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Neighborhood-informed positional information for precise cell identity specification.

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The 10 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Description of Drosophila data ↔ data/WildTypeDrosophila.py, lines 7–133 · score 0.89 · 40–44 min, 38–48 min, WT Drosophila, 40 min, 38 min, gap genes
  2. [2] § Methods › Quantifying the error in the prediction of the location ↔ test_results_analysis/TestResults.py, the whole file · a weak match · score 0.69 · predicted location, ground truth, MAP positions, position prediction, error, decoding
  3. [3] § Methods › Description of Drosophila data ↔ src/_constants.py, lines 16–85 · score 0.61 · bcdE1, WT embryos, double, osk, neighborhood informed, cell independent
  4. [4] § Methods › Description of Drosophila data ↔ data/WildTypeDrosophila.py, lines 7–133 · score 0.60 · 38–48 min, 38 min, gap gene, Drosophila, embryos
  5. [5] § Results › Prediction of pair-rule gene expression ↔ src/_figures.py, lines 30–42 · score 0.58 · reconstruction error, neighborhood informed decoding, prediction error, AP axis, schematic, Eve
  6. [6] § Methods › Description of Drosophila data ↔ src/_constants.py, lines 16–85 · score 0.57 · bcdE1, Nos, Tsl, 55 min, 45 min, pair rule gene
  7. [7] § Results › Prediction of pair-rule gene expression ↔ src/_figures.py, lines 30–42 · score 0.56 · qualitative comparison, prediction error, AP axis, Eve, Prd, neighborhood informed
  8. [8] § Methods › Quantifying the error in the prediction of the location ↔ test_results_analysis/TestResults.py, the whole file · a weak match · score 0.56 · positional prediction error, ground truth, absolute, location, MAP, embryos
  9. [9] § Results › Short-range gene expression correlations close the positional information gap in the Drosophila embryo ↔ src/_figures.py, lines 2–26 · score 0.55 · schematic diagram, pairwise correlation, decoding maps, Hb, Kr, bits
  10. [10] § Results › Decoding position and predicting pair-rule stripes in mutant embryos ↔ src/_figures.py, lines 44–56 · score 0.53 · osk mutants, binned positions, Gap gene, prediction, Decoding, embryos

Paper

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The authors' code

Python · 148 lines · 7.2 KB · no license · 4 matches

  1. from src._visualizations import *
  2. def plot_figure1_panels():
  3. """
  4. This figure presents the positional information encoded in neighboring positions in wild type Drosophila embryos
  5. This functions plots panels b,c,d,e,f,h, and i. Panels a and g are schematic diagrams drawn in biorender.com
  6. """
  7. ##panels b and c: gene expression correlations
  8. gene_expression_pairwise_correlation_plots()
  10. #panel d: positional information in bits
  11. plot_positional_information_in_bits()
  12. #
  13. # #panel e: decoding neighborhood-informed and cell independent given Kr expression
  14. plot_decoding_maps_comparison_wt(ONE_GENE_EXAMPLE_GENE, VMAXS_ONE_GENE, xlim=False) #True
  15. #
  16. # #panel f: decoding neighborhood-informed and cell independent give Kr, Gt, and Hb expression
  17. plot_decoding_maps_comparison_wt(THREE_GENES_EXAMPLE_GENES, VMAXS_THREE_GENES, xlim=False)
  18. #
  19. # #aggregated results - mean MAP error over positions and over wild type embryos, mean per number of genes used
  20. # #for decoding
  21. # #panel h:
  22. plot_position_MAP_error_all_gene_subsets()
  23. # # #same aggregation , but looking at the standard deviation of the position posterior distribution
  24. # # #neighborhood-informed vs cell-independent
  25. # # #panel i:
  26. plot_position_posterior_std_all_gene_subsets()
  30. def plot_figure2_and_related_supp_panels():
  31. """
  32. This figure presents pair-rule expression profile prediction given the position probabilistic decoder.
  33. We show both qualitatively, quantitivately across the AP axis, and aggregated summary results
  34. comparing neighborhood-informed decoding based vs cell-independent decoding based pair-rule expression profile predictions
  35. This function plots panels b,c,and d. Panel a is schematic and was drawn in biorender.com
  36. """
  37. ##pair rule prediction across AP axis - qualitative comparison
  38. #figure 2 panel b - Prd prediction and supplementary Run and Eve prediction
  39. pair_rule_prediction_across_positions()
  41. #figure 2 panel c -reconstruction error across AP axis, d - summary results - mean prediction error per pair-rule gene:
  42. plot_all_pr_genes_mean_prediction_error()
  44. def plot_figure3_panels():
  45. """
  46. This figure presents position decoding and pair-rule expression prediction results
  47. for Osk mutant background embryos
  48. :return:
  49. """
  50. plot_wt_gap_gene()
  51. plot_all_mutant_results(mutant_type='osk')
  53. ##mutant pair-rule prediction and errors:
  54. plot_mutant_pr_predictions_and_errors(mutant_type='osk', plot_binned_position_errors=True)
  56. plot_one_mutation_expression_correlation()
  61. def plot_figure5_panels():
  62. for mutant_type in MUTANT_TYPES:
  63. print(mutant_type)
  64. plot_all_mutant_results(mutant_type)
  67. def plot_figure6_panels():
  68. for mutant_type in MUTANT_TYPES:
  69. print(mutant_type)
  70. fig, axes = plt.subplots(1, 6, figsize=(
  71. 24, 5)) # 1 row, 6 columns, wider width (sharex=False and sharey=False by default)
  73. handles = [] # Store handles for the legend
  74. labels = []
  75. mean_wt_pr_exp = get_mean_wt_pr_per_position(EDGE_TRIM)
  76. mutant_pr = get_mutant_pr_data(mutant_type)
  77. mutant_pr_arr = reformat_exp_data_to_arr(mutant_pr)[:, EDGE_TRIM:-EDGE_TRIM, :]
  78. min_vals = np.min(mutant_pr_arr, axis=1, keepdims=True)
  79. max_vals = np.max(mutant_pr_arr, axis=1, keepdims=True)
  81. # Normalize the data
  82. mutant_pr_arr_normed = (mutant_pr_arr - min_vals) / (max_vals - min_vals)
  83. mean_pr_exp_per_gene = np.mean(mutant_pr_arr_normed, axis=0)[1:-1, :]
  84. std_pr_exp_per_gene = np.std(mutant_pr_arr_normed, axis=0)[1:-1, :]
  86. wn_map = TestResults.from_pickle(DROSO_RES_DIR, f'{mutant_type}_wn', GAP_GENES)
  87. sc_map = TestResults.from_pickle(DROSO_RES_DIR, f'{mutant_type}_sc', GAP_GENES)
  88. sc_pr_pred = sc_map.expected_pr_exp(mean_wt_pr_exp)[:, 1:-1, :]
  89. wn_pr_pred = wn_map.expected_pr_exp(mean_wt_pr_exp[1:-1, :])
  90. positions = np.linspace(0.1, 0.9, sc_pr_pred.shape[1])
  92. mean_wn_pred = np.mean(wn_pr_pred, axis=0)
  93. std_wn_pred = np.std(wn_pr_pred, axis=0)
  95. mean_sc_pred = np.mean(sc_pr_pred, axis=0)
  96. std_sc_pred = np.std(sc_pr_pred, axis=0)
  98. for i, gene in enumerate(PAIR_RULE_GENES[:3]):
  99. # WN plot
  100. ax_wn = axes[2 * i]
  101. ax_wn.fill_between(positions, mean_pr_exp_per_gene[:, i] - std_pr_exp_per_gene[:, i],
  102. mean_pr_exp_per_gene[:, i] + std_pr_exp_per_gene[:, i], color='grey', alpha=0.3)
  103. ax_wn.fill_between(positions, mean_wn_pred[:, i] - std_wn_pred[:, i],
  104. mean_wn_pred[:, i] + std_wn_pred[:, i], color=DECODER_TYPE_COLOR['wn'], alpha=0.3)
  105. line_wn = ax_wn.plot(positions, mean_wn_pred[:, i], color=DECODER_TYPE_COLOR['wn'])[0]
  106. line_pr = ax_wn.plot(positions, mean_pr_exp_per_gene[:, i], color='grey')[0]
  107. if i == 0:
  108. ax_wn.set_ylabel('Gene Expression')
  109. ax_wn.set_yticks(np.arange(0, 1.05, .2)) # Set yticks for leftmost plot
  110. else:
  111. ax_wn.set_yticks([]) # Remove yticks for the other plots
  113. # SC plot
  114. ax_sc = axes[2 * i + 1]
  115. ax_sc.fill_between(positions, mean_pr_exp_per_gene[:, i] - std_pr_exp_per_gene[:, i],
  116. mean_pr_exp_per_gene[:, i] + std_pr_exp_per_gene[:, i], color='grey', alpha=0.3)
  117. ax_sc.fill_between(positions, mean_sc_pred[:, i] - std_sc_pred[:, i],
  118. mean_sc_pred[:, i] + std_sc_pred[:, i], color=DECODER_TYPE_COLOR['sc'], alpha=0.3)
  119. line_sc = ax_sc.plot(positions, mean_sc_pred[:, i], color=DECODER_TYPE_COLOR['sc'])[0]
  120. ax_sc.plot(positions, mean_pr_exp_per_gene[:, i], color='grey', linewidth=2) # Add grey line here
  121. ax_sc.set_yticks([]) # Remove yticks for SC plot
  123. # Add gene name in the bottom-right corner of each subplot (larger and bold)
  124. # ax_sc.text(0.95, 0.05, f'{gene}', transform=ax_sc.transAxes, ha='right', va='bottom', fontsize=22,
  125. # fontweight='bold')
  126. # ax_wn.text(0.95, 0.05, f'{gene}', transform=ax_wn.transAxes, ha='right', va='bottom', fontsize=22,
  127. # fontweight='bold')
  128. ax_sc.text(0.5, 0.85, f'{gene}', transform=ax_sc.transAxes, ha='center', va='bottom', fontsize=22,
  129. fontweight='bold')
  130. ax_wn.text(0.5, 0.85, f'{gene}', transform=ax_wn.transAxes, ha='center', va='bottom', fontsize=22,
  131. fontweight='bold')
  133. # Collect handles and labels for the legend
  134. if i == 0:
  135. handles.extend([line_pr, line_wn, line_sc])
  136. labels.extend([f'Pair Rule Gene Mean Expression', DECODER_NAMES["wn"]+" Prediction", DECODER_NAMES["sc"]+" Prediction"])
  138. # Add a single legend for all subplots outside of the plots
  139. fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.03), ncol=3)
  141. for ax in axes:
  142. ax.set_xlabel('Position (x/L)')
  144. # Adjust layout to make more space and remove excess white space
  145. plt.subplots_adjust(top=0.85, bottom=0.2, left=0.05, right=0.97, hspace=0.05, wspace=0.05)
  146. plt.show()

_figures.py at commit ee57cd5, no license · at the source

Overview

Authors: Michal Erez1, Roy Friedman1, Mor Nitzan1,2,3
ORCID iDs: Roy Friedman, Mor Nitzan
  1. School of Computer Science and Engineering, The Hebrew University,Jerusalem, Israel
  2. Racah Institute of Physics, The Hebrew University,Jerusalem, Israel
  3. Faculty of Medicine, The Hebrew University,Jerusalem, Israel
Institutions: Hebrew University of Jerusalem (Israel)
Journal: Molecular systems biology, volume 22, issue 7, pages 1118-1131
Dates: received 10 February 2025; accepted 15 April 2026; published online 5 May 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44320-026-00211-y · PMID 42086862 · PMCID PMC13328375 · OpenAlex W4408608044
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.03.17.643609
Status: code verified
Categories: drosophila (organism)
Methods: Connectivity, Machine learning
Keywords: Chromatin, Transcription & Genomics, Computational Biology, Development
MeSH: Body Patterning*, Drosophila melanogaster*, Animals, Drosophila, Drosophila Proteins, Embryo, Nonmammalian, Gene Expression Regulation, Developmental (* major topic)
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: EC | Horizon Europe | Excellent Science | HORIZON EUROPE European Research Council (ERC) (DecodeSC,101040660)
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

During development, cells reliably establish their identities, a process that is enabled in part by positional information encoded in gene expression patterns. Previous works showed that cells in Drosophila embryos can utilize this information to decode their position along the anterior-posterior axis with a 1% embryo-length positional precision. However, this precision is insufficient to uniquely determine position, leading to a positional information gap. Here, we propose a neighborhood-informed information-theoretic framework which allows to quantitatively estimate the amount of information regarding position which exists in the microenvironment of each cell. We formulate how much additional information exists in neighboring cells as a function of spatial variation in gene expression. We show that the additional information encoded by local neighborhoods is sufficient to uniquely specify cell identities, closing the information gap on average across major patterning axes in Drosophila embryos, gastruloids, and the developing neural tube. Furthermore, neighborhood-informed decoders predict cell positions and downstream gene expression patterns more accurately than cell-independent decoders, resulting in lower decoding variability, which is maintained in mutant Drosophila embryos. Our results provide a basis for the analysis of cellular decision-making in the context of their microenvironments.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

nitzanlab/Neighborhood-Informed-Positional-Information

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ee57cd5673b5a6c0aae81d49241e87d49c764ac9, 22 January 2025
Languages: Python (12)
Size: 13 files, 12 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/nitzanlab/Neighborhood-Informed-Positional-Information

The paper's code and data availability statement is in the Data section.

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The computer code produced in this study is available on GitHub (https://github.com/nitzanlab/Neighborhood-Informed-Positional-Information).

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44320-026-00211-y (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44320-026-00211-y).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 3 keywords, 7 MeSH terms, 1 funder, 36 references.

Cite

This paper

Erez, M., Friedman, R., & Nitzan, M. (2026). Neighborhood-informed positional information for precise cell identity specification. Molecular systems biology, 22(7), 1118-1131. https://doi.org/10.1038/s44320-026-00211-y

BibTeX

@article{erez2026neighborhood,
author = {Erez, Michal and Friedman, Roy and Nitzan, Mor},
title = {{Neighborhood-informed positional information for precise cell identity specification}},
journal = {Molecular systems biology},
year = {2026},
month = may,
volume = {22},
number = {7},
pages = {1118--1131},
publisher = {Nature Publishing Group},
issn = {1744-4292},
doi = {10.1038/s44320-026-00211-y},
url = {https://doi.org/10.1038/s44320-026-00211-y},
pmid = {42086862},
pmcid = {PMC13328375}
}

RIS

TY - JOUR
AU - Erez, Michal
AU - Friedman, Roy
AU - Nitzan, Mor
TI - Neighborhood-informed positional information for precise cell identity specification
T2 - Molecular systems biology
J2 - Mol Syst Biol
PY - 2026
DA - 2026/05/05
VL - 22
IS - 7
SP - 1118
EP - 1131
SN - 1744-4292
PB - Nature Publishing Group
DO - 10.1038/s44320-026-00211-y
UR - https://doi.org/10.1038/s44320-026-00211-y
LA - en
ER -

CSL-JSON

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