barcode_rank_plot | Create a barcode rank plot |
call_targets | Call clean droplets after running EM |
convert_to_seurat | Convert an SCE object to Seurat |
create_SCE | Create an SCE object from a sparse matrix |
diem | Run DIEM pipeline |
divide_by_colsum | Divide elements of a column by the column's sum in a sparse... |
dmmn | Compute density of multinomial mixture for a matrix |
dmultinom_sparse | Get log multinomial density of columns in a sparse matrix. |
droplet_data | Return the droplet data from an SCE object |
em | EM function |
e_step_mn | Computed expected log likelihood of multinomial mixture |
fill_counts | Fill information from raw counts |
filter_genes | Filter out lowly expressed genes |
fraction_log | fraction of logs |
gene_data | Return the gene data from an SCE object |
get_clean_ids | Return IDs of clean droplets |
get_gene_pct | Get percent of reads align to given gene(s) |
get_knn | Get k-nearest neighbor graph |
get_removed_ids | Return IDs of removed droplets |
get_var_genes | Get variable genes |
initialize_clusters | Initialize clustering for EM |
init_param | Initialize EM parameters given group assignments |
mb_small | Single-nucleus RNA-seq of mouse brain |
m_step_mn | Maximize multinomial parameters in EM |
normalize_data | Normalize raw counts. |
norm_counts | Normalize counts of a sparse matrix |
plot_umi_gene | Scatterplot of genes vs. UMI counts, colored by posterior... |
plot_umi_gene_call | Scatterplot of genes vs. UMI counts, colored by call |
raw_counts | Return raw counts |
read_10x | Read 10X counts data |
run_em | Run EM on counts to estimate multinomial mixture model |
SCE-class | SCE |
set_cluster_set | Set droplets for cluster initialization |
set_debris_test_set | Set debris and test droplets |
sum_log | sum of logs |
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