Man pages for jhsiao999/ashbun
Methods and evaluation tools for single cell RNA-seq count data
| args.big_normal | Normal mixture prior. Default 1 component. |
| args.bimodal | bimodal prior |
| args.flat_top | flat top prior |
| args.near_normal | near normal prior |
| args.spiky | spiky prior |
| args.uni_natural | unimodal distribution with a non-zero mode |
| args.uni_natural_pm_zero | bi-modal distribution, with a non-zero mode and point mass at... |
| args.uni_natural_uni_zero | bi-modal distribution, with a non-zero mode and a mode at... |
| args.uni_zero | unimodal distribution with mode at zero |
| ashPoissonWrapper | ash-Poisson wrapper for evaluating different bimodality... |
| dens_unimix | compute density for all components in the mixture prior |
| dens_unimix_sing | compute density for a single component in the mixture prior |
| filter.excludeAllZeros | Filter all-zero samples and features. |
| filterFeatures.fractionExpressed | Filter genes by fraction of samples detected as expressed |
| filterSamples.fractionExpressed | Filter samples by fraction of genes detected as expressed |
| filter.Wrapper | Wrapper for all filtering steps |
| gene_variation | Per gene variance component model |
| getAUC | Compute AUC |
| getROC | compute ROC related informatino using pROC package |
| getROC.average | Plot ROC curve average |
| getTPR | Compute sensitivity or true positive rate given fixed FDR |
| getTPR.pROC | Compute sensitivity or true positive rate given fixed FDR... |
| Implement | MAST |
| make_normalmix | Generate beta (effects) from normal mixture prior |
| makeSimCount2groups | Generate count matrix of all null genes |
| makeSimCount2groups.filter | Generate count matrix of all null genes |
| methodWrapper.bpsc | BPSC |
| methodWrapper.DESeq2 | DESeq2 |
| methodWrapper.edgeR | edgeR |
| methodWrapper.limmaVoom | limma + voom |
| methodWrapper.num_sv | Estimate number of surrogate variables |
| methodWrapper.scde | SCDE |
| methodWrapper.sva | Surrogate Variable Analysis |
| negbin | Fit Negative binomial to gene expression counts (univariate) |
| negbin_hurdle | Hurdle negative binomial |
| negbin_zif | Zero-inflated negative binomial |
| non_null_sim | Simulate count matrix |
| normalize.census | census |
| normalize.cpm | Counts per million |
| normalize.lib | Library size normalization without adjustment |
| normalize.rle | RLE (relative log expression) |
| normalize.scnorm | SCnorm 1.1.0 |
| normalize.scran | scran |
| normalize.tmm | TMM |
| pois_thinning | Poisson thinning |
| query.evaluation | Evaluate multiple normalization methods and multiple DE... |
| query.methodsMeanExpression | Runs multiple DE methods |
| query.methodsNormalization | Run multiple normalization methods |
| query.pipeline | Run multiple normalization methods and multiple DE methods |
| sampleingene | Randomisation of sample at each gene |
| simulationWrapper | Wrapper for simulating M datasets |
| simulationWrapper.filter | Wrapper for simulating M datasets |
| voom.controlPseudocount | limma + voom |
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