R: Quality Control for Single-Cell RNA-seq Data

# Quality Control for Single-Cell RNA-seq Data

## Documentation for package ‘cellity’ version 1.40.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

cellity-package | Quality Control for Single-Cell RNA-seq Data
assess_cell_quality_PCA | ASSESS CELL QUALITY USING PCA AND OUTLIER DETECTION
assess_cell_quality_SVM | Assess quality of a cell - SVM version
extract_features | Extracts biological and technical features for given dataset
extra_human_genes | Additional human genes that are used in feature extraction
extra_mouse_genes | Additional mouse genes that are used in feature extraction
feature_generation | Helper Function to create all features
feature_info | Information which genes and GO categories should be included as features. Also defines which features are cell-type independent (common features)
mES1_features | Real test dataset containing all and common features from the paper (mES1)
mES1_labels | Real test dataset containing annotation of cells
multiplot | Internal multiplot function to combine plots onto a grid
normalise_by_factor | Internal function to normalize by library size
param_mES_all | Parameters used for SVM classification
param_mES_common | Parameters used for SVM classification
plot_pca | Plots PCA of all features. Colors high and low quality cells based on outlier detection.
sample_counts | Sample gene expression data containing 40 cells
sample_stats | Sample read statistics data containing 40 cells
simple_cap | Converts all first letters to capital letters
sum_prop | Sums up normalised values of genes to groups.
training_mES_features | Original training dataset containing all and common features from the paper (training mES)
training_mES_labels | Original training dataset containing annotation of cells
uni.plot | Internal function to detect outliers from the mvoultier pacakge Modified slightly so that plots are not printed