3- Alignment Evaluation
This tutorial describes the process of evaluating alignment in the ExR-Tools, including measuring alignment accuracy and calculating confidence intervals.
Step 1: Load Configuration Settings
Start by loading the configuration settings.
from exr.config.config import Config
# Create a new Config object instance.
config = Config()
# Provide the path to the configuration file.
config_file_path = '/path/to/your/configuration/file.json'
# Load the configuration settings from the specified file.
config.load_config(config_file_path)
Step 2: Additional Configuration for Alignment Evaluation
Configure additional parameters for alignment evaluation.
# Set various parameters for alignment evaluation
config.nonzero_thresh = .2 * 2048 * 2048 * 80
config.N = 1000
config.subvol_dim = 100
config.xystep = 0.1625/40 # check value
config.zstep = 0.25/40 # check value
config.pct_thresh = 99
Step 3: Alignment Measurement
Measure alignment for specified rounds and ROIs.
from exr.align.align_eval import measure_round_alignment_NCC
round_to_analyze = config.rounds
roi_to_analyze = config.rois # Adjust based on your dataset.
for roi in roi_to_analyze:
for round in round_to_analyze:
measure_round_alignment_NCC(config=config, round=round, roi=roi)
Step 4: Alignment Evaluation and Confidence Interval Calculation
Evaluate alignment and calculate confidence intervals.
from exr.align.align_eval import plot_alignment_evaluation, calculate_alignment_evaluation_ci
ci_percentage = 95
percentile_filter_value = 95
for roi in roi_to_analyze:
plot_alignment_evaluation(config, roi, percentile=percentile_filter_value, save_fig=True)
calculate_alignment_evaluation_ci(config, roi, ci=ci_percentage, percentile_filter=percentile_filter_value)
Next Steps
After assessing the alignment, the next step in the ExSeq-Toolbox workflow is Synapses Segmentation. This step involves segmenting and analyzing synapses within the dataset. For detailed instructions on Synapses Segmentation, refer to the Synapses Segmentation section of this guide.