# 前言
在利用 cellranger 比对单细胞 reads 时，可以发现有 STAR 的进程夹杂在里面，那么 STAR 可以用来比对单细胞数据吗？在 STAR 的 2.7 版本中 (2.7.6a) 出现了 STARsolo，可以进行单细胞数据的比对，由此可见 STAR 的强大
# Cellranger 输出结果
在使用 STAR 之前，先看一下 cellranger 的输出结果
. | |
├── analysis | |
│ ├── clustering | |
│ ├── diffexp | |
│ ├── pca | |
│ ├── tsne | |
│ └── umap | |
├── cloupe.cloupe | |
├── filtered_feature_bc_matrix | |
│ ├── barcodes.tsv.gz | |
│ ├── features.tsv.gz | |
│ └── matrix.mtx.gz | |
├── filtered_feature_bc_matrix.h5 | |
├── metrics_summary.csv | |
├── molecule_info.h5 | |
├── possorted_genome_bam.bam | |
├── possorted_genome_bam.bam.bai | |
├── raw_feature_bc_matrix | |
│ ├── barcodes.tsv.gz | |
│ ├── features.tsv.gz | |
│ └── matrix.mtx.gz | |
├── raw_feature_bc_matrix.h5 | |
└── web_summary.html |
为方便查看，cellranger 提供了一个网页端的结果，我们主要观察细胞和基因数目的评估即可，后续的聚类工作由 seurat 完成
在结果目录，可以看到如下两个目录
raw_feature_bc_matrix
filtered_gene_bc_matrices
raw 目录下是所有的 barcode 信息，包含了细胞相关的 barcoed 和背景 barcode, 而 filter 目录下只包含细胞相关的 barcode 信息，内容如下
│ ├── barcodes.tsv.gz | |
│ ├── features.tsv.gz | |
│ └── matrix.mtx.gz |
后缀为 mtx 的文件记录的就是基因的表达量信息，可以导入 R 或者 python 中查看，barcodes 相当于一个细胞，features 代表不同的基因，barcodes 文件在 STARsolo 中会用到，这就是为什么我要先说明一下 cellranger 的输出结果
# 利用 STAR 分析 10X 数据
STARsolo 被设计为替代 10X CellRanger 基因定量比对软件。而且 STARsolo 的速度是 cellranger 的十倍（具体怎么样我也不知道）
建立索引：
STAR --runMode genomeGenerate --genomeDir ghg38/ --genomeFastaFiles Homo_sapiens.GRCh38.dna.primary_assembly.fa --sjdbGTFfile Homo_sapiens.GRCh38.93.filtered.gtf |
其实利用 cellranger 构建的索引原则上也能用，在 GRCh38/star/ 下，但是由于 STAR 版本问题可能会导致无法识别，因为 cellranger 用的 STAR 构建的索引和我们自己用的 STAR 版本是不一致的。
STARsolo 与普通的转录组比对区别在于你需要在比对时加上 whitelist，whitelist 文件格式在 10X 官网有写，我们可以利用 cellranger 的 barcodes.tsv.gz 文件获得
zcat barcodes.tsv.gz>whitelist | |
sed -i "s\-1\\g" whitelist |
需要注意 ReadFilesIn 先读入测序数据，再读入 barcode+UMI 文件，即先读入 R2 再读入 R1
STAR --genomeDir ghg38/ --readFilesCommand zcat --readFilesIn SRR7722939/SRR7722939_S1_L001_R2_001.fastq.gz SRR7722939/SRR7722939_S1_L001_R1_001.fastq.gz --soloType CB_UMI_Simple --soloCBwhitelist whitelist --runThreadN 8 |
结果默认保存在 Solo.out 文件中，8 线程只用了 10min 左右，确实要快一点
├── Barcodes.stats | |
└── Gene | |
├── Features.stats | |
├── filtered | |
│ ├── barcodes.tsv | |
│ ├── features.tsv | |
│ └── matrix.mtx | |
├── raw | |
│ ├── barcodes.tsv | |
│ ├── features.tsv | |
│ └── matrix.mtx | |
├── Summary.csv | |
└── UMIperCellSorted.txt |
看一下 Summary 里是啥
Number of Reads,23095815 | |
Reads With Valid Barcodes,0.979732 | |
Sequencing Saturation,0.529367 | |
Q30 Bases in CB+UMI,0.991351 | |
Q30 Bases in RNA read,0.842989 | |
Reads Mapped to Genome: Unique+Multiple,0.959769 | |
Reads Mapped to Genome: Unique,0.874368 | |
Reads Mapped to Transcriptome: Unique+Multipe Genes,0.63797 | |
Reads Mapped to Transcriptome: Unique Genes,0.613655 | |
Estimated Number of Cells,2048 | |
Reads in Cells Mapped to Unique Genes,12815372 | |
Fraction of Reads in Cells,0.904219 | |
Mean Reads per Cell,6257 | |
Median Reads per Cell,5544 | |
UMIs in Cells,5978718 | |
Mean UMI per Cell,2919 | |
Median UMI per Cell,2577 | |
Mean Genes per Cell,915 | |
Median Genes per Cell,873 | |
Total Genes Detected,16265 |
可以看到比起 cellranger，STAR 捕获到的细胞数少一点，而且每个细胞的 reads 要低一点，其他差不多，后续将使用 Seurat 包对两组数据进行比较
# STARsolo 其他命令
--soloType
default: None
string(s): type of single-cell RNA-seqCB_UMI_Simple
(a.k.a. Droplet) one UMI and one Cell Barcode of xed length in
read2, e.g. Drop-seq and 10X Chromium.CB_UMI_Complex
one UMI of xed length, but multiple Cell Barcodes of varying length,
as well as adapters sequences are allowed in read2 only, e.g. inDrop.CB_samTagOut
output Cell Barcode as CR and/or CB SAm tag. No UMI counting.
{readFilesIn cDNA read1 [cDNA read2 if paired-end]
CellBarcode read . Requires {outSAMtype BAM Unsorted [and/or
SortedByCoordinate]SmartSeq
Smart-seq: each cell in a separate FASTQ (paired- or single-end),
barcodes are corresponding read-groups, no UMI sequences,
alignments deduplicated according to alignment start and end (after
extending soft-clipped bases)
--soloCBwhitelist
default: -
string(s): le(s) with whitelist(s) of cell barcodes. Only {soloType
CB UMI Complex allows more than one whitelist le.
None
no whitelist: all cell barcodes are allowed
--soloCBstart
default: 1
int>0: cell barcode start base
--soloCBlen
default: 16
int>0: cell barcode length
--soloUMIstart
default: 17
int>0: UMI start base
--soloUMIlen
default: 10
int>0: UMI length
--soloBarcodeReadLength
default: 1
int: length of the barcode read
1
equal to sum of soloCBlen+soloUMIlen
0
not de ned, do not check
--soloCBposition
default: -
strings(s) position of Cell Barcode(s) on the barcode read.
Presently only works with {soloType CB UMI Complex, and barcodes are
assumed to be on Read2.
Format for each barcode: startAnchor startPosition endAnchor endPosition
start(end)Anchor de nes the Anchor Base for the CB: 0: read start; 1: read
end; 2: adapter start; 3: adapter end
start(end)Position is the 0-based position with of the CB start(end) with
respect to the Anchor Base
String for di erent barcodes are separated by space.
Example: inDrop (Zilionis et al, Nat. Protocols, 2017):
{soloCBposition 0 0 2 -1 3 1 3 8
--soloUMIposition
default: -
string position of the UMI on the barcode read, same as soloCBposition
--soloAdapterSequence
default: -
string: adapter sequence to anchor barcodes.
--soloAdapterMismatchesNmax
default: 1
int>0: maximum number of mismatches allowed in adapter sequence.
--soloCBmatchWLtype
default: 1MM multi
string: matching the Cell Barcodes to the WhiteList
Exact
only exact matches allowed1MM
only one match in whitelist with 1 mismatched base allowed. Allowed
CBs have to have at least one read with exact match.1MM_multi
multiple matches in whitelist with 1 mismatched base allowed,
posterior probability calculation is used choose one of the matches.
Allowed CBs have to have at least one read with exact match. Similar to
CellRanger 2.2.01MM_multi_pseudocounts
same as 1MM Multi, but pseudocounts of 1 are added to all whitelist
barcodes.
Similar to CellRanger 3.x.x
--soloStrand
default: Forward
string: strandedness of the solo libraries:
Unstranded
no strand information
Forward
read strand same as the original RNA molecule
Reverse
read strand opposite to the original RNA molecule
--soloFeatures
default: Gene
string(s): genomic features for which the UMI counts per Cell Barcode are
collected
Gene
genes: reads match the gene transcriptSJ
splice junctions: reported in SJ.out.tabGeneFull
full genes: count all reads overlapping genes' exons and introns
--soloUMIdedup
default: 1MM_All
string(s): type of UMI deduplication (collapsing) algorithm1MM_All
all UMIs with 1 mismatch distance to each other are collapsed (i.e.
counted once)1MM_Directional
follows the "directional" method from the UMI-tools by Smith, Heger
and Sudbery (Genome Research 2017).Exact
only exactly matching UMIs are collapsedNoDedup
no deduplication of UMIs, count all reads. Allowed for --soloType
SmartSeq
--soloUMIfiltering
default: -
string(s) type of UMI ltering-
basic ltering: remove UMIs with N and homopolymers (similar to
CellRanger 2.2.0)MultiGeneUMI
remove lower-count UMIs that map to more than one gene
(introduced in CellRanger 3.x.x)
--soloOutFileNames
default: Solo.out/ features.tsv barcodes.tsv matrix.mtx
string(s) le names for STARsolo output:
le name pre x gene names barcode sequences cell feature count matrix
--soloCellFilter
default: CellRanger2.2 3000 0.99 10
string(s): cell ltering type and parametersCellRanger2.2
simple ltering of CellRanger 2.2, followed by three numbers: number
of expected cells, robust maximum percentile for UMI count,
maximum to minimum ratio for UMI countTopCells
only report top cells by UMI count, followed by the exact number of
cellsNone
do not output ltered cells
--soloOutFormatFeaturesGeneField3
default: "Gene Expression"
string(s): eld 3 in the Gene features.tsv le. If "-", then no 3rd ld is output.
