Normalization of single-cell RNA-seq counts by log(x + 1)* or log(1 + x).
Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don't hold when examining low-expressed genes, with the result that standard workflows can produce misleading results.
Supplementary data and all of the code to reproduce Figure 1 are available here: https://github.com/pachterlab/BP_2020_2/.
1. Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.
2. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
3. SCell: integrated analysis of single-cell RNA-seq data.
4. netAE: semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling.
5. Normalization of single-cell RNA-seq counts by log(x + 1)* or log(1 + x).
1. Designing a single cell RNA sequencing benchmark dataset to compare protocols and analysis methods (RNAmix_CEL-seq2 )
2. Multi-modal analysis of the aging mouse lung at cellular resolution
3. Ensemble learning for classifying single-cell data and projection across reference atlases
4. Single cell RNA-seq data of human hESCs to evaluate SCnorm: robust normalization of single-cell rna-seq data