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.
Tables of metrics can provide an overview of alignment statistics,rna sequencing quality and more.
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, which can provide overall alignment quality. Example shown below.
RNA metrics provide important summary based on gene annotation. PCT_USABLEBASES indicates the percentage of bases mapped to transcriptome(mRNA+UTR regions). This metrics 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. Example shown below.
These metrics provide based information on insert sizes for paired-end library. This metrics can be used to ensure that pair-end libraries are constructed as expected. Example shown below.
These metrics provide level of duplication(post alignment). This is coordinates based method, not raw fastq data based method. Example shown below.
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 |
We collected all metrics together and generated one table. We visualized several important metrics shown as below.
First,We examined metrics between two different celltype with the same read length 25bp.
TOTAL_READS metrics' density plot shown in figure. Cancer primary cells yields slightly less total number of reads
PCT_PF_READS_ALIGNED density plot. Overall, both celltypes yield ~75% alignment rate. There is a unusual peak at 25% alignment rate.
PF_MISMATCH_RATE density plot. Primary cells PF_MISMATCH_RATE have abnormal two peaks.
PCT_USABLE_BASES density plot.
PCT_RIBOSOMAL_BASES density plot. Both celltype yield good percentage of ribosomal bases.
MEDIAN_CV_COVERAGE density plot. The median coefficient of variation (CV) or stdev/mean for coverage values of the 1000 most highly expressed transcripts. Low values is ideal
MEDIAN_3PRIME_BIAS and MEDIAN_5PRIME_BIAS density plots. Both celltypes show low 5' and 3' end bias but there is a long tail extend to high bias region which indicate there are degradation in some of cells.
MEDIAN_INSERT_SIZE and MEDIAN_ABSOLUTE_DEVIATION density plots. both celltypes fall into expected region, 200~400bp
PERCENT_DUPLICATION density plot. Both celltypes show ~10% of duplication rate and a subset of cells show high duplication rate ~75%.
Then we examined 96 paired samples, which have two different read length, 25bp and 100bp. Samples in pair were sequenced from the same library.
For alignment, we examined PCT_PF_READS_ALIGNED, PF_MISMATCH_RATE and MEDIAN_CV_COVERAGE between paired L100bp and L25bp samples. L100bp and L25bp have similar PCT_PF_READS_ALIGNED. L100bp samples have lower value of CV (which is ideal). L100bp samples have much higher PF_MISMATCH_RATE compared to L25bp. The cause for this difference is unclear, maybe due to the parameters we used to run STAR alignment.
For RNA metrics, we examined PCT_USABLE_BASES and PCT_INTRONIC_BASES, PCT_RIBOSOMAL_BASES. Since paired L100bp and L25bp samples are actually sequenced from the same library, it is not surprised to observe the high consistent rna metrics.
For Insertion Size metrics, we examined MEDIAN_INSERT_SIZE. High consistent between L100bp and L25bp samples.
For Duplication metrics, we examined PERCENT_DUPLICATION. L100bp show lower duplication rate.
