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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We next multiplied these normalized read counts by the median gene length across all genes in the datasets and treated those length-normalized counts equivalently to raw counts from the datasets obtained with the help of unique molecular identifiers in our downstream analyses.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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As a next step we generated a log-normalized expression matrix by first dividing the counts for each cell by the total counts in that cell and multiplying by a factor of 1,000,000 before taking the natural logarithm of each count + 1.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We computed 3,000 highly variable features in a batch-aware manner using the scanpy highly_variable_genes function (flavor = ‘seurat_v3’, batch_key = ‘bio_sample’).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Here, bio_sample represents biological samples as provided in the original metadata of the datasets.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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On average, 72% of the 3,000 highly variable genes were reported in each of the constituent HNOCA datasets.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We used these 3,000 features to compute a 50-dimensional representation of the data using principal component analysis (PCA), which in turn we used to compute a k-nearest-neighbour (kNN) graph (n_neighbors = 30, metric = ‘cosine’).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Using the neighbour graph we computed a two-dimensional representation of the data using UMAP and a coarse (resolution 1) and fine (resolution 80) clustering of the unintegrated data using Leiden clustering.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Snapseed is a scalable auto-annotation strategy, which annotates cells on the basis of a provided hierarchy of cell types and the corresponding cell type markers.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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It is based on enrichment of marker gene expression in cell clusters (high-resolution clustering is preferred), and data integration is not necessarily required.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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In this study, we used snapseed to obtain initial annotations for label-aware integration.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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First, we constructed a hierarchy of cell types including progenitor, neuron and non-neural types, each defined by a set of marker genes (Supplementary Data 1).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Next, we represented the data by the RSS to average expression profiles of cell clusters in the recently published human developing brain cell atlas.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We then constructed a kNN graph (k = 30) in the RSS space and clustered the dataset using the Leiden algorithm (resolution 80).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For both steps, we used the graphical processing unit (GPU)-accelerated RAPIDS implementation that is provided through scanpy.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For all cell type marker genes on a given level in the hierarchy, we computed the area under the receiver operating characteristic curve (AUROC) as well as the detection rate across clusters.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For each cell type, a score was computed by multiplying the maximum AUROC with the maximum detection rate among its marker genes.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Each cluster was then assigned to the cell type with the highest score.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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This procedure was performed recursively for all levels of the hierarchy.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The same procedure was carried out using the fine (resolution 80) clustering of the unintegrated data to obtain cell type labels for the unintegrated dataset that were used downstream as a ground-truth input for benchmarking integration methods.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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This auto-annotation strategy was implemented in the snapseed Python package and is available on GitHub (https://github.com/devsystemslab/snapseed).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Snapseed is a light-weight package to enable scalable marker-based annotation for atlas-level datasets in which manual annotation is not readily feasible.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The package implements three main functions: annotate() for non-hierarchical annotation of a list of cell types with defined marker genes, annotate_hierarchy() for annotating more complex, manually defined cell type hierarchies and find_markers() for fast discovery of cluster-specific features.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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All functions are based on a GPU-accelerated implementation of AUROC scores using JAX (https://github.com/google/jax).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We performed integration of the organoid datasets for HNOCA using the scPoli model from the scArches package.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We defined the batch covariate for integration as a concatenation of the dataset identifier (annotation column ‘id’), the annotation of biological replicates (annotation column ‘bio_sample’) as well as technical replicates (annotation column ‘tech_sample’).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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This resulted in 396 individual batches.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The batch covariate is represented in the model as a learned vector of size five.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We used the top three levels of the RSS-based snapseed cell type annotation as the cell type label input for the scPoli prototype loss.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We chose the hidden layer size of the one-layer scPoli encoder and decoder as 1,024, and the latent embedding dimension as ten.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We used a value of 100 for the ‘alpha_epoch_anneal’ parameter.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We did not use the unlabelled prototype pretraining.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We trained the model for a total of seven epochs, five of which were pretraining epochs.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To quantitatively compare the organoid atlas integration results from several tools, we used the GPU-accelerated scib-metrics Python package (v.0.3.3) and used the embedding with the highest overall performance for all downstream analyses.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We compared the data integration performance across the following latent representations of the data: unintegrated PCA, RSS integration, scVI (default parameters except for using two layers, latent space of size 30 and negative binomial likelihood) integration, scANVI (default parameters) integrations using snapseed level 1, 2 or 3 annotation as cell type label input, scPoli (parameters shown above) integrations using either snapseed level 1, 2 or 3 annotation or all three annotation levels at once as the cell type label input, scPoli integrations of metacells aggregated with the aggrecell algorithm (first used as ‘pseudocell’) using either snapseed level 1 or 3 annotation as the cell type label input to scPoli.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We used the following scores for determining integration quality (each described in ref. ):
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Leiden normalized mutual information score, Leiden adjusted rand index, average silhouette width per cell type label, isolated label score (average silhouette width-scored) and cell type local inverse Simpson’s index to quantify conservation of biological variability.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To quantify batch-effect removal, we used average silhouette width per batch label, integration local inverse Simpson’s index, kNN batch-effect test score and graph connectivity.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Integration approaches were then ranked by an aggregate total score of individually normalized (into the range of ) metrics.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Before we carried out the benchmarking, we iteratively removed any cells from the dataset that had an identical latent representation to another cell in the dataset until no latent representation contained any more duplicate rows.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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This procedure removed a total of 3,293 duplicate cells (0.002% of the whole dataset) and was required for the benchmarking algorithm to complete without errors.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We used the snapseed level 3 annotation computed on the unintegrated PCA embedding as ground-truth cell type labels in the integration.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To infer a global ordering of differentiation state, we sought to infer a real-time-informed pseudotime on the basis of neural optimal transport in the scPoli latent space.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We first grouped organoid age in days into seven bins ((0, 15], (15, 30], (30,60], (60, 90], (90, 120], (120, 150], (150, 450]).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Next, we used moscot to solve a temporal neural problem.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To score the marginal distributions on the basis of expected proliferation rates, we obtained proliferation and apoptosis scores for each cell with the method score_genes_for_marginals().
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Marginal weights were then computed with[12pt] $$ (4 (}-}))$$(4×(prolif_score−apoptosis_score)) The optimal transport problem was solved using the following parameters: iterations = 25,000, compute_wasserstein_baseline = False, batch_size = 1,024, patience = 100, pretrain = True, train_size = 1.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To compute displacement vectors for each cell in age bin i, we used the subproblem corresponding to the [i, i + 1] transport map, except for the last age bin, where we used the subproblem [i − 1,i].
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Displacement vectors were obtained by subtracting the original cell distribution from the transported distribution.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Using the velocity kernel from CellRank we computed a transition matrix from displacement vectors and used it as an input for computing diffusion maps.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Ranks on negative diffusion component 1 were used as a pseudotemporal ordering.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The cell ranger-processed scRNA-seq data for the primary atlas were obtained from the link provided on its GitHub page (https://storage.googleapis.com/linnarsson-lab-human/human_dev_GRCh38-3.0.0.h5ad).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For further quality control, cells with fewer than 300 detected genes were filtered out.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Transcript counts were normalized by the total number of counts for that cell, multiplied by a scaling factor of 10,000 and subsequently natural-log transformed.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The feature set was intersected with all genes detected in the organoid atlas and the 2,000 most highly variable genes were selected with the scanpy function highly_variable_genes using ‘Donor’ as the batch key.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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An extra column of ‘neuron_ntt_label’ was created to represent the automatic classified neural transmitter transporter subtype labels derived from the ‘AutoAnnotation’ column of the cell cluster metadata (https://github.com/linnarsson-lab/developing-human-brain/files/9755350/table_S2.xlsx).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To compare our organoid atlas with data from the primary developing human brain, we used scArches to project it to the above mentioned primary human brain scRNA-seq atlas.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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We first pretrained a scVI model on the primary atlas with ‘Donor’ as the batch key.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The model was constructed with following parameters: n_latent = 20, n_layers = 2, n_hidden = 256, use_layer_norm = ‘both’, use_batch_norm = ‘none’, encode_covariates = True, dropout_rate = 0.2 and trained with a batch size of 1,024 for a maximum or 500 epochs with early stopping criterion.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Next, the model was fine-tuned with scANVI using ‘Subregion’ and ‘CellClass’ as cell type labels with a batch size of 1,024 for a maximum of 100 epochs with early stopping criterion and n_samples_per_label = 100.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To project the organoids atlas to the primary atlas, we used the scArches implementation provided by scvi-tools.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The query model was fine-tuned with a batch size of 1,024 for a maximum of 100 epochs with early stopping criterion and weight_decay = 0.0.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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With the primary reference and query (HNOCA) data projected to the same latent space, an unweighted bipartite kNN graph was constructed by identifying 100 nearest neighbours of each query cell in the reference data with either PyNNDescent or RAPIDS-cuML (https://github.com/rapidsai/cuml) in Python, depending on availability of GPU acceleration.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Similarly, a reference kNN graph was also built by identifying 100 nearest neighbours of each reference cell in the reference data.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For each edge in the reference-query bipartite graph, the similarity between the reference neighbours of the two linked cells, defined as A and B, respectively, is represented by the Jaccard index:[12pt] $$J(A,B)=.$$(A,B)=∣A∩B∣∣A∪B∣. The square of Jaccard index was then assigned as the weight of the edge, to get the bipartite weighted kNN graph between the reference and query datasets.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Given the wkNN estimated between primary reference and query (HNOCA), any categorical metadata label of reference can be transferred to query cells by means of majority voting.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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In brief, for each category, its support was calculated for each query cell as the sum of weights of edges that link to reference cells in this category.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The category with the largest support was assigned to the query cell.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To get the final regional labels for the non-telencephalic NPCs and neurons, as well as the NTT labels for non-telencephalic neurons, constraints were added to the transfer procedure.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For regional labels, only the non-telencephalic regions, namely diencephalon, hypothalamus, thalamus, midbrain, midbrain dorsal, midbrain ventral, hindbrain, cerebellum, pons and medulla, were considered valid categories to be transferred.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The label-transfer procedure was only applied to the non-telencephalic NPCs and neurons in HNOCA.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Before any majority voting was done, the support scores of each valid category across all non-telencephalic NPCs and neurons in HNOCA were smoothed with a random-walk-with-restart procedure (restart probability alpha, 85%).
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Next, a hierarchical label transfer, which takes into account the structure hierarchy, was applied.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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First, the considered regions were grouped into diencephalon, midbrain and hindbrain, with a support score of each structure as its score summed up with scores of its substructures.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Majority voting was applied to assign each cell to one of the three structures.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
|
Next, a second majority voting was applied to only consider the substructures under the assigned structure (for example, hypothalamus and thalamus for diencephalon).
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For NTT labels, we first identified valid region-NTT label pairs in the reference on the basis of the provided NTT labels in the reference neuroblast and neuron clusters and their most common regions.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Here, the most common regions were re-estimated in a hierarchical manner to the finest resolution mentioned above.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Next, when transferring NTT labels, for each non-telencephalic neuron with the regional label transferred, only NTT labels that were considered valid for the region were considered during majority voting.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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To match telencephalic NPCs and neurons in HNOCA to developmental stages, we used the recently published human neocortical development atlas as the reference.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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The processed single nucleus RNA-seq data were obtained from its data portal (https://cell.ucsf.edu/snMultiome/).
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Given the ‘class’, ‘subclass’ and ‘type’ labels in the provided metadata as annotations, and ‘individual’ as the batch label, scPoli was applied for label-aware data integration.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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Next, data representing different developmental stages were split.
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PMC11578878
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An integrated transcriptomic cell atlas of human neural organoids.
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For each stage, Louvain clustering based on the scPoli latent representation (resolution, 5) was applied.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
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Clusters of all stages were pooled, and highly variable genes were identified on the basis of coefficient of variations as described in this page: https://pklab.med.harvard.edu/scw2014/subpop_tutorial.html.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Finally, every one of HNOCA telencephalic NPCs and neurons were correlated to each cluster across the identified highly variable genes.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
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The stage label of the best-correlated cluster was assigned to the query HNOCA cell.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
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To extend the analysis to other neuronal cell types, the second-trimester multiple-region human brain atlas was also introduced.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
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The processed count matrices and metadata were obtained from the NeMO data portal (https://data.nemoarchive.org/biccn/grant/u01_devhu/kriegstein/transcriptome/scell/10x_v2/human/processed/counts/).
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Given the ‘cell_type’ label of the provided metadata as the annotation and ‘individual’ as the batch label, scPoli was run for label-aware data integration.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Louvain clustering was applied to the scPoli latent representation to identify clusters (resolution, 20).
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Similarly, Louvain clustering with a resolution of 20 was also applied to the first-trimester multiple-region human brain atlas on the basis of the scANVI latent representation we generated earlier.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Average expression profiles were calculated for all the clusters, and highly variable genes were identified using the same procedure as above for clusters of the two primary atlases combined.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Next, every NPC and neuron in HNOCA was correlated to the average expression profiles of those clusters.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
The best-correlated first- and second-trimester clusters, as well as the correlations, were identified.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
The differences between the two correlations were used as the metrics to indicate the stage-matching preferences of NPCs and neurons in HNOCA.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Given a reference dataset and a query dataset, the presence score is a score assigned to each cell in the reference, which describes the frequency or likelihood of the cell type or state of that reference cell appearing in the query data.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
In this study, we calculated the presence scores of primary atlas cells in each HNOCA dataset to quantify how frequently we saw a cell type or state represented by each primary cell in each of the HNOCA datasets.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Specifically, for each HNOCA dataset, we first subset the wkNN graph to only HNOCA cells in that dataset.
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PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
Next, the raw weighted degree was calculated for each cell in the primary atlas, as the sum of weights of the remaining edges linked to the cell.
|
PMC11578878
|
An integrated transcriptomic cell atlas of human neural organoids.
|
A random-walk-with-restart procedure was then applied to smooth the raw scores across the kNN graph of the primary atlas.
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