Package index • asapR

All functions

asap_bbknn(): Reconcile multi-batch matrices by batch-balancing KNN

asap_build_interacting_columns(): Identify pairs of columns interacting with one another

asap_build_interaction_columns_mtx(): Identify pairs of columns interacting with one another

asap_fit_pmf(): A quick PMF estimation based on alternating Poisson regressions

asap_fit_pmf_cbind(): A quick PMF estimation based on alternating Poisson regressions while sharing a dictionary/factors matrix

asap_fit_pmf_delta(): A quick PMF estimation based on alternating Poisson regressions across multiple matrices with the same dimensionality

asap_fit_pmf_larch(): A quick PMF estimation based on alternating Poisson regressions with tree-structured priors

asap_fit_pmf_rbind(): A quick PMF estimation based on alternating Poisson regressions while sharing a factor loading/topic proportion matrix while concatenating the rows of data matrices

asap_pmf_regression(): PMF regression

asap_pmf_regression_cbind_mtx(): PMF statistics to estimate factor loading

asap_pmf_regression_mtx(): PMF regression

asap_pmf_stat_interacting_columns_mtx(): Topic statistics to estimate factor loading

asap_pmf_stat_rbind(): Topic statistics to estimate factor loading

asap_random_bulk_cbind(): Generate approximate pseudo-bulk data by random projections while sharing rows/features across multiple data sets. Horizontal concatenation.

asap_random_bulk_cbind_mtx(): Generate approximate pseudo-bulk data by random projections while sharing rows/features across multiple data sets. Horizontal concatenation.

asap_random_bulk_interacting_columns(): Generate approximate pseudo-bulk interaction data by random projections

asap_random_bulk_linking_mtx(): Generate approximate pseudo-bulk data by random projections while linking features across multiple mtx files

asap_random_bulk_mtx(): Generate approximate pseudo-bulk data by random projections

asap_random_bulk_rbind_mtx(): Generate approximate pseudo-bulk data by random projections while sharing columns/cells across multiple data sets. Vertical concatenation.

asap_stretch_nn_matrix_columns(): Stretch non-negative matrix

asap_topic_pmf(): Calibrate topic proportions based on sufficient statistics

asap_topic_pmf_rbind(): Calibrate topic proportions based on sufficient statistics

collapse_network(): Collapse N x N adjacency network into S x S

decompose_network(): Assign best topic membership for the edges

fileset.list(): Create a list of MTX-related files

fit_poisson_cluster_rows(): Clustering the rows of a count data matrix

mmutil_build_index(): Create an index file for a given MTX

mmutil_check_index(): Check if the index tab is valid

mmutil_colnames(): Just read each col name per line

mmutil_copy_selected_columns(): Take a subset of columns and create a new MTX file-set

mmutil_copy_selected_rows(): Take a subset of rows and create a new MTX file-set

mmutil_info(): Just read the header information

mmutil_read_columns(): Read a subset of columns from the data matrix

mmutil_read_columns_sparse(): Read a subset of columns from the data matrix

mmutil_read_index(): Read an index file to R

mmutil_read_rows_columns(): Read a subset of rows and columns from the data matrix

mmutil_rownames(): Just read each row name per line

mmutil_simulate_poisson(): Simulation Poisson data based on Mu

mmutil_simulate_poisson_mixture(): Simulate sparse counting data with a mixture of Poisson parameters

mmutil_write_mtx(): Write down sparse matrix to the disk

pmf2topic(): Convert Poisson Matrix Factorization to Multinomial Topic model. The same idea was first coined by fastTopics paper.

project.proportions(): Create 2D projection of topic proportions

rbind(<mtx>): A wrapper function that concatenates two files vertically

read.mtx.dense(): A wrapper function to read a dense submatrix

read.mtx.sparse(): A wrapper function to read a sparse submatrix

read.vec(): Read a vector of string names

write.sparse(): Write matrix market file set