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Source paper: PMC11578878
In this study, we built a large-scale integrated cell atlas of human neural organoids, the HNOCA, by integrating 1.8 million cells spanning 36 scRNA-seq datasets generated by 15 different laboratories worldwide using 26 different differentiation protocols as well as diverse scRNA-seq technologies.
|
[
{
"end": 113,
"label": "Tissue",
"start": 91,
"text": "human neural organoids"
},
{
"end": 158,
"label": "CellType",
"start": 153,
"text": "cells"
}
] |
Single_Cell
|
The resulting atlas revealed the high complexity of neuronal, glial and non-neural cell types that can develop in neural organoids grown under existing protocol conditions.
|
[
{
"end": 60,
"label": "CellType",
"start": 52,
"text": "neuronal"
},
{
"end": 67,
"label": "CellType",
"start": 62,
"text": "glial"
},
{
"end": 93,
"label": "CellType",
"start": 72,
"text": "non-neural cell types"
},
{
"end": 130,
"label": "Tissue",
"start": 114,
"text": "neural organoids"
}
] |
Single_Cell
|
Mapping the HNOCA data to various human developing brain cell reference atlases allowed comprehensive evaluation of neural organoid protocols to generate cell types of different brain regions.
|
[
{
"end": 191,
"label": "CellType",
"start": 154,
"text": "cell types of different brain regions"
}
] |
Single_Cell
|
We found that organoids in the first 3 months of culture best match to first-trimester primary data, whereas organoids around 3 months of culture and older best match second-trimester primary cell states.
|
[
{
"end": 23,
"label": "Tissue",
"start": 14,
"text": "organoids"
},
{
"end": 118,
"label": "Tissue",
"start": 109,
"text": "organoids"
}
] |
Single_Cell
|
We did not observe significant neuronal maturation and diversification signatures matching older developmental stages, suggesting a limitation of neuronal maturation in current neural organoid protocols.
|
[] |
Single_Cell
|
Source paper: PMC11578878
We performed DE analysis between organoid neuron types and their primary counterparts to evaluate transcriptomic fidelity, and identified metabolic changes related to the glycolysis pathway as a main factor that distinguishes organoid and primary cell states, consistent with previous reports.
|
[
{
"end": 82,
"label": "CellType",
"start": 61,
"text": "organoid neuron types"
}
] |
Single_Cell
|
Despite the negative effects of metabolic stress on overall transcriptomic fidelity, the molecular identity of regional cell types is maintained as evidenced by transcription factor coexpression patterns that are highly consistent with primary counterparts.
|
[
{
"end": 130,
"label": "CellType",
"start": 111,
"text": "regional cell types"
}
] |
Single_Cell
|
Source paper: PMC11578878
We showcased the mapping of query data, a recently published single-cell transcriptomic neural organoid morphogen screen, to the HNOCA and the primary reference, which enabled a refined cell type annotation, as well as a compositional comparison with existing neural organoid datasets.
|
[] |
Single_Cell
|
Our powerful framework will facilitate quantitative and comparative analysis of scRNA-seq data of human neural organoids, and for the benchmarking of new neural organoid protocols.
|
[
{
"end": 120,
"label": "Tissue",
"start": 98,
"text": "human neural organoids"
}
] |
Single_Cell
|
Source paper: PMC11578878
Consistent with earlier reports , we find that unguided protocols generate neural cells with high brain regional variability, which is useful when studying broader fate determination during neurodevelopment.
|
[
{
"end": 115,
"label": "CellType",
"start": 103,
"text": "neural cells"
}
] |
Single_Cell
|
Guided protocols resulted in a strong enrichment of the targeted brain regions.
|
[
{
"end": 78,
"label": "Tissue",
"start": 65,
"text": "brain regions"
}
] |
Single_Cell
|
We also note that some guided protocols, particularly those targeting midbrain, show relatively low specificity and generate neural cells from the nearby brain regions.
|
[
{
"end": 78,
"label": "Tissue",
"start": 70,
"text": "midbrain"
},
{
"end": 137,
"label": "CellType",
"start": 125,
"text": "neural cells"
},
{
"end": 167,
"label": "Tissue",
"start": 154,
"text": "brain regions"
}
] |
Single_Cell
|
This issue may be due to a differential response of neural stem cells in the organoid to the same morphogen cue, or to the lack of a full understanding of the timing, concentration and combinations of morphogens required to precisely define cells of the deeper regions in the central nervous system.
|
[
{
"end": 298,
"label": "Tissue",
"start": 276,
"text": "central nervous system"
},
{
"end": 85,
"label": "CellType",
"start": 52,
"text": "neural stem cells in the organoid"
},
{
"end": 268,
"label": "CellType",
"start": 241,
"text": "cells of the deeper regions"
}
] |
Single_Cell
|
Source paper: PMC11578878
We demonstrate how the HNOCA can be extended and updated by projecting extra single-cell transcriptomic data of neural organoids to the atlas.
|
[
{
"end": 156,
"label": "Tissue",
"start": 140,
"text": "neural organoids"
}
] |
Single_Cell
|
Further, we have developed a computational toolkit, HNOCA-tools, which will enable other researchers to recapitulate the analytic framework applied in our study.
|
[] |
Single_Cell
|
Together, we imagine that the HNOCA will be kept up to date and continue to reflect the landscape of human neural cell states generated in organoids in vitro, serving as a living resource for the neural organoid community that enables the assessment of organoid fidelity, the characterization of perturbed and diseased states and the development of new protocols.
|
[
{
"end": 148,
"label": "Tissue",
"start": 139,
"text": "organoids"
}
] |
Single_Cell
|
Source paper: PMC11578878
We included 33 human neural organoid data from a total of 25 publications plus three unpublished datasets in our atlas (Supplementary Table 1 ).
|
[] |
Single_Cell
|
We curated all neural organoid datasets used in this study through the sfaira framework (GitHub dev branch, 18 April 2023).
|
[] |
Single_Cell
|
For this, we obtained scRNA-seq count matrices and associated metadata from the location provided in the data availability section for every included publication or directly from the authors in case of unpublished data.
|
[] |
Single_Cell
|
We harmonized metadata according to the sfaira standards ( https://sfaira.readthedocs.io/en/latest/adding_datasets.html ) and manually curated an extra metadata column organoid_age_days, which described the number of days the organoid had been in culture before collection.
|
[
{
"end": 234,
"label": "Tissue",
"start": 226,
"text": "organoid"
}
] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 149,
"label": "CellType",
"start": 139,
"text": "cell types"
}
] |
Single_Cell
|
It is based on enrichment of marker gene expression in cell clusters (high-resolution clustering is preferred), and data integration is not necessarily required.
|
[] |
Single_Cell
|
Source paper: PMC11578878
In this study, we used snapseed to obtain initial annotations for label-aware integration.
|
[] |
Single_Cell
|
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 ).
|
[
{
"end": 76,
"label": "CellType",
"start": 70,
"text": "neuron"
},
{
"end": 47,
"label": "CellType",
"start": 37,
"text": "cell types"
},
{
"end": 68,
"label": "CellType",
"start": 58,
"text": "progenitor"
},
{
"end": 97,
"label": "CellType",
"start": 81,
"text": "non-neural types"
}
] |
Single_Cell
|
Next, we represented the data by the RSS to average expression profiles of cell clusters in the recently published human developing brain cell atlas .
|
[] |
Single_Cell
|
We then constructed a kNN graph ( k = 30) in the RSS space and clustered the dataset using the Leiden algorithm (resolution 80).
|
[] |
Single_Cell
|
For both steps, we used the graphical processing unit (GPU)-accelerated RAPIDS implementation that is provided through scanpy .
|
[] |
Single_Cell
|
Source paper: PMC11578878
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 .
|
[
{
"end": 107,
"label": "Tissue",
"start": 77,
"text": "primary developing human brain"
}
] |
Single_Cell
|
We first pretrained a scVI model on the primary atlas with ‘Donor’ as the batch key.
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
To project the organoids atlas to the primary atlas, we used the scArches implementation provided by scvi-tools .
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 103,
"label": "CellType",
"start": 96,
"text": "neurons"
},
{
"end": 91,
"label": "CellType",
"start": 69,
"text": "non-telencephalic NPCs"
},
{
"end": 160,
"label": "CellType",
"start": 135,
"text": "non-telencephalic neurons"
}
] |
Single_Cell
|
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.
|
[
{
"end": 174,
"label": "Tissue",
"start": 170,
"text": "pons"
},
{
"end": 110,
"label": "Tissue",
"start": 102,
"text": "midbrain"
},
{
"end": 90,
"label": "Tissue",
"start": 78,
"text": "hypothalamus"
},
{
"end": 156,
"label": "Tissue",
"start": 147,
"text": "hindbrain"
},
{
"end": 168,
"label": "Tissue",
"start": 158,
"text": "cerebellum"
},
{
"end": 100,
"label": "Tissue",
"start": 92,
"text": "thalamus"
},
{
"end": 55,
"label": "Tissue",
"start": 30,
"text": "non-telencephalic regions"
},
{
"end": 76,
"label": "Tissue",
"start": 64,
"text": "diencephalon"
},
{
"end": 127,
"label": "Tissue",
"start": 112,
"text": "midbrain dorsal"
},
{
"end": 145,
"label": "Tissue",
"start": 129,
"text": "midbrain ventral"
},
{
"end": 186,
"label": "Tissue",
"start": 179,
"text": "medulla"
}
] |
Single_Cell
|
The label-transfer procedure was only applied to the non-telencephalic NPCs and neurons in HNOCA.
|
[
{
"end": 87,
"label": "CellType",
"start": 80,
"text": "neurons"
},
{
"end": 75,
"label": "CellType",
"start": 53,
"text": "non-telencephalic NPCs"
}
] |
Single_Cell
|
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%).
|
[
{
"end": 124,
"label": "CellType",
"start": 117,
"text": "neurons"
},
{
"end": 112,
"label": "CellType",
"start": 90,
"text": "non-telencephalic NPCs"
}
] |
Single_Cell
|
Next, a hierarchical label transfer, which takes into account the structure hierarchy, was applied.
|
[] |
Single_Cell
|
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.
|
[
{
"end": 70,
"label": "Tissue",
"start": 62,
"text": "midbrain"
},
{
"end": 84,
"label": "Tissue",
"start": 75,
"text": "hindbrain"
},
{
"end": 60,
"label": "Tissue",
"start": 48,
"text": "diencephalon"
}
] |
Single_Cell
|
Majority voting was applied to assign each cell to one of the three structures.
|
[] |
Single_Cell
|
Next, a second majority voting was applied to only consider the substructures under the assigned structure (for example, hypothalamus and thalamus for diencephalon).
|
[
{
"end": 133,
"label": "Tissue",
"start": 121,
"text": "hypothalamus"
},
{
"end": 146,
"label": "Tissue",
"start": 138,
"text": "thalamus"
},
{
"end": 163,
"label": "Tissue",
"start": 151,
"text": "diencephalon"
}
] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 177,
"label": "CellType",
"start": 167,
"text": "neuroblast"
},
{
"end": 197,
"label": "CellType",
"start": 182,
"text": "neuron clusters"
}
] |
Single_Cell
|
Here, the most common regions were re-estimated in a hierarchical manner to the finest resolution mentioned above.
|
[] |
Single_Cell
|
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.
|
[
{
"end": 69,
"label": "CellType",
"start": 45,
"text": "non-telencephalic neuron"
}
] |
Single_Cell
|
Source paper: PMC11578878
To match telencephalic NPCs and neurons in HNOCA to developmental stages, we used the recently published human neocortical development atlas as the reference.
|
[
{
"end": 67,
"label": "CellType",
"start": 60,
"text": "neurons"
},
{
"end": 55,
"label": "CellType",
"start": 37,
"text": "telencephalic NPCs"
}
] |
Single_Cell
|
The processed single nucleus RNA-seq data were obtained from its data portal ( https://cell.ucsf.edu/snMultiome/ ).
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
Next, data representing different developmental stages were split.
|
[] |
Single_Cell
|
For each stage, Louvain clustering based on the scPoli latent representation (resolution, 5) was applied.
|
[] |
Single_Cell
|
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 .
|
[] |
Single_Cell
|
Finally, every one of HNOCA telencephalic NPCs and neurons were correlated to each cluster across the identified highly variable genes.
|
[
{
"end": 58,
"label": "CellType",
"start": 51,
"text": "neurons"
},
{
"end": 46,
"label": "CellType",
"start": 28,
"text": "telencephalic NPCs"
}
] |
Single_Cell
|
The stage label of the best-correlated cluster was assigned to the query HNOCA cell.
|
[] |
Single_Cell
|
Source paper: PMC11578878
To extend the analysis to other neuronal cell types, the second-trimester multiple-region human brain atlas was also introduced.
|
[
{
"end": 79,
"label": "CellType",
"start": 60,
"text": "neuronal cell types"
}
] |
Single_Cell
|
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/ ).
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
Louvain clustering was applied to the scPoli latent representation to identify clusters (resolution, 20).
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
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.
|
[] |
Single_Cell
|
Next, every NPC and neuron in HNOCA was correlated to the average expression profiles of those clusters.
|
[
{
"end": 26,
"label": "CellType",
"start": 20,
"text": "neuron"
},
{
"end": 15,
"label": "CellType",
"start": 12,
"text": "NPC"
}
] |
Single_Cell
|
The best-correlated first- and second-trimester clusters, as well as the correlations, were identified.
|
[] |
Single_Cell
|
The differences between the two correlations were used as the metrics to indicate the stage-matching preferences of NPCs and neurons in HNOCA.
|
[
{
"end": 132,
"label": "CellType",
"start": 125,
"text": "neurons"
},
{
"end": 120,
"label": "CellType",
"start": 116,
"text": "NPCs"
}
] |
Single_Cell
|
Source paper: PMC11578878
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.
|
[
{
"end": 237,
"label": "CellType",
"start": 223,
"text": "reference cell"
}
] |
Single_Cell
|
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.
|
[
{
"end": 181,
"label": "CellType",
"start": 169,
"text": "primary cell"
}
] |
Single_Cell
|
Source paper: PMC11578878
To test the cell type compositional changes on admission of certain morphogens from different organoid differentiation protocols, we used the pertpy implementation of the scCODA algorithm .
|
[] |
Single_Cell
|
scCODA is a Bayesian model for detecting compositional changes in scRNA-seq data.
|
[] |
Single_Cell
|
For this, we have extracted the information about the added morphogens from each differentiation protocol and grouped them into 15 broad molecule groups on the basis of their role in neural differentiation (Supplementary Table 1 ).
|
[] |
Single_Cell
|
These molecule groups were used as a covariate in the model.
|
[] |
Single_Cell
|
The region labels transferred from the primary atlas were used as labels in the analysis (cell_type_identifier).
|
[] |
Single_Cell
|
For cell types without regional identity, the cell type labels presented in Fig. 1c were used.
|
[
{
"end": 40,
"label": "CellType",
"start": 4,
"text": "cell types without regional identity"
}
] |
Single_Cell
|
Pluripotent stem cells and neuroepithelium cells were removed from the analysis because they are mainly present in the early organoid stages.
|
[
{
"end": 22,
"label": "CellType",
"start": 0,
"text": "Pluripotent stem cells"
},
{
"end": 48,
"label": "CellType",
"start": 27,
"text": "neuroepithelium cells"
}
] |
Single_Cell
|
We used bio_sample as the sample_identifier.
|
[] |
Single_Cell
|
We ran scCODA sequentially with default parameters, using No-U-turn sampling (run_nuts function) and selecting each cell type once as a reference.
|
[
{
"end": 125,
"label": "CellType",
"start": 116,
"text": "cell type"
}
] |
Single_Cell
|
We used a majority vote-based system to find the cell types that were credibly changing in more than half of the iterations.
|
[
{
"end": 59,
"label": "CellType",
"start": 49,
"text": "cell types"
}
] |
Single_Cell
|
Source paper: PMC11578878
To study the transcriptomic differences between organoid and primary cells, we subset HNOCA using the final level 1 annotation to cells labelled ‘Neuron’.
|
[
{
"end": 102,
"label": "CellType",
"start": 89,
"text": "primary cells"
},
{
"end": 84,
"label": "CellType",
"start": 76,
"text": "organoid"
},
{
"end": 181,
"label": "CellType",
"start": 158,
"text": "cells labelled ‘Neuron’"
}
] |
Single_Cell
|
We furthermore subset the human developing brain atlas to cells that had been assigned a valid label in the neuron_ntt_label annotation column.
|
[
{
"end": 63,
"label": "CellType",
"start": 58,
"text": "cells"
}
] |
Single_Cell
|
We added an extra two datasets of fetal cortical cells from ref.
|
[
{
"end": 54,
"label": "CellType",
"start": 34,
"text": "fetal cortical cells"
}
] |
Single_Cell
|
and ref. .
|
[] |
Single_Cell
|
For the data from ref. ,
|
[] |
Single_Cell
|
we subset the data to cells labelled ‘fetal’ and estimated transcripts per million reads for each gene in each cell using RSEM given the STAR mapping results.
|
[
{
"end": 44,
"label": "CellType",
"start": 22,
"text": "cells labelled ‘fetal’"
}
] |
Single_Cell
|
We then computed a PCA, a kNN graph, UMAP and Leiden clustering (resolution 0.2) using scanpy.
|
[] |
Single_Cell
|
We then selected the cluster with the highest STMN2 and NEUROD6 expression as the cortical neuron cluster and used only those cells.
|
[
{
"end": 105,
"label": "Tissue",
"start": 82,
"text": "cortical neuron cluster"
},
{
"end": 131,
"label": "CellType",
"start": 126,
"text": "cells"
}
] |
Single_Cell
|
For the data from ref.
|
[] |
Single_Cell
|
we subset the datasets to cells annotated as ‘Neuronal’ in Supplementary Table 5 (‘Cortex annotations’) of their publication and computed a PCA, neighbourhood graph and UMAP to visualize the dataset.
|
[
{
"end": 55,
"label": "CellType",
"start": 26,
"text": "cells annotated as ‘Neuronal’"
}
] |
Single_Cell
|
We found that only samples from the individuals CS14_3, CS20, CS22 and CS20 contained detectable expression of STMN2 and NEUROD6 so we subset the dataset further to only cells from those individuals.
|
[
{
"end": 175,
"label": "CellType",
"start": 170,
"text": "cells"
}
] |
Single_Cell
|
Source paper: PMC11578878
To compute DE between HNOCA cells and their primary counterparts, we first aggregated cells of the same regional neural cell type into pseudobulk samples by summing the counts for every sample (annotation columns, ‘batch’ for HNOCA; ‘SampleID’ for the human developing brain atlas; ‘sample’ for ref.
|
[
{
"end": 61,
"label": "CellType",
"start": 56,
"text": "cells"
},
{
"end": 157,
"label": "CellType",
"start": 114,
"text": "cells of the same regional neural cell type"
}
] |
Single_Cell
|
and ‘individual’ for ref. )
|
[] |
Single_Cell
|
using the Python implementation of decoupler (v.1.4.0) while discarding any samples with fewer than ten cells or 1,000 total counts.
|
[
{
"end": 109,
"label": "CellType",
"start": 104,
"text": "cells"
}
] |
Single_Cell
|
We then subsetted the feature space to the intersection of features of all datasets and removed any cells with fewer than 200 genes expressed.
|
[
{
"end": 105,
"label": "CellType",
"start": 100,
"text": "cells"
}
] |
Single_Cell
|
We further removed any genes expressed in less than 1% of neurons in HNOCA and any genes located on the X and Y chromosomes.
|
[
{
"end": 65,
"label": "CellType",
"start": 58,
"text": "neurons"
}
] |
Single_Cell
|
Out of the remaining 11,636 genes, on average, 99% were reported in each of the constituent HNOCA datasets.
|
[] |
Single_Cell
|
For each regional neural cell type, we removed any sample from the pseudobulk data that was associated with an organoid differentiation assay with fewer than two total samples or fewer than 100 total cells.
|
[
{
"end": 34,
"label": "CellType",
"start": 9,
"text": "regional neural cell type"
},
{
"end": 205,
"label": "CellType",
"start": 200,
"text": "cells"
}
] |
Single_Cell
|
We next used edgeR to iteratively compute DE genes between each organoid differentiation protocol and primary cells of the matching regional neural cell types for every regional neural cell type while correcting for organoid age in days, number of cells per pseudobulk sample, median and standard deviation of the number of detected genes per pseudobulk sample.
|
[
{
"end": 115,
"label": "CellType",
"start": 102,
"text": "primary cells"
},
{
"end": 158,
"label": "CellType",
"start": 132,
"text": "regional neural cell types"
},
{
"end": 194,
"label": "CellType",
"start": 169,
"text": "regional neural cell type"
}
] |
Single_Cell
|
We used the data from ref. (
|
[] |
Single_Cell
|
the human developing brain atlas mentioned above), ref.
|
[] |
Single_Cell
|
and ref.
|
[] |
Single_Cell
|
as primary data for the DE comparison in the cell type ‘Dorsal Telencephalic Neuron NT-VGLUT’, whereas for all other cell types we used the human developing brain atlas as the fetal dataset.
|
[
{
"end": 93,
"label": "CellType",
"start": 45,
"text": "cell type ‘Dorsal Telencephalic Neuron NT-VGLUT’"
},
{
"end": 127,
"label": "CellType",
"start": 117,
"text": "cell types"
}
] |
Single_Cell
|
We used the edgeR genewise negative binomial generalized linear model with quasi-likelihood F -tests.
|
[] |
Single_Cell
|
We deemed a gene significantly DE if its false-discovery rate (Benjamini–Hochberg) corrected P value was smaller than 0.05 and it had an absolute log 2 -fold change above 0.5.
|
[] |
Single_Cell
|
We used the GSEApy Python package to carry out functional enrichment analysis in our DE results using the ‘GO_Biological_Process_2021’ gene set.
|
[] |
Single_Cell
|
Source paper: PMC11578878
To evaluate the effect of different primary datasets on the DE results, we computed the DE between Dorsal Telencephalic Neuron NT-VGLUT from the HNOCA subset generated with the protocol from ref.
|
[
{
"end": 163,
"label": "CellType",
"start": 119,
"text": "between Dorsal Telencephalic Neuron NT-VGLUT"
}
] |
Single_Cell
|
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