看到一篇文章做了这两个数据,正好可以比较一下,文章是 Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis
研究是 【Idiopathic Pulmonary Fibrosis 特发性肺纤维化】
数据下载
数据存放在:GEO GSE86618 and GSE94555
scRNA-seq 采用的是 Fluidigm C1 Single-Cell Auto Prep System , 测序详情是:
Single-cell libraries are multiplexed and sequenced across 4 lanes of a NextSeq 500 platform (Illumina) using 75-bp single-end sequencing. On average, about 4–5 million reads were generated from each single-cell library.
放在:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86618 共540个细胞,数据量不小。
其中包括540 single cells from control (n = 3) and IPF patients (n = 6) reveals 4 major cell types (C1–C4), termed as
normal AT2 (C1, green)
indeterminate (C2, yellow)
basal (C3, red)
club/goblet (C4, blue) cells.
单细胞转录组的优点就是可以分群,但是本教程需要探索单细胞转录组的平均值是否与其bulk测序有相关性。
bulk转录组测序数据在:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94555 EPCAM+ (CD326+) and HTII-280+ epithelial cells from control and IPF donors were isolated from peripheral lung tissue by FACS and subjected to RNA sequencing (RNA-seq).
ID处理
GSM2478109IPF_1
GSM2478110IPF_2
GSM2478111IPF_3
GSM2478112CON_1
GSM2478113CON_2
GSM2478114CON_3
提供表达量矩阵的下载: GSE94555_IPF_Epithelial_Type2_RNA-seq_Reads_and_FPKM.xlsx 当然,也是可以下载原始数据走一波转录组分析流程得到表达矩阵进行差异分析的。
如果你不会上面这样的简单分析,那么你可能是需要去B站看我的视频,搜索生信技能树即可。
进行比较
首先需要使用R下载两个表达矩阵,然后需要对应单细胞来源于的病人与bulk的病人,这样就可以计算相关性啦!!!
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