Zebrafish data for whole-embryo lineage reconstruction with linajea

This article provides access to the zebrafish data (160328) for "Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations" (Malin-Mayor et al. 2023, DOI: https://doi.org/10.1038/s41587-022-01427-7).Here we provide the ground truth tracks used to train the deep learning model, the trained networks, and the predicted tracks. Additionally, we provide information on how to access the image data, although it is not uploaded here due to size. Related artifacts include the source code for experiments and methods.Image DataThe image dataset in n5/zarr format (as used in Malin-Mayor et al. 2023) can be accessed at the following Dropbox link: https://www.dropbox.com/scl/fi/1qeac8uwvctq9q1y451lg/160328_zebrafish.tar.gz?rlkey=whs5hyxtacdet0ypwyroigara&dl=0. This image dataset was originally published in "Single-cell reconstruction of emerging population activity in an entire developing circuit" ( Wan, Y. et al. 2019, DOI: https://doi.org/10.1016/j.cell.2019.08.039). The image dataset has two channels, corresponding to two camera views that have been registered but not merged. While originally these channels each were anisotropic with a voxel size ratio of 6:1, we have resampled them to be isotropic by downsampling the dimension with larger resolution by 2 and upsampling the two dimensions with smaller resolution by 3.Ground Truth TracksInside gt_tracks.zip there are a number of files containing different subsets of tracks. Each has the following columns separated by tabs: time, z, y, x, cell_id, parent_id, track_id.tracks_side_1.txt and tracks_side_2.txt are the main files containg manual annotations of individual cells from start to end of video used to train the model. These tracks are sparse, but each cell included had its whole lineage traced as completely as possible from start to end of the video. These tracks were split based on which side of the center line of the embryo the cells were on: discarded.txt contains tracks that crossed the center line and were not used for training or testing.full_frame_divisions.txt is a set of manually annotated division points (points right before the cell divides) that are as complete as possible for target time points 50, 100, 150, 200, 250, 300, and 350 and adjacent frames, and were used for evaluation and not model training.Trained Modelstrained_networks.zip includes both networks trained on the zebrafish dataset. The config files are in the zebrafish_config_files directory, and the other directories are named corresponding to the model_name in each config file and contain the trained model files. There is one model trained/validated on each side of the embryo, and evaluated on the other side, as described in Supplemental Note 1.Predicted Trackspredicted_tracks.zip contains both the TGMM baseline results and the results for the linajea method. Each has the following columns separated by tabs: time, z, y, x, cell_id, parent_id, track_id, [node_score, edge_score] (node and edge score only present for lineajea predictions).The TGMM directory contains the TGMM results provided to us by the authors of the TGMM method. There is a separate result for each input channel.The linajea results are organized similarly to the trained models, with one text file for each side of the embryo. zebrafish_side_1_tracks_071621.txt contains tracks generated by the model trained on side 2 and predicted on side 1, and zebrafish_side_2_tracks_071621.txt contains tracks generated by the model trained on side 1 and predicted on side 2. Predictions were masked via a rough rectangle to only be done on the test "side", to save computation.本文提供了《自动化从稀疏标注学习实现全胚胎细胞谱系的全自动重建（Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations）》（Malin-Mayor等人，2023，DOI: https://doi.org/10.1038/s41587-022-01427-7）相关的斑马鱼数据集（160328）的访问权限。此处提供了用于训练深度学习模型的真值轨迹（ground truth tracks）、训练完成的网络模型，以及预测得到的细胞轨迹。此外，我们还提供了图像数据的获取途径，但因文件体积过大，未在此处上传。相关辅助资源还包含实验与方法的源代码。 图像数据 图像数据集采用n5/zarr格式（与Malin-Mayor等人2023年研究中使用的格式一致），可通过以下Dropbox链接获取：https://www.dropbox.com/scl/fi/1qeac8uwvctq9q1y451lg/160328_zebrafish.tar.gz?rlkey=whs5hyxtacdet0ypwyroigara&dl=0。该图像数据集最初发表于《完整发育回路中新兴群体活动的单细胞重建（Single-cell reconstruction of emerging population activity in an entire developing circuit）》（Wan, Y. 等人，2019，DOI: https://doi.org/10.1016/j.cell.2019.08.039）。该数据集包含两个通道，分别对应两个已完成配准但未融合的相机视角。原始数据中每个通道均为各向异性（anisotropic）体素，体素尺寸比为6:1；我们已将其重采样为各向同性（isotropic）：将分辨率较高的维度下采样2倍，将分辨率较低的两个维度上采样3倍。 真值轨迹 gt_tracks.zip 中包含多个文件，分别存储不同子集的细胞轨迹。每个文件均采用制表符分隔以下字段：时间（time）、z坐标、y坐标、x坐标、细胞ID（cell_id）、父细胞ID（parent_id）、轨迹ID（track_id）。 tracks_side_1.txt 与 tracks_side_2.txt 为核心文件，包含从视频起始至结束的人工标注单细胞轨迹，用于模型训练。这些轨迹属于稀疏标注，但所有纳入的细胞均已尽可能完整地追踪了从视频起始到结束的完整细胞谱系。这些轨迹根据细胞位于胚胎中心线的哪一侧进行划分：discarded.txt 包含跨越中心线的轨迹，未用于模型训练与测试。 full_frame_divisions.txt 是一套人工标注的细胞分裂点（细胞分裂前瞬间的坐标点），针对时间点50、100、150、200、250、300及350及其相邻帧进行了尽可能完整的标注，仅用于模型评估而非训练。 训练好的模型 trained_networks.zip 包含两个基于斑马鱼数据集训练的网络模型。配置文件存放在 zebrafish_config_files 目录中，其余目录的命名与各配置文件中的模型名称（model_name）一一对应，内部存储训练好的模型文件。如补充说明1所述，我们分别在胚胎的两侧进行模型训练与验证，并在对侧开展模型评估。 预测轨迹 predicted_tracks.zip 包含TGMM基线结果与linajea方法的预测结果。两者均采用制表符分隔以下字段：time、z、y、x、cell_id、parent_id、track_id，[节点得分、边得分]（仅linajea预测结果包含节点得分与边得分）。 TGMM目录存储了TGMM方法原作者提供的结果，每个输入通道对应一份独立结果。 linajea结果的组织方式与训练好的模型类似，为胚胎的每一侧生成一个文本文件。其中，zebrafish_side_1_tracks_071621.txt 包含使用侧2训练的模型在侧1上生成的预测轨迹，zebrafish_side_2_tracks_071621.txt 包含使用侧1训练的模型在侧2上生成的预测轨迹。为节省计算资源，预测过程通过粗略矩形掩码限定仅在测试侧进行。