SmartSeq2 scRNASeq QC Metrics

Sequencing QC metrics and its visualization can not only provide overall view of quality for experiment but also play important role in quality troubleshooting, library construction improvement.

Table of Contents

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• scRNASeq QC Metrics

  • Alignment Metrics
  • RNA Metrics
  • Insertion Metrics
  • Duplciation Metrics

• Example dataset

  • Sample Details
  • QC Metrics and Visualization

scRNASeq QC Metrics

Tables of metrics can provide an overiew of alignment statistics,rna sequencing quality and more.

Alignment Metrics

Alignment metrics can be used to provide overall idea of the quality of alignment for your libraries. One of important metrics is PCT_PF_ALIGNED which indicates the percentage of reads mapped to reference genome. Another important metrics is PF_MISMATCH_RATE, wchich can provide overall alignment quality.

RNA Metrics

RNA metrics provide important summary based on gene annotation. PCT_USABLEBASES indicates the percentage of bases mapped to transcriptome(mRNA+UTR regions). This meitrics provide overall view of quality of RNA sequencing. High values in PCT_INTRONIC_BASES, PCT_INERGENIC_BASES and PCT_RIBOSOMAL_BASES indicate low quality or degraded RNA. High in MEDIAN_3PRIME_BIAS also indicates high chance of degraded RNA.

Insertion Metrics

These metrics provide based information on insert sizes for paired-end library. This metrics can be used to ensure that paire-end libraries are constructed as expected.

Duplication Metrics

These metrics provid level of duplication(post alignment). This is cordinates based method, not raw fastq data based method.

Example dataset

Sample Details

In this task, we applied a scRNA-Seq pipeline on a published dataset GSE47872. We selected single cell samples include primary Glioblastoma and Gliomasphere Cell Line cells. The sample counts are listed below:

	25bp	100bp
Glioblastoma	581	96
Gliomasphere Cell Line	195	0

QC Metrics and Visualization

We collected all matrics together and generated one table. We visualized several important metrics shown as below. First we examed metrics between two different celltype

TOTAL_READS metrics' density plot shown in figure. there are fairely even number of reads generated among two celltypes.

PCT_PF_READS_ALIGNED density plot. There is no significant difference between celltype but there is unusual peak at lower end, which indicate low alignment rate.

PF_MISMATCH_RATE density plot. primary cancer cell show an unusual peak at high mismatching rate.

PCT_USABLE_BASES density plot.

PCT_RIBOSOMAL_BASES

MEDIAN_CV_COVERAGE

MEDIAN_3PRIME_BIAS

MEDIAN_INSERT_SIZE

MEDIAN_ABSOLUTE_DEVIATION

PERCENT_DUPLICATION