QuantBio’s scRNA-seq platform

QB_QuickPassQC and QB_StanAnnDEX

Single-cell RNA sequencing (scRNA-Seq) is a powerful tool that is growing in popularity thanks to its utility.

We have built a standardized pipeline that speeds up QC and processing of single-cell data and enables complete transparency and reproducibility. Our platform consists of two applications, QB_QuickPassQC, and QB_StanAnnDEX.

QB_QuickPassQC allows us to rapidly assess new and un-vetted single-cell data sets for overall quality, doublet presence1, ambient RNA contamination2, and confounding technical covariates.

QB_QuickPassQC also enables us to swiftly identify biologically and statistically sound filtering thresholds and confounding variables which we utilize in our QB_StanAnnDEX platform.

In Brief:

  • Multiple filtering thresholds are run and their effects on downstream annotation of cell types, including the retention or loss of minority cell type populations is assessed.
  • Multiple clustering algorithms and resolutions are utilized and concordance with annotated cell types is measured via the Adjusted Rand Index (ARI).
  • For each dataset, we calculate the association between technical and biological covariates with a dimension-reduced representation of the data, allowing us to identify potential technical effects to control for during differential expression.

QB_StanAnnDEXis a containerized R application that performs (1) filtering and standardization, (2) annotation, and (3) differential expression on a single-cell RNA-Seq data.

We utilize this pipeline on pre-filtered data sets or data sets that have been vetted internally via QB_QuickPassQC.

A brief explanation of each step within the pipeline is provided below:

(1) Quality control and standardization:

We perform quality control at the individual cell and sample level. All cells and samples that are low quality are removed. Expression matrices are then normalized and if necessary, confounding variables are regressed out.

(2) Annotating and clustering cells:

Cells are annotated with either standard or custom references using the R package, SingleR3. After annotating cells, QB performs dimension reduction via PCA followed by graph-based clustering, and then visualizations via UMAP.

(3) Differential Expression:

During QC, we have automated the assessment of client-provided covariates, which may be of technical or biological nature. This allows us to conduct differential expression (DE) analyses with the proper controls, and regress technical covariates as needed. After completion of annotation and clustering, differential expression is performed utilizing Wilcoxon’s rank sum test within Seurat4,5. Final DE lists are determined and outputted.

References

  1. Bais AS, Kostka D (2020) scds: computational annotation of doublets in single-cell RNA sequencing data. Bioinformatics. 36(4):1150-1158. https://doi.org/10.1093/bioinformatics/btz698
  2. Yang S, Corbett SE, Koga Y, Wang Z, Johnson WE, Yajima M, Campbell JD (2020) Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biology. 21(1):57. >https://doi.org/10.1186/s13059-020-1950-6
  3. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology. 20:163-172. https://doi.org/10.1038/s41590-018-0276-y
  4. Satija R, Farrell J, Gennert D. et al. (2015) Spatial reconstruction of single-cell gene expression data. Nature Biotechnology. 33:495–502. https://doi.org/10.1038/nbt.3192
  5. Soneson C, Robinson M. (2018) Bias, robustness and scalability in single-cell differential expression analysis. Nature Methods. 15:255–261. https://doi.org/10.1038/nmeth.4612