View source: R/auto_cna_signal.R
auto_cna_signal | R Documentation |
Goes from reading raw gene counts to CNA-level signal, tSNE and community detection.
auto_cna_signal( data, genes_coord, prefix = "scCNAutils_out", nb_cores = 1, pause_after_qc = FALSE, use_cache = TRUE, sample_names = NULL, info_df = NULL, max_mito_prop = 0.2, min_total_exp = 0, cells_sel = NULL, chrs = c(1:22, "X", "Y"), cell_cycle = NULL, bin_mean_exp = 3, rm_cv_quant = NULL, z_wins_th = 3, smooth_wsize = 3, cc_sd_th = 3, nb_pcs = 10, comm_k = 100, viz = c("tsne", "umap", "both"), tsne.seed = 999, rcpp = TRUE )
data |
a data.frame with gene expression or the path to the folder with the 'matrix.mtx', 'genes.tsv' and 'barcodes.tsv' files. A list if multiple samples. |
genes_coord |
either a file name or a data.frame with coordinates and gene names. |
prefix |
the prefix to use for the files created by this function (e.g. graphs). |
nb_cores |
the number of processors to use. |
pause_after_qc |
pause after the QC to pick custom QC thresholds. |
use_cache |
should intermediate files used and avoid redoing steps? |
sample_names |
the names of each sample. If NULL, tries to use data's names. |
info_df |
a data.frame with information about cells. |
max_mito_prop |
the maximum proportion of mitochondrial RNA. |
min_total_exp |
the minimum total cell expression |
cells_sel |
consider only these cells. Other cells filtered no matter what. |
chrs |
the chromosome names to keep. NULL to include all the chromosomes. |
cell_cycle |
if non-null, either a file or data.frame to compute cell cycle scores. See details. |
bin_mean_exp |
the desired minimum mean expression in the bin. |
rm_cv_quant |
the quantile threshold to remove CV outlier. Default NULL (i.e. not used). |
z_wins_th |
the threshold to winsorize Z-score. Default is 3 |
smooth_wsize |
the window size for smoothing. Default is 3. |
cc_sd_th |
the number of SD used for the thresholds when defining cycling cells. |
nb_pcs |
the number of PCs used in the community detection or tSNE. |
comm_k |
the number of nearest neighbor for the KNN graph. Default 100. |
viz |
which method to use for visualization ('tsne', 'umap' or 'both'). Default is 'tsne'. |
tsne.seed |
the seed for the tSNE. |
rcpp |
use Rcpp function. Default is TRUE. More memory-efficient and faster when running on one core. |
a data.frame with QC, community and tSNE for each cell.
Jean Monlong
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