E12_v2_exocrine_seur_ob - E12.5 V2 Dataset. Only exocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5d E14_v2_exocrine_seur_ob - E14.5 V2 Dataset. Only exocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5d E17_v2_agg_exocrine_seur_ob - E17.5 aggregated V2 Dataset. Only exocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5d E12_v2_endocrine_seur_ob - E12.5 V2 Dataset. Only endocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5e E14_v2_endocrine_seur_ob - E14.5 V2 Dataset. Only endocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5e E17_v2_agg_endocrine_seur_ob - E17.5 aggregated V2 Dataset. Only endocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Supplementary Fig. 5e merged_v2_endocrine_seur_ob. - E12.5, E14.5, E17.5 (aggregated) V2 Dataset. Only endocrine cells. Grouped by "merged" in @meta.data slot. Corresponds to Fig. 5h. Generated by CCA_aligment_v2.R and CCA_merging_v2.R scripts. E14_fev_lineage_endocrine_seur_ob. - E14.5 Fev-Cre;mTmG lineage traced V2 Dataset. Only endocrine cells. Grouped by "res.1.4" in @meta.data slot. Corresponds to Fig. 6h. "GFP" and "GFP_Bi" in @meta.data refer to GFP counts and binary "yes" or "no" for cells with >0 GFP counts.

Phylogenetic tree for V6 30hpf embryo #7A tree created with the PHYLIP Mix package from DNA sequenced from a 30hpf zebrafish embryo with an integrated GESTALT barcode, exposed to CRISPR/Cas9 editing reagents.embryos_1_7.jsonfish_ADR1_PHYLIP_MIX_gte5a maximum parsimony tree of GESTALT barcodes collected from adult zebrafish ADR1. The tree was assembled using the PHYLIP Mix software package and annotated into a JSON data object for visualizationfish_ADR2_PHYLIP_MIX_gt5a maximum parsimony tree of GESTALT barcodes collected from adult zebrafish ADR2. The tree was assembled using the PHYLIP Mix software package and annotated into a JSON data object for visualizationcell_culture_gte2a maximum parsimony tree of GESTALT barcodes collected from cell culture lineages. The tree was assembled using the PHYLIP Mix software package and annotated into a JSON data object for visualizationembryos_1_7the raw maximum parsimony tree set output of GESTALT barcodes collected from embryo #7, a 30hpf zebraf...

Datasets necessary for analysis of heart regeneration data in R after moslin transition matrix computations.

Files:

Tree_list.rds: dataset of all single cell lineage trees. To be read in R. Zebrafish_cell_type_colors.csv: colors per cell type for plotting purposes. Zebrafish_metadata.csv: single cell annotations (barcode, sample, cell types, timepoint etc.) Tree_times: timepoint for every single cell lineage tree.

Objects relate to datasets originally published in Hu, Lelek, Spanjaard et al. (Nature Genetics 2022). Please cite the original publication: https://www.nature.com/articles/s41588-022-01129-5

This dataset contains scRNA-sequencing of endogenous H2Db-gp33 specific CD8 T cells in acute and chronic LCMV infection at days 4,8,28, and 40 post-infection (linked Dryad submission). There is also scRNA sequencing with CRISPR features (perturbSeq) of P14 Cas9 T cells in acute infection with a sgRNA library targeting ~40 transcription factors and epigenetic modulators (this Dryad submission). In addition, there is bulk atac-seq data of WT and KLF2 KO P14 T cells from day 8 of acute and chronic infection (linked Dryad submission). Finally data from Connolly et al, Science Immunology 2021 was reanalyzed and an rds object is uploaded here. This data is scRNA of H2Db-gp33 specific CD8s from KP-NINJA autochthonous lung tumor and tdLN. Raw sequencing files are included of all data in addition to bigwig files of atac data and rds objects of single cell data. Dryad_data_key.xls contains details on all samples for this and linked upload.

These are processed AnnData objects (converted from Seurat objects) for GeneTrajectory tutorials (https://github.com/KlugerLab/GeneTrajectory-python/):Human myeloid dataset analysisMyeloid cells were extracted from a publicly available 10x scRNA-seq dataset (https:// support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc 10k v3). QC was performed using the same workflow in (https://github.com/satijalab/ Integration2019/blob/master/preprocessing scripts/pbmc 10k v3.R). After standard normalization, highly-variable gene selection and scaling using the Seurat R package, we applied PCA and retained the top 30 principal components. Four sub-clusters of myeloid cells were identified based on Louvian clustering with a resolution of 0.3. Wilcoxon rank-sum test was employed to find cluster-specific gene markers for cell type annotation.For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel, each bandwidth is determined by the distance to its k-nearest neighbor, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 5 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 0.5% − 75% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (11,21,8) to extract three gene trajectories.Mouse embryo skin data analysisWe separated out dermal cell populations from the newly collected mouse embryo skin samples. Cells from the wildtype and the Wls mutant were pooled for analyses. After standard normalization, highly-variable gene selection and scaling using Seurat, we applied PCA and retained the top 30 principal components. Three dermal celltypes were stratified based on the expression of canonical dermal markers, including Sox2, Dkk1, and Dkk2. For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel bandwidth, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 10 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 1% − 50% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (9,16,5) to sequentially extract three gene trajectories. To compare the differences between the wiltype and the Wls mutant, we stratified Wnt-active UD cells into seven stages according to their expression profiles of the genes binned along the DC gene trajectory.

These are processed Seurat objects for the two biological datasets in GeneTrajectory inference (https://github.com/KlugerLab/GeneTrajectory/):Human myeloid dataset analysisMyeloid cells were extracted from a publicly available 10x scRNA-seq dataset (https:// support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc 10k v3). QC was performed using the same workflow in (https://github.com/satijalab/ Integration2019/blob/master/preprocessing scripts/pbmc 10k v3.R). After standard normalization, highly-variable gene selection and scaling using the Seurat R package, we applied PCA and retained the top 30 principal components. Four sub-clusters of myeloid cells were identified based on Louvian clustering with a resolution of 0.3. Wilcoxon rank-sum test was employed to find cluster-specific gene markers for cell type annotation.For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel, each bandwidth is determined by the distance to its k-nearest neighbor, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 5 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 0.5% − 75% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (11,21,8) to extract three gene trajectories. Mouse embryo skin data analysisWe separated out dermal cell populations from the newly collected mouse embryo skin samples. Cells from the wildtype and the Wls mutant were pooled for analyses. After standard normalization, highly-variable gene selection and scaling using Seurat, we applied PCA and retained the top 30 principal components. Three dermal celltypes were stratified based on the expression of canonical dermal markers, including Sox2, Dkk1, and Dkk2. For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel bandwidth, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 10 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 1% − 50% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (9,16,5) to sequentially extract three gene trajectories. To compare the differences between the wiltype and the Wls mutant, we stratified Wnt-active UD cells into seven stages according to their expression profiles of the genes binned along the DC gene trajectory.

R objects for the V1 Datasets. Created with R package Seurat.E14_allcells_seur_ob.Rdata - E14.5 V1 Dataset. Includes all cells. Grouped by "ordered_manuscript" in @data.info slot. Corresponds to Fig. 1c. E14_mesenchyme_seur_ob.Rdata - E14.5 V1 Dataset. Only mesenchymal cells. Grouped by "ordered_manuscript" in @data.info slot. Corresponds to Fig. 2a. merged_mesenchyme_seur_ob.Rdata - E12.5, E14.5, E17.5 merged V1 Dataset. Only mesenchymal cells. Grouped by "ordered_manuscript" in @data.info slot. Corresponds to Fig. 3a. E14_epithelial_seur_ob.Rdata - E14.5 V1 Dataset. Only epithelial cells. Grouped by "ordered" in @data.info slot. Corresponds to Fig. 4a. E14_endocrine_seur_ob.Rdata - E14.5 V1 Dataset. Only endocrine cells. Grouped by "ordered_res1_5" in @data.info slot. Corresponds to Fig. 4f. merged_epithelial_seur_ob.Rdata - E12.5, E14.5, E17.5 merged V1 Dataset. Only epithelial cells. Grouped by "ordered" in @data.info slot. Corresponds to Supplementary Fig.5a