Deconvolution

Deconvolution functions

music_prop()

MuSiC Deconvolution

music_prop.cluster()

MuSiC Deconvolution with Clusters

music.basic()

Estimate cell type proportion with MuSiC and NNLS

music.iter()

Scaling bulk data and signature matrix and estimate cell type proportion

music.basic.ct()

Estimate cell type proportion with MuSiC and NNLS

music.iter.ct()

Scaling bulk data and signature matrix and estimate cell type proportion

weight.cal.ct()

Calculate weight with cross cell type covariance

Weight_cal()

Calculate weight with cross-subject variance for each cell types

Construction

Construct artificial bulk expression. Construct matrices for MuSiC deconvolution, such as design matrix, cross-subject mean matrix, cross-subject variance matrix and library size vector.

bulk_construct()

Construct artificial bulk tissue expression from single cell data

music_basis()

Prepare Design matrix and Cross-subject Variance for MuSiC Deconvolution

music_M.theta()

Cross-subject Mean of Relative Abudance

music_Theta()

Subject and cell type specific relative abundance

music_Sigma.ct()

Cross-subject Covariance of Relative Abundance

music_Sigma()

Cross-subject Variance of Relative Abundance

music_S()

Cell type specific library size

music_Design.matrix()

Cell type specific library size

MuSiC2

Cell type deconvolution for multi-condition bulk RNA-seq data

music2_prop()

MuSiC2_Deconvolution

music2_prop_t_statistics()

MuSiC2 Deconvolution with T Statistics

music2_prop_toast()

MuSiC2 Deconvolution with TOAST

Graphics

Create graphics for visualization and evaluation. The numerical evaluation function is also included.

Relative_gene_boxplot()

Boxplot of relative abundance

Prop_comp_multi()

Plot heatmap of real and estimated cell type proportions

Abs_diff_multi()

Plot heatmap of Absolute difference between estimated and real cell type proportions

Scatter_multi()

Plot Scatter plot of real and estimated cell type proportions

Boxplot_Est()

Boxplot of estimated cell type proportions

Jitter_Est()

Jitter plot of estimated cell type proportions

Prop_heat_Est()

Heatmap of estimated cell type proportions

Eval_multi()

Evaluate estimation methods

Prop_convert()

Convert list of real and estimated cell type proportions to data frame

CellTotal.df()

Compare cell type specific total expression (library size) between 2 dataset

plotCellTotal.two()

Plot the cell type specific library size of 2 single cell datasets

Simulations

Generate simulations

Twocelltype.Generator()

Simulate Single cell read counts

Eval_sim_boxplot()

Evaluate simulation results by boxplots

Utilities

relative.ab()

Calculate relative abundance

fpkmToTpm()

Calculate fpkm to tpm

my.log()

log transformation with regulation

my.sqrt()

Calculate square root of all values

my.rowMeans()

Calculate Row Means with NA remove

get_upper_tri()

Get upper triangle matrix

Anova_info()

ANOVA Test for each gene