SmartSeq3 scRNA-seq
Institut Curie NGS/Bioinformatics core facilities
A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report
generated on 2022-04-17, 21:12
based on data in:
/home/litd/SmartSeq3/work/9c/cf82fa8c28349740546b8841c1ba61
Change sample names:
General Metrics
Result summaries of each cell. The number of fragments is the number of cDNA sequenced. It is the sequencing depth. The percentage of UMIs is the proportion of fragments having tag-UMI-GGG pattern within their sequences. At least 50% are expected to have a UMI. Alignment is made on non UMI and UMI reads and should be around 70%. The assignment is made on correctly aligned reads and should be arround 60%. The number of genes is calculated from UMI reads only. A minimum of 5 000 genes and 30 000 UMIs are expected.
| Sample ID | Sample Name | Total Fragments | UMI reads (%) | Aligned (%) | Assigned (%) | UMIs | Genes |
|---|---|---|---|---|---|---|---|
| V590T10 | V590T10 | 816926 | 69.0% | 82.3% | % | 55829 | 7483 |
Mapping summary
Overview of mapping steps corresponding to alignment & assignment. The alignment is done on all reads (UMI and non UMI reads). Correct reads are aligned on the genome and assigned to a gene.
featureCounts
Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.
RSeQC
RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.
Gene Body Coverage
Gene Body Coverage calculates read coverage over gene bodies. This is used to check if reads coverage is uniform and if there is any 5' or 3' bias.
Expression level
Distribution of the number of UMIs per gene in each sample. Genes having more than 70 UMIs (x axis) are not shown. Most genes have between one and 10 UMIs and a bend can be observed around 5.
Library complexity
Genes are those found in the UMI matrices (non UMI reads are not take into account). In SmartSeq3, cells should have more than 5000 genes.
Number of UMIs per cell
At least 30 000 UMIs are expected.
Number of genes & UMIs per cell
A scatter plot of the ratio of the number of genes and UMIs per cell allow an easy representation of a cell composition.
Cell viability
Percentage of mitochondrial RNAs is a cell viability marker. It varies according your cell type (e.g cell line, primary cells, etc.). Here, only UMI reads are take into account.
Gene-based saturation
plot showing the number of detected genes for a given set of subsampling values.
Sequencing Complexity
Sequencing Complexity estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.
Complexity curve
Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.
Software Versions
are collected at run time from the software output.
- Pipeline
- v1.0.0
- Nextflow
- v21.10.6
- cutadapt
- v3.1
- seqkit
- v0.14.0
- STAR
- v2.7.6a
- umi_tools
- v1.1.1
- samtools
- v1.11
- deeptools
- v3.5.0
- rseqc
- v4.0.0
- R
- v4.0.3
- preseq
- v2.0.3
Workflow Summary
- this information is collected when the pipeline is started.
- Pipeline Name
- SmartSeq3
- Pipeline Version
- 1.0.0
- Run Name
- nostalgic_bassi
- Command Line
- nextflow run main.nf -profile test,singularity --singularityImagePath /home/litd/SmartSeq3/singularity_image --genomeAnnotationPath /home/litd/SmartSeq3 --maxCpus 12 --maxMemory 48.GB --maxTime 240.h
- Reads
- N/A
- Genome
- hg38
- Annotation
- /home/litd/SmartSeq3
- Max Memory
- 48.GB
- Max CPUs
- 12
- Max Time
- 240.h
- Current home
- /home/litd
- Current user
- litd
- Current path
- /home/litd/SmartSeq3
- Working dir
- /home/litd/SmartSeq3/work
- Output dir
- ./results
- Config Profile
- test,singularity
