Single-cell sequencing analysis.

Single-cell sequencing analysis.

<p>(A) Harmony plot colored by one RA and three normal samples. (B) UMAP plot colored by various cell clusters. (C) UMAP plot colored by cells after annotation. (D) Heat map shows the expression of hallmark genes in different cell clusters from RA PBMCs. The scaled average expression levels of...

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Bibliographic Details
Main Author: Kaiyi Zhou (2553352) (author)
Other Authors: Siyu Luo (11856014) (author), Qinxiao Wang (20141557) (author), Sheng Fang (709608) (author)
Published: 2024
Subjects:
Biophysics
Cell Biology
Genetics
Molecular Biology
Immunology
Developmental Biology
Cancer
Hematology
Biological Sciences not elsewhere classified
receiver operating characteristic
nf -&# 954
lymphocyte antigen 96
least absolute shrinkage
inflammatory musculoskeletal disorders
constructed using data
b signaling pathways
gsea analysis indicated
gene expression omnibus
gap junction function
cell sequencing data
enhanced vegf signaling
seven overlapping degs
decision curve analysis
immune cell function
two marker genes
tf network suggested
potential diagnostic markers
study identified rpl22l1
vegf signaling
results indicated
potential association
marker genes
high expression
gap junctions
diagnostic capacity
cell infiltration
cell development
cell communication
immune landscape
immune infiltrates
immune cells
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utilized datasets
transcription factor
thereby exploring
shared biomarkers
selection operator
rheumatoid arthritis
rfe ).
psoriatic arthritis
nk cells
mechanistic similarities
machine learning
like receptors
like 1
key biomarkers
investigations demonstrated
findings based
experimental approaches
dca ).
comprehensive understanding
common types
cellular subpopulations
cells examined
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