Unsupervised Single-Cell Clustering with Asymmetric Within-Sample Transformation and Per-Cluster Supervised Features Selection.
Methods Mol Biol
; 2812: 155-168, 2024.
Article
en En
| MEDLINE
| ID: mdl-39068361
ABSTRACT
This chapter shows applying the Asymmetric Within-Sample Transformation to single-cell RNA-Seq data matched with a previous dropout imputation. The asymmetric transformation is a special winsorization that flattens low-expressed intensities and preserves highly expressed gene levels. Before a standard hierarchical clustering algorithm, an intermediate step removes noninformative genes according to a threshold applied to a per-gene entropy estimate. Following the clustering, a time-intensive algorithm is shown to uncover the molecular features associated with each cluster. This step implements a resampling algorithm to generate a random baseline to measure up/downregulated significant genes. To this aim, we adopt a GLM model as implemented in DESeq2 package. We render the results in graphical mode. While the tools are standard heat maps, we introduce some data scaling to clarify the results' reliability.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Análisis de la Célula Individual
Límite:
Humans
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article
País de afiliación:
Italia
Pais de publicación:
Estados Unidos