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Topic Modeling with Amortized LDA

In this tutorial, we will explore how to run the amortized Latent Dirichlet Allocation (LDA) model implementation in scvi-tools. LDA is a topic modelling method first introduced in the natural language processing field. By treating each cell as a document and each gene expression count as a word, we can carry over the method to the single-cell biology field.

Below, we will train the model over a dataset, plot the topics over a UMAP of the reference set, and inspect the topics for characteristic gene sets.

As an example, we use the PBMC 10K dataset from 10x Genomics.

!pip install --quiet scvi-colab
from scvi_colab import install

install()

import os

import pandas as pd
import scanpy as sc
import scvi

sc.set_figure_params(figsize=(4, 4))

%config InlineBackend.print_figure_kwargs={'facecolor' : "w"}
%config InlineBackend.figure_format='retina'

Global seed set to 0
/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.
  warnings.warn(problem)

Load and process data

Load the 10x genomics PBMC dataset. Generally, it is good practice for LDA to remove ubiquitous genes, to prevent the model from modeling these genes as a separate topic. Here, we first filter out all mitochrondrial genes, then select the top 1000 variable genes with seurat_v3 method from the remaining genes.

save_path = "data"
adata = sc.read(
    os.path.join(save_path, "pbmc_10k_protein_v3.h5ad"),
    backup_url="https://github.com/YosefLab/scVI-data/raw/master/pbmc_10k_protein_v3.h5ad?raw=true",
)

adata.layers["counts"] = adata.X.copy()  # preserve counts
sc.pp.normalize_total(adata, target_sum=10e4)
sc.pp.log1p(adata)
adata.raw = adata  # freeze the state in `.raw`

adata = adata[:, ~adata.var_names.str.startswith("MT-")]
sc.pp.highly_variable_genes(
    adata, flavor="seurat_v3", layer="counts", n_top_genes=1000, subset=True
)

Trying to set attribute `._uns` of view, copying.

Create and fit AmortizedLDA model

Here, we initialize and fit an AmortizedLDA model on the dataset. We pick 10 topics to model in this case.

n_topics = 10

scvi.model.AmortizedLDA.setup_anndata(adata, layer="counts")
model = scvi.model.AmortizedLDA(adata, n_topics=n_topics)

Note

By default we train with KL annealing which means the effective loss will generally not decrease steadily in the beginning. Our Pyro implementations present this train loss term as the elbo_train in the progress bar which is misleading. We plan on correcting this in the future.

model.train()

GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/configuration_validator.py:120: UserWarning: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
  rank_zero_warn("You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.")
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Epoch 1000/1000: 100%|██████████| 1000/1000 [12:06<00:00,  1.38it/s, v_num=1, elbo_train=1.87e+7]

Visualizing learned topics

By calling model.get_latent_representation(), the model will compute a Monte Carlo estimate of the topic proportions for each cell. Since we use a logistic-Normal distribution to approximate the Dirichlet distribution, the model cannot compute the analytic mean. The number of samples used to compute the latent representation can be configured with the optional argument n_samples.

topic_prop = model.get_latent_representation()
topic_prop.head()

	topic_0	topic_1	topic_2	topic_3	topic_4	topic_5	topic_6	topic_7	topic_8	topic_9
index
AAACCCAAGATTGTGA-1	0.000474	0.021273	0.116839	0.005243	0.000534	0.852167	0.000484	0.001973	0.000373	0.000640
AAACCCACATCGGTTA-1	0.000340	0.005373	0.000217	0.000532	0.000214	0.992013	0.000402	0.000553	0.000161	0.000195
AAACCCAGTACCGCGT-1	0.002523	0.018910	0.602798	0.011032	0.002445	0.356765	0.002098	0.001950	0.000712	0.000767
AAACCCAGTATCGAAA-1	0.011468	0.004124	0.003098	0.004504	0.006355	0.002744	0.003459	0.003941	0.004475	0.955833
AAACCCAGTCGTCATA-1	0.000981	0.000920	0.000645	0.000751	0.001073	0.000595	0.000921	0.000838	0.000661	0.992613

# Save topic proportions in obsm and obs columns.
adata.obsm["X_LDA"] = topic_prop
for i in range(n_topics):
    adata.obs[f"LDA_topic_{i}"] = topic_prop[[f"topic_{i}"]]

Plot UMAP

sc.tl.pca(adata, svd_solver="arpack")
sc.pp.neighbors(adata, n_pcs=30, n_neighbors=20)
sc.tl.umap(adata)
sc.tl.leiden(adata, key_added="leiden_scVI", resolution=0.8)

# Save UMAP to custom .obsm field.
adata.obsm["raw_counts_umap"] = adata.obsm["X_umap"].copy()

sc.pl.embedding(adata, "raw_counts_umap", color=["leiden_scVI"], frameon=False)

Color UMAP by topic proportions

By coloring by UMAP by topic proportions, we find that the learned topics are generally dominant in cells close together in the UMAP space. In some cases, a topic is dominant in multiple clusters in the UMAP, which indicates similarilty between these clusters despite being far apart in the plot. This is not surprising considering that UMAP does not preserve local relationships beyond a certain threshold.

sc.pl.embedding(
    adata,
    "raw_counts_umap",
    color=[f"LDA_topic_{i}" for i in range(n_topics)],
    frameon=False,
)

Plot UMAP in topic space

sc.pp.neighbors(adata, use_rep="X_LDA", n_neighbors=20, metric="hellinger")
sc.tl.umap(adata)

# Save UMAP to custom .obsm field.
adata.obsm["topic_space_umap"] = adata.obsm["X_umap"].copy()

sc.pl.embedding(
    adata,
    "topic_space_umap",
    color=[f"LDA_topic_{i}" for i in range(n_topics)],
    frameon=False,
)

Find top genes per topic

Similar to the topic proportions, model.get_feature_by_topic() returns a Monte Carlo estimate of the gene by topic matrix, which contains the proportion that a gene is weighted in each topic. This is also due to another approximation of the Dirichlet with a logistic-Normal distribution. We can inspect each topic in this matrix and sort by proportion allocated to each gene to determine top genes characterizing each topic.

feature_by_topic = model.get_feature_by_topic()
feature_by_topic.head()

	topic_0	topic_1	topic_2	topic_3	topic_4	topic_5	topic_6	topic_7	topic_8	topic_9
index
AL645608.8	0.000001	0.000003	0.000002	0.000002	0.000041	3.110339e-06	0.000005	0.000003	0.000004	0.000002
HES4	0.000008	0.000013	0.000008	0.000019	0.000843	8.526634e-06	0.000005	0.000012	0.000009	0.000006
ISG15	0.001131	0.000169	0.000402	0.000596	0.000610	2.966939e-04	0.001417	0.000501	0.000305	0.001375
TNFRSF18	0.000296	0.000003	0.000001	0.000002	0.000004	9.796551e-07	0.000553	0.000006	0.000025	0.000132
TNFRSF4	0.000667	0.000004	0.000001	0.000004	0.000004	1.897183e-06	0.000922	0.000004	0.000006	0.000059

rank_by_topic = pd.DataFrame()
for i in range(n_topics):
    topic_name = f"topic_{i}"
    topic = feature_by_topic[topic_name].sort_values(ascending=False)
    rank_by_topic[topic_name] = topic.index
    rank_by_topic[f"{topic_name}_prop"] = topic.values

rank_by_topic.head()

	topic_0	topic_0_prop	topic_1	topic_1_prop	topic_2	topic_2_prop	topic_3	topic_3_prop	topic_4	topic_4_prop	topic_5	topic_5_prop	topic_6	topic_6_prop	topic_7	topic_7_prop	topic_8	topic_8_prop	topic_9	topic_9_prop
0	TMSB4X	0.126198	LYZ	0.071308	LYZ	0.056180	FTL	0.068417	FTL	0.098839	S100A9	0.133041	ACTB	0.164749	CD74	0.122255	IGKC	0.189100	ACTB	0.090690
1	TMSB10	0.086073	ACTB	0.071091	S100A9	0.053984	FTH1	0.051274	ACTB	0.070052	S100A8	0.094452	TMSB4X	0.132325	HLA-DRA	0.089028	IGLC2	0.099806	TMSB4X	0.088525
2	ACTB	0.084189	HLA-DRA	0.040470	FTH1	0.050848	ACTB	0.047326	TMSB4X	0.059721	LYZ	0.056391	TMSB10	0.082519	TMSB4X	0.059973	IGHA1	0.065741	GNLY	0.069525
3	JUNB	0.035964	CD74	0.036424	FTL	0.050564	TMSB4X	0.043904	FTH1	0.057384	FTL	0.044824	ACTG1	0.055351	ACTB	0.055289	IGHM	0.046249	NKG7	0.052817
4	FTL	0.028489	TMSB4X	0.035223	ACTB	0.034482	LYZ	0.040371	S100A4	0.029948	ACTB	0.039486	S100A4	0.037912	HLA-DRB1	0.050972	CD74	0.045520	TMSB10	0.040158