Package index

Data preprocessing

createProjectSpace(): Create a project space for scMINER analysis

Data intake

Functions to read input data from multiple formats

readInput_10x.dir(): Read the input data generated by 10x Genomics from a directory

readInput_10x.h5(): Read the input data generated by 10x Genomics from the HDF5 file

readInput_h5ad(): Read the h5ad file

readInput_table(): Read the table format file

SparseEset manipulation

Functions that help create and filter SparseEset objects

createSparseEset(): Create a sparse expression set object from a data matrix

combineSparseEset(): Combine multiple sparse expression set objects

updateSparseEset(): Update the slots and/or meta data of the sparse eset object

filterSparseEset(): Filter the cells and/or features of sparse eset object using automatic or self-customized cutoffs

normalizeSparseEset(): Normalize and log-transform the sparse eset object

drawSparseEsetQC(): Generate a quality control report from sparse eset object

MI-based clustering analysis

Functions that mainly used in clustering analysis

generateMICAinput(): Generate the standard input files for MICA from sparse eset object

addMICAoutput(): Add the MICA output (cluster labels and UMAP/tSNE coordinates) to sparse eset object

MICAplot(): Draw a scatter plot showing the coordinates and cluster id of each cell

MI-based network inference

Functions that principally operate for network inference

getDriverList(): Extract the pre-defined driver lists of human or mouse

generateSJARACNeInput(): Generate the standard input files for SJARACNe from sparse eset object

drawNetworkQC(): Assess the quality of each network generated by SJARACNe

getActivity_individual(): Calculate driver activities per group from network files

getActivity_inBatch(): Calculate driver activities in batch from the SJARACNe directory

Functions that visulize the data analysis and prepare inputs for scMINER Portal

feature_vlnplot(): Violin plot showing the expression or activity of selected features by self-defined groups

feature_boxplot(): Box plot showing the expression or activity of selected features by self-defined groups

feature_scatterplot(): Scatter plot showing the expression or activity of selected features on UMAP or t-SNE coordinates

feature_bubbleplot(): Bubble blot showing the expression or activity of selected features by self-defined groups

feature_heatmap(): Heatmap showing the expression or activity of selected features by self-defined groups

draw_barplot(): Bar plot showing the cell composition of self-defined groups

draw_bubbleplot(): Bubble plot showing the signature scores by self-defined groups

generatePortalInputs(): Prepare the standard input files for scMINER Portal

Differential analysis

Functions that identify the differntially expressed genes (DEGs) or differentially activated drivers (DADs)

getDE(): Perform differential expression analysis on expression set object

getDA(): Perform differential activity analysis on expression set

getTopFeatures(): Pick the top genes/drivers for each group from differential analysis results

Helper functions

Functions that make the primary functions simpler

combinePvalVector(): Combine P values using Fisher's method or Stouffer's method

compare2groups(): Perform differential analysis between two groups

z_normalization(): Scale a numeric vector using Z-normalization

get_net2target_list(): Convert a txt network file to a list

get_target_list2matrix(): Convert ther list generated by get_net2target_list() to a matrix of signed mutual information

cal_Activity(): Calculate driver activities from gene expression matrix and networks

SparseExpressionSet-class: SparseExpressionSet

pbmc14k_rawCount: Raw count matrix of PBMC14k dataset

pbmc14k_expression.eset: SparseEset object of PBMC14k dataset