非小细胞肺癌(GSE148071)

非小细胞肺癌(GSE148071)



数据下载:

wget -c "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE148071&format=file" -O GSE148071_RAW.tar
tar xvf GSE148071_RAW.tar


数据map.txt 准备:
GSM4453576	P1	71	female	LUSC	advanced
GSM4453577	P2	62	male	LUAD	advanced
GSM4453578	P3	67	male	LUSC	advanced
GSM4453579	P4	58	male	LUSC	advanced
GSM4453580	P5	62	male	LUAD	advanced
GSM4453581	P6	59	male	LUSC	advanced
GSM4453582	P7	71	male	LUSC	advanced
GSM4453583	P8	35	male	LUAD	advanced
GSM4453584	P9	40	male	LUAD	advanced
GSM4453585	P10	65	male	LUSC	advanced
GSM4453586	P11	58	female	NSCLC	advanced
GSM4453587	P12	62	female	LUAD	advanced
GSM4453588	P13	50	female	LUAD	advanced
GSM4453589	P14	60	male	LUSC	advanced
GSM4453590	P15	64	male	LUSC	advanced
GSM4453591	P16	49	male	LUAD	advanced
GSM4453592	P17	71	male	LUSC	advanced
GSM4453593	P18	66	male	LUSC	advanced
GSM4453594	P19	55	male	LUSC	advanced
GSM4453595	P20	59	male	LUAD	advanced
GSM4453596	P21	65	male	LUAD	advanced
GSM4453597	P22	67	male	LUSC	advanced
GSM4453598	P23	77	male	LUSC	advanced
GSM4453599	P24	52	male	LUAD	advanced
GSM4453600	P25	75	male	LUSC	advanced
GSM4453601	P26	53	female	LUSC	advanced
GSM4453602	P27	68	male	LUSC	advanced
GSM4453603	P28	64	female	LUAD	advanced
GSM4453604	P29	48	male	LUAD	advanced
GSM4453605	P30	73	male	LUSC	advanced
GSM4453606	P31	53	male	LUSC	advanced
GSM4453607	P32	71	female	LUAD	advanced
GSM4453608	P33	42	female	LUAD	advanced
GSM4453609	P34	72	male	LUAD	advanced
GSM4453610	P35	63	male	LUAD	advanced
GSM4453611	P36	42	male	LUSC	advanced
GSM4453612	P37	73	female	LUSC	advanced
GSM4453613	P38	55	male	LUAD	advanced
GSM4453614	P39	49	male	LUAD	advanced
GSM4453615	P40	62	male	LUSC	advanced
GSM4453616	P41	60	male	LUSC	advanced
GSM4453617	P42	64	male	NSCLC	advanced

数据读入分析:



cat map.txt |while read s t;do
  Rscript $scripts/seurat_sc_qc.r  --count ${s}_${t}_exp.txt.gz  --project GSE148071  \
       --metadata.col.name project Capture.Method Sample Tissue\
       --metadata.value $project 10X3 $t $s\
        --nUMI.min 500 \
         --nUMI.max 40000 \
      --nGene.min 250 \
      --mito.gene.pattern "^MT.*-" \
      --percent_mito 20 \
      --log10GenesPerUMI 0.7 \
      -o $project/ -p $t
   
done
#合并数据
Rscript $scripts/merge_seurat_obj.r -i `ls *.afterQC.rds` \
    -o $project/01.qc -p lung


单细胞课程推荐:

单细胞转录组分析课程推荐:https://bdtcd.xetslk.com/s/4i88K6


attachments-2024-07-E8KTP8mv669e3288e8034.png




  • 发表于 2024-08-16 15:10
  • 阅读 ( 460 )
  • 分类:转录组

0 条评论

请先 登录 后评论
omicsgene
omicsgene

生物信息

698 篇文章

作家榜 »

  1. omicsgene 698 文章
  2. 安生水 347 文章
  3. Daitoue 167 文章
  4. 生物女学霸 120 文章
  5. xun 82 文章
  6. 红橙子 78 文章
  7. rzx 74 文章
  8. CORNERSTONE 72 文章