Quality Control for Single-Cell RNA-seq Data

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Documentation for package ‘cellity’ version 1.30.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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