Permutation score analysis - Pre-frontal cortex

Library imports

import os
import sys

import numpy as np
import pandas as pd
import torch
from scipy import stats
from sklearn.preprocessing import MinMaxScaler

import matplotlib.pyplot as plt
import mplscience
import seaborn as sns

import scanpy as sc
import scvelo as scv
import scvi
from scvelo.plotting.simulation import compute_dynamics
from velovi import VELOVI

sys.path.append("../..")
from paths import DATA_DIR, FIG_DIR

Global seed set to 0

General settings

scvi.settings.dl_pin_memory_gpu_training = False

sns.reset_defaults()
sns.reset_orig()
scv.settings.set_figure_params('scvelo', dpi_save=400, dpi=80, transparent=True, fontsize=20, color_map='viridis')

SAVE_FIGURES = True
if SAVE_FIGURES:
    os.makedirs(FIG_DIR / 'permutation' / 'pfc', exist_ok=True)

Function definitions

def fit_velovi(bdata):
    VELOVI.setup_anndata(bdata, spliced_layer="Ms", unspliced_layer="Mu")

    vae = VELOVI(bdata)
    vae.train()

    df = vae.history["elbo_train"].iloc[20:].reset_index().rename(columns={'elbo_train': 'elbo'})
    df['set'] = 'train'

    _df = vae.history["elbo_validation"].iloc[20:].reset_index().rename(columns={'elbo_validation': 'elbo'})
    _df['set'] = 'validation'

    df = pd.concat([df, _df], axis=0).reset_index(drop=True)

    with mplscience.style_context():
        sns.set_style(style="whitegrid")
        fig, ax = plt.subplots(figsize=(6, 4))
        sns.lineplot(data=df, x='epoch', y='elbo', hue='set', palette=['#0173B2', '#DE8F05'], ax=ax)

    latent_time = vae.get_latent_time(n_samples=25)
    velocities = vae.get_velocity(n_samples=25, velo_statistic="mean")

    t = latent_time
    scaling = 20 / t.max(0)

    bdata.layers["velocities_velovi"] = velocities / scaling
    bdata.layers["latent_time_velovi"] = latent_time

    bdata.var["fit_alpha"] = vae.get_rates()["alpha"] / scaling
    bdata.var["fit_beta"] = vae.get_rates()["beta"] / scaling
    bdata.var["fit_gamma"] = vae.get_rates()["gamma"] / scaling
    bdata.var["fit_t_"] = (
        torch.nn.functional.softplus(vae.module.switch_time_unconstr)
        .detach()
        .cpu()
        .numpy()
    ) * scaling
    bdata.layers["fit_t"] = latent_time.values * scaling[np.newaxis, :]
    bdata.var['fit_scaling'] = 1.0

    return vae

def plot_phase_portrait(adata, gene, color, permuted=False, figsize=(6, 6)):
    fig, ax = plt.subplots(figsize=figsize)

    df = pd.DataFrame(
        {
            'unspliced': adata[:, gene].layers['Mu'].squeeze().copy(),
            'spliced': adata[:, gene].layers['Ms'].squeeze().copy(),
            'color': color
        }
    )

    with mplscience.style_context():
        sns.scatterplot(data=df, x='spliced', y='unspliced', c=color, s=25, ax=ax);

        _, unspliced, spliced = compute_dynamics(adata, basis=gene, extrapolate=True, sort=True)
        df = pd.DataFrame(
            {
                'unspliced': unspliced.squeeze(),
                'spliced': spliced.squeeze(),
            }
        )

        ax.plot(spliced, unspliced, color="purple", linewidth=2)

        spliced_steady_state = np.linspace(np.min(spliced), np.max(spliced))
        unspliced_steady_state = adata.var.loc[gene, 'fit_gamma'] / adata.var.loc[gene, 'fit_beta'] * (spliced_steady_state - np.min(spliced_steady_state)) + np.min(unspliced)
        ax.plot(spliced_steady_state, unspliced_steady_state, color='purple', linestyle="--", linewidth=2);

    ax.axis('off')
    
    if SAVE_FIGURES:
        if permuted:
            fname = f'phase_portrait_{gene}_permuted'
        else:
            fname = f'phase_portrait_{gene}'
        fig.savefig(
            FIG_DIR / 'permutation' / 'pfc' / f'{fname}.svg',
            format="svg",
            transparent=True,
            bbox_inches='tight'
        )

def plot_perm_scores(adata, perm_scores, gene, color_label, figsize=(6, 4)):
    df = pd.DataFrame(perm_scores.loc[gene])
    df["Cell type"] = df.index
    order = adata.obs[color_label].cat.categories.tolist()
    
    with mplscience.style_context():
        sns.set_style(style="whitegrid")
        fig, ax = plt.subplots(figsize=figsize)
        sns.barplot(
            data=df,
            y=gene,
            x="Cell type",
            palette=adata.uns[f"{color_label}_colors"],
            order=order,
            ax=ax,
        )
        ax.set_ylim([-4.5, 30])
        ax.tick_params(axis='x', rotation=45)

    if SAVE_FIGURES:
        fig.savefig(
            FIG_DIR / 'permutation' / 'pfc' / f'permutation_score_{gene}.svg',
            format="svg",
            transparent=True,
            bbox_inches='tight'
        )

Data loading

adata = sc.read(DATA_DIR / "pfc" / "pfc.h5ad")
adata

AnnData object with n_obs × n_vars = 1267 × 54144
    obs: 'cell_index', 'clusters_coarse', 'clusters', 'X', 'nGene', 'nUMI', 'percent.mito', 'Sample', 'treatment', 'Period', 'stage', 'DevStage'
    uns: 'clusters_colors'
    layers: 'spliced', 'unspliced'

Data preprocessing

scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

scaler = MinMaxScaler()
adata.layers["Mu"] = scaler.fit_transform(adata.layers["Mu"])

scaler = MinMaxScaler()
adata.layers["Ms"] = scaler.fit_transform(adata.layers["Ms"])

Filtered out 48098 genes that are detected 20 counts (shared).
Normalized count data: X, spliced, unspliced.
Extracted 2000 highly variable genes.
Logarithmized X.
computing neighbors
    finished (0:00:05) --> added 
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:00) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)

Model fitting

velovi_vae = fit_velovi(adata)

/home/icb/philipp.weiler/miniconda3/envs/velovi-py39/lib/python3.9/site-packages/torch/distributed/_sharded_tensor/__init__.py:8: DeprecationWarning: torch.distributed._sharded_tensor will be deprecated, use torch.distributed._shard.sharded_tensor instead
  warnings.warn(
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Set SLURM handle signals.

Epoch 500/500: 100%|██████████| 500/500 [01:05<00:00,  7.60it/s, loss=-5.68e+03, v_num=1]

Permutation score evaluation

perm_scores, permuted_adata = velovi_vae.get_permutation_scores(labels_key='clusters')

INFO     Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup       
INFO     Input AnnData not setup with scvi-tools. attempting to transfer AnnData setup       

full_perm_df = pd.DataFrame(columns=["Score", "Dataset"])

max_ratio = np.nanmax(perm_scores.values, axis=1)
scores = max_ratio.tolist()
dataset = ['PFC'] * len(max_ratio)

full_perm_df["Score"] = scores
full_perm_df["Dataset"] = dataset

color = adata.obs['clusters'].astype(str).replace(
    dict(zip(adata.obs['clusters'].cat.categories, adata.uns['clusters_colors']))
).tolist()

Lowest ranked genes

for gene in adata.var_names[np.argsort(scores)[:2]]:
    plot_phase_portrait(adata, gene, color)
    plot_phase_portrait(permuted_adata, gene, color, permuted=True)
    plot_perm_scores(adata, perm_scores, gene, 'clusters')

Highest ranked genes

for gene in adata.var_names[np.argsort(scores)[-2:]]:
    plot_phase_portrait(adata, gene, color)
    plot_phase_portrait(permuted_adata, gene, color, permuted=True)
    plot_perm_scores(adata, perm_scores, gene, 'clusters')