Quality Control for Single-Cell RNA-seq Data

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Documentation for package ‘cellity’ version 1.21.0

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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