Last updated: 2017-01-03
Code version: d222594411724cecd242d834f8bcfee6488499c5
We used this site to collaborate and share our results. Please feel free to explore. The results that made it into the final paper are in the section Finalizing below. Here are some useful links:
Analysis
- Identification of noisy genes
- Compare read versus molecule counts - (per batch,per batch conversion rates comparison, per cell)
- Variance within and between individaul
- Correlation with ERCC spike-ins
- ERCC normalization
- Subsample - (LCLs)
- Cell-cycle analysis - (final), (cycle-gene-set)
- pluripotency gene expression
- Proportion of detected genes - (filter and final)
- Batch effect correction with mixed modeling - (linear transformation, filter and final, Poisson transformation, filter and final)
- Individual PCAs
- Ordering effect of capture sites
- Extract gene symbols
- Cell-to-cell variation analysis
- CV and sparsity
- Mean-adjusted CV – ( Normalize CV excluding outlier batch, Normalize CV including outlier batch, Gene rankings, Annotations, Gene-level dissimilarity measure )
- Compare mean-adjusted CVs – (ANOVA, ANOVA & annotations, Sum of Squared Deviation from the Median, Permutation-based p-values )
- Noisy genes analysis – (final and Gaussian-based transformation, final and Poisson-based transformation, Pluripotent gene density plots )
- Putting it all together… (All cells, Expressed cells, Undetected cells )
- Quality control plots
- Total molecule-counts and standardization
- Exploring sequence coverage
- Endogenous genes
- ERCC spike-ins
- Using ngsplot
- PCA quantification
LCL data from a full flowcell