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graph TB
    A[子宫内膜及非典型子宫内膜增生AEH及子宫内膜样子宫内膜癌ECC] --> B[细胞分群]
    B --> C[上皮细胞比例在AEH中增加,在EEC中进一步扩大,CNV变化大]
    B --> D[间质成纤维细胞比例下降]
    C --> F[RNA velocity分析,细胞相似性分析,MET marer基因分析等表明ECC不来源CAF]
    D --> F
    F --> G[EEC的上皮聚类,发现AEH特有非纤毛上皮亚群,并在ECC中存在,推测来源于正常的非纤毛上皮]    
    G --> H[EEC上皮细胞独有亚群为致癌亚群,RNA velocity分析非纤毛上皮腺细胞有可能是致癌亚群中存在的来源]
    H --> I[致癌亚群特征基因,发现LCN2和SAA1/2可能是子宫内膜早期肿瘤发生的一个特征]
    I --> J[类器官实验证明在正常子宫内膜和EEC中成纤维细胞是不可缺少的]
    J --> K[类器官实验证明在正常子宫内膜和EEC中成纤维细胞是不可缺少的]
    K --> L[巨噬细胞和淋巴细胞亚群分析表明免疫环境的失调可导致子宫内膜肿瘤的发生]

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library(recount3)

# 同 https://jhubiostatistics.shinyapps.io/recount3-study-explorer/
# 可以看到project_home和project，包括TCGA,GTEX和SRA
human_projects <- available_projects()

proj_info <- subset(
  human_projects,
  project == "OV" & project_type == "data_sources"
)

rse_gene_tcga_ov <- create_rse(proj_info)

#counts data
assays(rse_gene_tcga_ov)$counts <- transform_counts(rse_gene_tcga_ov)
# ## Compute TPMs
assays(rse_gene_tcga_ov)$TPM <- recount::getTPM(rse_gene_tcga_ov, length_var = "score")
# ## Check TPM. Should all be equal to 1
colSums(assay(rse_gene_tcga_ov, "TPM")) / 1e6 


# View sample annotation
View(data.frame(colData(rse_gene_tcga_ov)))

# View gene annotation
View(data.frame(exp$tcga.ov.expr@rowRanges))

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rse_gene_tcga_ov = recount3::create_rse_manual(
    project = "OV",
    project_home = "data_sources/tcga",
    organism = "human",
    annotation = "gencode_v26",
    type = "gene"
)

#counts data
assays(rse_gene_tcga_ov)$counts <- transform_counts(rse_gene_tcga_ov)
# ## Compute TPMs
assays(rse_gene_tcga_ov)$TPM <- recount::getTPM(rse_gene_tcga_ov, length_var = "score")
# ## Check TPM. Should all be equal to 1
colSums(assay(rse_gene_tcga_ov, "TPM")) / 1e6

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kingfisher get -r ERR1739691 -m ena-ascp
# 可以指定列表--run-identifiers-list
# 指定project编号SRP，--bioprojects
# 指定下载方法，--download-methods，可以指定多种，程序会一个方法一个方法的试，包括ena-ascp，ena-ftp，prefetch，aws-http，aws-cp，gcp-cp
# 指定线程数目，--download-threads
# 指定ascp需要的key路径，--ascp-ssh-key

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zcat input.fastq.gz | bowtie --seedlen 10 -p 48 -v2 -m20 --best -S --strata --chunkmbs 8000 $BOWTIE_IND - output.sam

-M 1           like -m, but reports 1 random hit (MAPQ=0); requires --best
- 为zcat的输入，bowtie不识别gz，所以用zcat管道流入bowtie
-p 48 线程
-v report end-to-end hits w/ <=v mismatches; ignore qualitie
-m20 丢弃超过20次multi-map的reads
--best --strata multi-map的reads只报告最好的比对
--chunkmbs 8000 best-first搜索的最大内存（M）
-S 输出SAM格式
BOWTIE_IND bowtie的索引

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samtools sort --threads 10 out.sam -o out_sorted.bam --output-fmt BAM && samtools index out_sorted.bam

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axel -n 10 https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.annotation.gtf.gz

zcat gencode.v44.annotation.gtf.gz | grep "snoRNA" > snoRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "Mt_rRNA" > Mt_rRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "Mt_tRNA" > Mt_tRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "rRNA" > rRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "snRNA" > snRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "sRNA" > sRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "scaRNA" > scaRNA.gtf
zcat gencode.v44.annotation.gtf.gz | grep "miRNA" > miRNA.gtf

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# 也来自gencode，tRNA genes predicted by ENSEMBL on the reference chromosomes using tRNAscan-SE
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.tRNAs.gtf.gz
zcat gencode.v44.tRNAs.gtf.gz > tRNA.gtf