Concerted changes in the pediatric single-cell intestinal ecosystem before and after anti-TNF blockade

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Cell Metadata

Mono

Ontology Term: monocyteCL:0000576
SynonymsTerms that have the same, or nearly the same, meaning as the selected cell type.: unknown
CategoryClass of which the cell type is a subclass.: myeloid cellCL:0000763
Marker Gene EvidenceGene markers used by researcher to assign this cell annotation.: FTL, LYZ, CD14, FCN1, S100A8, S100A9
Canonical Marker Genes: CD14, CD16 (FCGR3A/B), CD64 (FCGR1A), CCR2, CSF1R, S100A8, S100A9
Rationale: This one has high AIF1, CD14, LYZ, FTL, all of which can be in macrophages. or monocytes. It is often difficult to discern macrophage from monocyte in gut data, but this one has higher CD14 as well as FCN1 and S100A8/9, so we went with monocyte

Set of Labels Description: In this paper we used an automated iterative subclustering algorithm to find hundreds of cell state clusters, after which we merged unsupported clusters and annotated each resulting cluster with a better-known cell subtype. The high-res cell state clusters are annotated as subtype.gene1.gene2, so in some cases we didn't directly name some commonly named subtypes, such as pericytes. When we run DE on the resulting subtypes, we find many canonical and scRNA-suggested marker genes.
Annotation Method: both
Algorithm Name: ARBOL
Algorithm Version: v1.0
Algorithm URL: https://github.com/jo-m-lab/ARBOL/

Ontology Term: monocyteCL:0000576
SynonymsTerms that have the same, or nearly the same, meaning as the selected cell type.: unknown
CategoryClass of which the cell type is a subclass.: myeloid cellCL:0000763
Marker Gene EvidenceGene markers used by researcher to assign this cell annotation.: FTL, LYZ, CD14, FCN1, S100A8, S100A9
Canonical Marker Genes: CD14, CD16 (FCGR3A/B), CD64 (FCGR1A), CCR2, CSF1R, S100A8, S100A9
Rationale: This one has high AIF1, CD14, LYZ, FTL, all of which can be in macrophages. or monocytes. It is often difficult to discern macrophage from monocyte in gut data, but this one has higher CD14 as well as FCN1 and S100A8/9, so we went with monocyte

Set of Labels Description: In this paper we used an automated iterative subclustering algorithm to find hundreds of cell state clusters, after which we merged unsupported clusters and annotated each resulting cluster with a better-known cell subtype. The high-res cell state clusters are annotated as subtype.gene1.gene2, so in some cases we didn't directly name some commonly named subtypes, such as pericytes. When we run DE on the resulting subtypes, we find many canonical and scRNA-suggested marker genes.
Annotation Method: both
Algorithm Name: ARBOL
Algorithm Version: v1.0
Algorithm URL: https://github.com/jo-m-lab/ARBOL/