scRNA-seq

—————- 2023.03.06 ———————-
俺来填坑了！！！

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scRNA-seq analysis 大体分两个模块：

测序数据前期处理
转为矩阵
数据挖掘

前期处理，包括 barcode 的拆解，比对。
利用比对结果，进行定量，去除 doublet 和 empty droplet，拿到矩阵。定量有：表达量定量；虽然 scRNA-seq 覆盖度较低，但已经有了对剪接的研究，取得了一定的进展。

QC steps must also be performed at the level of transcripts. 在基因水平的计数，过滤掉不表达的基因后，基因数量大幅减少，难以区分细胞的异质性。

normalization:
CPM, TPM

data correlation (batch correction, noise correction)

data processing stages:

1. raw data
2. normalized data
3. corrected data
4. feature-selected data
5. dimensionality-reduced data

stages above grouped into three layers:

1. measured data
2. corrected data
3. reduced data

无论是基因表达量还是剪接水平定量，最后都是一个矩阵。对矩阵进行降维、聚类等数据挖掘，完成细胞分群，拟时序分许等。

tools:

CellRanger

• also performs cell QC, which has three covariates for dying cells, doublet and empty droplet:
  • number of counts per barcode (count depth)
  • number of genes per barcode
  • the fraction of counts from mitochondrial genes per barcode

Seurat vignettes

10X genomics: what is fixed RNA profiling

• single-sample solutions
• multiplexing solutions

reference:

1. Alvarez (2022) BMC Gen Med Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma‑associated cell‑type affecting survival