cryojax 0.2.3

Cryo-EM image simulation and analysis powered by JAX

cryoJAX

cryoJAX is a library that provides tools for simulating and analyzing cryo-electron microscopy (cryo-EM) images. It is built on jax.

Summary

Specifically, cryoJAX aims to provide three things in the cryo-EM image-to-structure pipeline.

1. Physical modeling of image formation
2. Statistical modeling of the distributions from which images are drawn
3. Easy-to-use utilities for working with real data

With these tools, cryojax aims to appeal to two different communities. Experimentalists can use cryojax in order to push the boundaries of what they can extract from their data by interfacing with the jax scientific computing ecosystem. Additionally, method developers may use cryojax as a backend for an algorithmic research project, such as in cryo-EM structure determination. These two aims are possible because cryojax is written to be fully interoperable with anything else in the JAX ecosystem.

Dig a little deeper and you'll find that cryojax aims to be a fully extensible modeling language for cryo-EM image formation. It implements a collection of abstract interfaces, which aim to be general enough to support any level of modeling complexity—from simple linear image formation to the most realistic physical models in the field. Best of all, these interfaces are all part of the public API. Users can create their own extensions to cryojax, tailored to their specific use-case!

Documentation

See the documentation at https://mjo22.github.io/cryojax/. It is a work-in-progress, so thank you for your patience!

Installation

Installing cryojax is simple. To start, I recommend creating a new virtual environment. For example, you could do this with conda.

conda create -n cryojax-env -c conda-forge python=3.11

Note that python>=3.10 is required. After creating a new environment, install JAX with either CPU or GPU support. Then, install cryojax. For the latest stable release, install using pip.

python -m pip install cryojax

To install the latest commit, you can build the repository directly.

git clone https://github.com/mjo22/cryojax
cd cryojax
python -m pip install .

The jax-finufft package is an optional dependency used for non-uniform fast fourier transforms. These are included as an option for computing image projections of real-space voxel-based scattering potential representations. In this case, we recommend first following the jax_finufft installation instructions and then installing cryojax.

Simulating an image

The following is a basic workflow to simulate an image.

First, instantiate the scattering potential representation and its respective method for computing image projections.

import jax
import jax.numpy as jnp
import cryojax.simulator as cs
from cryojax.io import read_array_with_spacing_from_mrc

# Instantiate the scattering potential.
filename = "example_scattering_potential.mrc"
real_voxel_grid, voxel_size = read_array_with_spacing_from_mrc(filename)
potential = cs.FourierVoxelGridPotential.from_real_voxel_grid(real_voxel_grid, voxel_size)
# ... now instantiate fourier slice extraction
integrator = cs.FourierSliceExtract(interpolation_order=1)

Here, the 3D scattering potential array is read from filename. Then, the abstraction of the scattering potential is then loaded in fourier-space into a FourierVoxelGridPotential, and the fourier-slice projection theorem is initialized with FourierSliceExtract. The scattering potential can be generated with an external program, such as the cisTEM simulate tool.

We can now instantiate the representation of a biological specimen, which also includes a pose.

# First instantiate the pose. Here, angles are given in degrees
pose = cs.EulerAnglePose(
    offset_x_in_angstroms=5.0,
    offset_y_in_angstroms=-3.0,
    view_phi=20.0,
    view_theta=80.0,
    view_psi=-10.0,
)
# ... now, build the biological specimen
specimen = cs.Specimen(potential, integrator, pose)

Next, build the model for the electron microscope. Here, we simply include a model for the CTF in the weak-phase approximation (linear image formation theory).

from cryojax.image import operators as op

# First, initialize the CTF and its optics model
ctf = cs.CTF(
    defocus_u_in_angstroms=10000.0,
    defocus_v_in_angstroms=9800.0,
    astigmatism_angle=10.0,
    amplitude_contrast_ratio=0.1)
optics = cs.WeakPhaseOptics(ctf, envelope=op.FourierGaussian(b_factor=5.0))  # b_factor is given in Angstroms^2
# ... these are stored in the Instrument
voltage_in_kilovolts = 300.0,
instrument = cs.Instrument(voltage_in_kilovolts, optics)

The CTF has parameters used in CTFFIND4, which take their default values if not explicitly configured here. Finally, we can instantiate the ImagePipeline and simulate an image.

# Instantiate the image configuration
config = cs.ImageConfig(shape=(320, 320), pixel_size=voxel_size)
# Build the image formation model
pipeline = cs.ImagePipeline(config, specimen, instrument)
# ... simulate an image and return in real-space.
image_without_noise = pipeline.render(get_real=True)

cryojax also defines a library of distributions from which to sample the data. These distributions define the stochastic model from which images are drawn. For example, instantiate an IndependentFourierGaussian distribution and either sample from it or compute its log-likelihood.

from cryojax.image import rfftn
from cryojax.inference import distributions as dist
from cryojax.image import operators as op

# Passing the ImagePipeline and a variance function, instantiate the distribution
distribution = dist.IndependentFourierGaussian(pipeline, variance=op.Constant(1.0))
# ... then, either simulate an image from this distribution
key = jax.random.PRNGKey(seed=0)
image_with_noise = distribution.sample(key)
# ... or compute the likelihood
observed = rfftn(...)  # for this example, read in observed data and take FFT
log_likelihood = distribution.log_likelihood(observed)

For more advanced image simulation examples and to understand the many features in this library, see the documentation.

Acknowledgements

• cryojax has been greatly informed by the open-source cryo-EM softwares cisTEM and BioEM.
• cryojax relies heavily on and has taken great inspiration from equinox. We think that equinox has great design principles and highly recommend learning about it to fully make use of the power of jax.