Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China.
Bioinformatics. 2022 Sep 2;38(17):4135-4144. doi: 10.1093/bioinformatics/btac457.
Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions.
We develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate P-values from multiple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal biological insights at the cell-type level.
The R package of CTSV is available at https://bioconductor.org/packages/devel/bioc/html/CTSV.html.
Supplementary data are available at Bioinformatics online.
空间转录组技术可以在保留空间信息的同时对基因表达进行分析，从而为探索基因表达与空间位置之间的关系提供前所未有的机会。这种空间关系可能因细胞类型而异，但缺乏同时对细胞类型特有的空间可变（SV）基因进行建模的统计方法，以同时对过剩零值和细胞类型比例进行建模。
我们开发了一种用于检测细胞类型特有的 SV 基因的统计方法 CTSV。CTSV 直接对空间原始计数数据进行建模，并使用零膨胀负二项分布考虑零膨胀和过离散。然后，它将细胞类型比例和空间效应函数纳入零膨胀负二项回归框架中。使用 pscl R 包来拟合模型。为了稳健性，采用柯西组合规则将来自多个空间效应函数选择的 P 值进行整合。模拟研究表明，CTSV 不仅在聚合水平上优于竞争方法，而且在细胞类型水平上具有更高的功效。通过分析胰腺导管腺癌的空间转录组数据，CTSV 鉴定的 SV 基因揭示了细胞类型水平上的生物学见解。
CTSV 的 R 包可在 https://bioconductor.org/packages/devel/bioc/html/CTSV.html 上获得。
补充数据可在 Bioinformatics 在线获得。
