文章题目：Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia
发表杂志：Proc Natl Acad Sci U S A
发表时间：2022年12月6日
通讯作者：Sai-Juan Chen
原文链接：https://pubmed.ncbi.nlm.nih.gov/36442087/
软件链接：ConsensusClusterPlus、ConsensusClusterPlus github
本文的作者利用高通量测序数据识别急性髓系白血病(AML)的生物学相关的分子亚型，并基于组学的对相关靶向药物进行筛选。
mathod and software
- Raw RNA-Seq reads counts were extracted by both genome alignment-based Featurecounts v2.0.1 (40) and Htseq v0.11.3 (41) and alignment-free methods salmon v1.2.1 (42) and Kallisto v0.46.2 (43)
- Normalization of the counts matrix was simultaneously computed based on the R DESeq2 (v1.28.0) (44) transformation and the Transcripts Per Kilobase Million (TPM) value, which were used as the gene expression matrix for downstream analysis
- ComBat function in the R sva package (v3.40.0) (45) was used to adjust the batch effect
- Unsupervised clustering of top variance genes was conducted in R using the ComplexHeatmap (46) and a modified consensus clustering workflow
- Autogluon (v0.2.0) (https://github.com/awslabs/autogluon) in Python was applied in the training and assessment of predictive models of GEP-defined subgroups