方法一： https://doi.org/10.1186/s12967-020-02366-0 doi:10.1002/1878-0261.12747
To deduce the hypoxia status, an algorithm of t-distributed
Stochastic Neighbor Embedding (t-SNE) was
applied [19]. t-SNE, a nonparametric, unsupervised
method, can divide or condense patients into several distinct
clusters, based on given signatures or hallmarks.
The hallmark gene sets of hypoxia including 200 genes,
were downloaded from the Molecular Signatures Database
(MSigDB version 6.0).
MCP-counter
scores regarding immune-related activity and fibroblasts
were evaluated using the ‘MCPcounter’ package
in R [40]. ssGSEA was performed to calculate the
enrichment score of specific immune signatures in samples
using the ‘GSVA’ package in R according to a
previous study [41]. doi:10.1002/1878-0261.12747
ESTIMATE method 免疫预测 Immune and stromal scores
were further estimated to quantify the immune and
stromal components by the ESTIMATE algorithm
using the ‘estimate’ package in R
方法二: https://doi.org/10.3389/fonc.2020.579868
According to the studies published, we selected 13 hypoxia
related gene expression signature for our analysis: ADM,
TUBB6, MRPS17, CDKN3, TPI1, ALDOA, MIF, PGAM1,
LDHA, P4HA1, SLC2A1, NDRG1, and VEGFA, which have
been shown to perform the hypoxia status (12, 24).
two different hypoxia status groups (cluster1 and
cluster2) among 1104 TCGA BRCA tumor samples were selected
by using ConsensusClusterPlus package with 50 iterations,
resample rate of 0.8.
基因表达量预测免疫细胞量：ImmuCellAI database
