SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies.
Genome Biol
; 22(1): 184, 2021 06 21.
Article
en En
| MEDLINE
| ID: mdl-34154649
ABSTRACT
Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Ováricas
/
Algoritmos
/
Transcriptoma
/
Modelos de Interacción Espacial
Tipo de estudio:
Diagnostic_studies
Límite:
Animals
/
Female
/
Humans
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos