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Supplemental data for Berg et al. (2025)

This repository contains derived data products for Berg et al. (2025).

For primary connectome data artefacts (segmentation, synapse detection, etc.) as well as a range of interactive online resources, please see https://male-cns.janelia.org/.

Quantification of neuron and connection counts

[quantify-neuron-connections.ipynb]

A Python notebook counting the number of neurons and connections between them (after thresholding by connection strength) based on our published data exports.

Sensorimotor Information Flow

Maxflow values across all neuron-neuron edges for all combinations of sensory modality and motor domains.

Example with code

Unzip the archive and read one of the sensory->motor flows:

>>> import pandas as pd
>>> df = pd.read_parquet("maxflow_sensorimotor_edges.20250911T1351.parquet/sensory=chemosensory/motor=am/184b5da9b9f74cb78ea698fcbb4b43cf-0.parquet")
>>> df.head()
   weight    pre   post
0  0.0001  10001  10654
1  0.0001  10001  10924
2  0.0023  10002  10863
3  0.0015  10002  10927
4  0.0006  10005  11051

Cluster assignments by type for all descending and ascending neurons.

Example with code
>>> import pandas as pd
>>> df = pd.read_csv("dnan_cluster_function_20250919.csv")
>>> df.head()
   bodyId  ...                            reference
0   10001  ...  Lima 2005, Kennedy and Broadie 2018
1   10010  ...  Lima 2005, Kennedy and Broadie 2018
2   10026  ...                                  NaN
3   10030  ...                                  NaN
4   10033  ...                                  NaN

Graph traversal layers

Mean, median, minimum and maximum layers after 10,000 iterations of graph traversal; layer_min is the earliest traversal across all runs, layer_max is the latest; layer_mean is the average across all runs, layer_median is the median. Seeds were all sensory neurons (as per their superclass).

Example with code
>>> import pandas as pd
>>> df = pd.read_feather("sensory_network_traversal_model_layers.feather")
>>> df.head()
    node  layer_min  layer_max  layer_mean  layer_median
0  10001          2          6    4.081481           4.0
1  10002          3          6    4.251852           4.0
2  10003          3          7    5.066667           5.0
3  10005          3          7    5.244444           5.0
4  10006          3          7    5.111111           5.0

Optic lobe column assignment

For each visual column of the left and right optic lobe: IDs of the corresponding L1, R7 and R8 neurons, and column type (pale, yellow, edge, dorsal rim area). Additional columns indicate the presence of identified vertical aMe12 and/or Tm5a branches which serve as anatomical markers to identify likely pale/yellow columns (see Methods).

Example with code
>>> import pandas as pd
>>> df = pd.read_excel("optic-column-type-assignments.xlsx")
>>> df.head()
           column     L1      R7  ... aMe12_branch  Tm5a_branch Notes
0  ME_R_col_10_06  39722  230050  ...            1            0   NaN
1  ME_R_col_18_12  36897     -99  ...            1            1   NaN
2  ME_R_col_20_09  26900     -99  ...            1            1   NaN
3  ME_R_col_21_08  26152     -99  ...            1            1   NaN
4  ME_R_col_21_12  30138     -99  ...            1            1   NaN

Male-female aligned edges

Edge weights between cross-matched male CNS and FlyWire cell types with arbors in the central brain. Connections made within the VNC portion of the male CNS have been excluded:

  • pre/post: pre- and postsynaptic cross-matched cell type, respectively
  • weight_m/_f: total synapses between the pre- and postsynaptic type in male and female, respectively
  • t: t-statistics as described in Methods
  • p_corr: p-values after false-discovery rate correction
  • verdict_corr: edge dimorphism after fixing false-positives (connections between isomorphic types; “dimorphic” -> “isomorphic”) and false-negatives (connections involving sex-specific types; “isomorphic” -> “dimorphic”)
Example with code
>>> import pandas as pd
>>> df = pd.read_feather("mcns_fw_edge_comp.feather")
>>> df.head()
                pre              post  ...    p_corr  verdict_corr
0  (PLP191,PLP192)b  (PLP191,PLP192)b  ...  0.177720    isomorphic
1  (PLP191,PLP192)b        5-HTPMPV03  ...  0.746035         noise
2  (PLP191,PLP192)b     AOTU044,LTe43  ...  0.500310         noise
3  (PLP191,PLP192)b           AOTU065  ...  0.746035         noise
4  (PLP191,PLP192)b          AVL006_a  ...  0.500310         noise

Male-female mappings

The neuron-to-label assignment for male CNS and FlyWire neurons used to generate the cross-matched edges. Each unique label corresponds to a cross-matched group of neurons. In cases where the neurons have the exact same cell type name, the label correspond to that type. In cases where a group of neurons has different cell type names in male CNS and FlyWire, the label is chosen arbitrarily.

Example with code
>>> import json
>>> with open("mcns_fw_edge_comp_mappings.json") as f:
...     data = json.load(f)
>>> list(data.items())[:2]
[('79556', 'ANG_GNG_94,AN_GNG_117'), ('85165', 'ANG_GNG_94,AN_GNG_117')]

Synapse precision and recall

The precision and recall of pre-synapse (t-bar) predictions based on validation data collected in 81 neuropil compartments. This is the source data for the upper plot in Fig 1i of Berg et al. (2025).

The precision and recall of predicted pre-to-post synaptic connections based on validation data collected in 81 neuropil compartments. This is the source data for the lower plot in Fig 1i of Berg et al. (2025).

Citation

@article{Berg2025,
  author = {Berg,  Stuart and Beckett,  Isabella R and Costa,  Marta and Schlegel,  Philipp and Januszewski,  Michal and Marin,  Elizabeth C and Nern,  Aljoscha and Preibisch,  Stephan and Qiu,  Wei and Takemura,  Shin-ya and Fragniere,  Alexandra M C and Champion,  Andrew S and Adjavon,  Diane-Yayra and Cook,  Michael and Gkantia,  Marina and Hayworth,  Kenneth J and Huang,  Gary B and Kampf,  Florian and Katz,  William T and Lu,  Zhiyuan and Ordish,  Christopher and Paterson,  Tyler and Stuerner,  Tomke and Trautman,  Eric T and Whittle,  Catherine R and Burnett,  Laura E and Hoeller,  Judith and Li,  Feng and Loesche,  Frank and Morris,  Billy J and Pietzsch,  Tobias and Pleijzier,  Markus W and Silva,  Valeria and Yin,  Yijie and Ali,  Iris and Badalamente,  Griffin and Bates,  Alexander Shakeel and Bogovic,  John and Brooks,  Paul and Cachero,  Sebastian and Canino,  Brandon S and Chaisrisawatsuk,  Bhumpanya and Clements,  Jody and Crowe,  Arthur and de Haan Vicente,  Ines and Dempsey,  Georgia and Dona,  Erika and dos Santos,  Marcia and Dreher,  Marisa and Dunne,  Christopher R and Eichler,  Katharina and Finley-May,  Samantha and Flynn,  Miriam A and Hameed,  Imran and Hopkins,  Gary Patrick and Hubbard,  Philip M and Kiassat,  Ladann and Kovalyak,  Julie and Lauchie,  Shirley A and Leonard,  Meghan and Lohff,  Alanna and Longden,  Kit D and Maldonado,  Charli A and Mitletton,  Myrto and Moitra,  Ilina and Moon,  Sung Soo and Mooney,  Caroline and Munnelly,  Eva J and Okeoma,  Nneoma and Olbris,  Donald J and Pai,  Anika and Patel,  Birava and Phillips,  Emily M and Plaza,  Stephen M and Richards,  Alana and Rivas Salinas,  Jennifer and Roberts,  Ruairi J V and Rogers,  Edward M and Scott,  Ashley L and Scuderi,  Louis A and Seenivasan,  Pavithraa and Serratosa Capdevila,  Laia and Smith,  Claire and Svirskas,  Rob and Takemura,  Satoko and Tastekin,  Ibrahim and Thomson,  Alexander and Umayam,  Lowell and Walsh,  John J and Whittome,  Holly and Xu,  C Shan and Yakal,  Emily A and Yang,  Tansy and Zhao,  Arthur and George,  Reed and Jain,  Viren and Jayaraman,  Vivek and Korff,  Wyatt and Meissner,  Geoffrey W and Romani,  Sandro and Funke,  Jan and Knecht,  Christopher and Saalfeld,  Stephan and Scheffer,  Louis K and Waddell,  Scott and Card,  Gwyneth M and Ribeiro,  Carlos and Reiser,  Michael B and Hess,  Harald F and Rubin,  Gerald M and Jefferis,  Gregory S X E},
  title = {Sexual dimorphism in the complete connectome of the Drosophila male central nervous system},
  DOI = {10.1101/2025.10.09.680999},
  publisher = {Cold Spring Harbor Laboratory},
  year = {2025},
}

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