 0.063965 | -0.063965 | ... | -0.063965 | 0.106934 | 0.063965 | 0.035156 | -0.046631 | -0.032471 | -0.085449 | 0.016479 | -0.032227 | 0.032471 |
| ZNF90 | 0.283203 | 0.281250 | -0.071289 | 0.016357 | -0.253906 | -0.141602 | 0.092773 | -0.143555 | 0.082031 | 0.265625 | ... | 0.141602 | 0.141602 | 0.141602 | 0.143555 | -0.102539 | -0.072266 | -0.153320 | -0.141602 | -0.070801 | -0.141602 |
| RPS29 | 0.056396 | -0.125977 | 0.030640 | -0.040771 | 0.032227 | 0.062988 | -0.062988 | -0.062988 | -0.031494 | 0.062988 | ... | -0.062988 | -0.056641 | 0.037842 | 0.062988 | 0.062988 | -0.031494 | -0.067871 | -0.032227 | -0.041016 | -0.062988 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| IGKV3D-7 | 1.156250 | -0.281250 | 0.168945 | -0.285156 | -0.330078 | -0.765625 | 1.062500 | -0.002670 | 0.671875 | 1.140625 | ... | -0.163086 | 0.018066 | -0.546875 | 0.925781 | 0.200195 | -0.107422 | -1.656250 | -0.024292 | 0.287109 | 0.110840 |
| FAM187B | -0.050293 | -0.773438 | 0.032471 | 0.019775 | -0.566406 | 1.203125 | 0.047607 | 0.476562 | 0.535156 | 2.000000 | ... | -0.898438 | 0.084961 | 1.265625 | 0.984375 | 0.859375 | -0.601562 | 1.296875 | 0.281250 | -0.234375 | -0.177734 |
| ATP2B3 | 0.855469 | -0.335938 | -0.427734 | 0.781250 | -0.178711 | -0.468750 | 0.164062 | 0.006195 | 0.208008 | 2.218750 | ... | -1.289062 | -0.238281 | 0.019775 | 1.070312 | 0.498047 | -0.408203 | 1.203125 | 1.398438 | -0.625000 | 0.182617 |
| ITIH1 | 0.058594 | -1.820312 | -0.376953 | 0.021240 | -0.953125 | 0.507812 | 0.071289 | 0.714844 | 0.332031 | 2.421875 | ... | -0.347656 | -0.419922 | 0.359375 | 1.078125 | 0.765625 | -0.126953 | 0.796875 | 1.570312 | -0.063965 | 0.675781 |
| GRM5 | -0.867188 | -3.421875 | -0.003281 | -0.114258 | -0.558594 | 0.589844 | -0.691406 | -0.119141 | 0.546875 | 2.156250 | ... | -1.007812 | 0.871094 | -0.357422 | 0.828125 | 0.941406 | -0.378906 | -0.056152 | -0.275391 | 0.261719 | -0.355469 |
17178 rows × 256 columns
Visualize network connectivity within desired gene program¶
The following tutorial is adapted from scGPT tutorial to show how to use gene embeddings to build gene co-expression network
In [32]:
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CD_genes = ["LCK", "CD2", "LTB", "CD3E", "CD3G", "CD8B", "CD8A", "IL7R", "CD3D"]
CD_genes = ["LCK", "CD2", "LTB", "CD3E", "CD3G", "CD8B", "CD8A", "IL7R", "CD3D"]
In [33]:
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import operator
from sklearn.metrics.pairwise import cosine_similarity
def compute_similarities(gene_embeddings_df, gene, subset=None):
"""
Compute cosine similarities of the given gene against all others
in the `gene_embeddings_df` DataFrame.
Parameters:
- gene (str): Gene symbol to compare against.
- subset (list[str], optional): Optional list of target genes to compare to.
Returns:
- pd.DataFrame: DataFrame with columns ['Gene', 'Similarity'] sorted by similarity.
"""
if gene not in gene_embeddings_df.index:
return None
embedding = gene_embeddings_df.loc[gene].values.reshape(1, -1)
if subset:
targets = list(set(gene_embeddings_df.index).intersection(set(subset)))
else:
targets = list(gene_embeddings_df.index)
similarities = {}
for target in targets:
if target == gene:
continue
v = gene_embeddings_df.loc[target].values.reshape(1, -1)
similarity = float(cosine_similarity(embedding, v)[0])
similarities[target] = similarity
# Sort by similarity (descending)
sorted_distances = sorted(similarities.items(), key=operator.itemgetter(1), reverse=True)
return pd.DataFrame(sorted_distances, columns=["Gene", "Similarity"])
import operator
from sklearn.metrics.pairwise import cosine_similarity
def compute_similarities(gene_embeddings_df, gene, subset=None):
"""
Compute cosine similarities of the given gene against all others
in the `gene_embeddings_df` DataFrame.
Parameters:
- gene (str): Gene symbol to compare against.
- subset (list[str], optional): Optional list of target genes to compare to.
Returns:
- pd.DataFrame: DataFrame with columns ['Gene', 'Similarity'] sorted by similarity.
"""
if gene not in gene_embeddings_df.index:
return None
embedding = gene_embeddings_df.loc[gene].values.reshape(1, -1)
if subset:
targets = list(set(gene_embeddings_df.index).intersection(set(subset)))
else:
targets = list(gene_embeddings_df.index)
similarities = {}
for target in targets:
if target == gene:
continue
v = gene_embeddings_df.loc[target].values.reshape(1, -1)
similarity = float(cosine_similarity(embedding, v)[0])
similarities[target] = similarity
# Sort by similarity (descending)
sorted_distances = sorted(similarities.items(), key=operator.itemgetter(1), reverse=True)
return pd.DataFrame(sorted_distances, columns=["Gene", "Similarity"])
In [34]:
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import tqdm
# Initialize an empty list to collect DataFrames
df_list = []
gene_embeddings_df_CD = gene_embeddings_df.loc[gene_embeddings_df.index.intersection(CD_genes)]
for i in tqdm.tqdm(CD_genes):
df = compute_similarities(gene_embeddings_df_CD, i)
if df is not None:
df["Gene1"] = i
df_list.append(df)
# Concatenate all collected DataFrames
df_CD = pd.concat(df_list, ignore_index=True)
# Filter out self-similarity and sort
df_CD_sub = df_CD[df_CD["Similarity"] < 0.99].sort_values(by="Gene")
df_CD_sub
import tqdm
# Initialize an empty list to collect DataFrames
df_list = []
gene_embeddings_df_CD = gene_embeddings_df.loc[gene_embeddings_df.index.intersection(CD_genes)]
for i in tqdm.tqdm(CD_genes):
df = compute_similarities(gene_embeddings_df_CD, i)
if df is not None:
df["Gene1"] = i
df_list.append(df)
# Concatenate all collected DataFrames
df_CD = pd.concat(df_list, ignore_index=True)
# Filter out self-similarity and sort
df_CD_sub = df_CD[df_CD["Similarity"] < 0.99].sort_values(by="Gene")
df_CD_sub
0%| | 0/9 [00:00<?, ?it/s]/tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) /tmp/ipykernel_177/2481566272.py:31: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) similarity = float(cosine_similarity(embedding, v)[0]) 100%|█████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 635.83it/s]
Out[34]:
| Gene | Similarity | Gene1 | |
|---|---|---|---|
| 22 | CD2 | 0.460666 | LTB |
| 46 | CD2 | 0.515722 | CD8B |
| 31 | CD2 | 0.495085 | CD3E |
| 62 | CD2 | 0.486329 | IL7R |
| 39 | CD2 | 0.316598 | CD3G |
| ... | ... | ... | ... |
| 54 | LTB | 0.515988 | CD8A |
| 45 | LTB | 0.572584 | CD8B |
| 38 | LTB | 0.371931 | CD3G |
| 61 | LTB | 0.542769 | IL7R |
| 13 | LTB | 0.460666 | CD2 |
72 rows × 3 columns
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import matplotlib.pyplot as plt
import networkx as nx
import matplotlib.pyplot as plt
import networkx as nx
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# Creates a graph from the cosine similarity network
input_node_weights = [(row["Gene"], row["Gene1"], round(row["Similarity"], 2)) for i, row in df_CD_sub.iterrows()]
G = nx.Graph()
G.add_weighted_edges_from(input_node_weights)
# Creates a graph from the cosine similarity network
input_node_weights = [(row["Gene"], row["Gene1"], round(row["Similarity"], 2)) for i, row in df_CD_sub.iterrows()]
G = nx.Graph()
G.add_weighted_edges_from(input_node_weights)
In [37]:
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# Plot the cosine similarity network; strong edges (> select threshold) are highlighted
thresh = 0.4
plt.figure(figsize=(15, 15))
widths = nx.get_edge_attributes(G, "weight")
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > thresh]
esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= thresh]
pos = nx.spring_layout(G, k=0.4, iterations=15, seed=3)
width_large = {}
width_small = {}
for i, v in enumerate(list(widths.values())):
if v > thresh:
width_large[list(widths.keys())[i]] = v * 10
else:
width_small[list(widths.keys())[i]] = max(v, 0) * 10
nx.draw_networkx_edges(
G, pos, edgelist=width_small.keys(), width=list(width_small.values()), edge_color="lightblue", alpha=0.8
)
nx.draw_networkx_edges(
G,
pos,
edgelist=width_large.keys(),
width=list(width_large.values()),
alpha=0.5,
edge_color="blue",
)
# node labels
nx.draw_networkx_labels(G, pos, font_size=25, font_family="sans-serif")
# edge weight labels
d = nx.get_edge_attributes(G, "weight")
edge_labels = {k: d[k] for k in elarge}
nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=15)
ax = plt.gca()
ax.margins(0.08)
plt.axis("off")
plt.show()
# Plot the cosine similarity network; strong edges (> select threshold) are highlighted
thresh = 0.4
plt.figure(figsize=(15, 15))
widths = nx.get_edge_attributes(G, "weight")
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > thresh]
esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= thresh]
pos = nx.spring_layout(G, k=0.4, iterations=15, seed=3)
width_large = {}
width_small = {}
for i, v in enumerate(list(widths.values())):
if v > thresh:
width_large[list(widths.keys())[i]] = v * 10
else:
width_small[list(widths.keys())[i]] = max(v, 0) * 10
nx.draw_networkx_edges(
G, pos, edgelist=width_small.keys(), width=list(width_small.values()), edge_color="lightblue", alpha=0.8
)
nx.draw_networkx_edges(
G,
pos,
edgelist=width_large.keys(),
width=list(width_large.values()),
alpha=0.5,
edge_color="blue",
)
# node labels
nx.draw_networkx_labels(G, pos, font_size=25, font_family="sans-serif")
# edge weight labels
d = nx.get_edge_attributes(G, "weight")
edge_labels = {k: d[k] for k in elarge}
nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=15)
ax = plt.gca()
ax.margins(0.08)
plt.axis("off")
plt.show()
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