Package 'SiNMFiD'

Title: Supervised iNMF informed Deconvolution
Description: A package for completing cell type deconvolution on bulk spatial transcriptomic data utilizing multiple reference scRNA-seq datasets.
Authors: Joshua Sodicoff [aut, cre], Yichen Wang [ctb]
Maintainer: Joshua Sodicoff <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9000
Built: 2024-07-03 04:03:58 UTC
Source: https://github.com/welch-lab/SiNMFiD

Help Index


Calculate relationships between cell types

Description

Calculate relationships between cell types

Usage

analyze_gene_signatures(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  cell.types.use = NULL,
  return.objs = F
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

cell.types.use

A string of cell type labels to include in the plot, by default all cell types present

return.objs

Logical, whether to return a list of matrices of derived data

Value

named list of cosine similarity matrix and hierarchical clustering, if return.objs = TRUE


Calculate relationships between cell type distributions

Description

Calculate relationships between cell type distributions

Usage

analyze_spatial_correlation(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  mat.use = "proportions",
  cell.types.use = NULL,
  return.objs = F
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

mat.use

A string, either "raw" or "proportions" referring to what version of the results to summarize

cell.types.use

A string of cell type labels to include in the plot, by default all cell types present

return.objs

Logical, whether to return a list of matrices of derived data

Value

named list of pearson correlation matrix and hierarchical clustering, if return.objs = TRUE


Calculate cell sizes with all reference data

Description

Calculate cell sizes with all reference data

Usage

calculate_cell_sizes(
  data.list,
  annotations,
  filepath,
  analysis.name,
  datasets.remove = NULL,
  plot.hist = FALSE,
  chunk = 1000
)

Arguments

data.list

Various formats are allowed, including 1. a liger object; 2. a character vector containing file names to RDS/H5 files. 3. Named list of liger object, RDS/H5 file name, matrix/dgCMatrix. List option can have element types mixed. A liger object have to be of version older than 1.99. RDS files must contain base dense matrix or dgCMatrix supported by package "Matrix". H5 files must contain dataset processed by rliger < 1.99.

annotations

Named factor of all cell type assignments, should be concatenated from all datasets.

filepath

Path to analysis directory where output sampling needs to be stored.

analysis.name

String identifying the analysis, used to make up a sub-folder name.

datasets.remove

Character vector of datasets to be excluded from sampling if data.list is a liger object. Named list of dataset names for exluding datasets in liger objects passed with a list data.list. See sample_single_cell examples.

plot.hist

Logical, if to display and save histograms of nUMIs by cell type

chunk

Integer chunk size for processing sparse data stored in H5. Number of cells to load into memory per iteration. Default 1000.

Value

Nothing is returned, but the following file will be stored to local:

  • "<filepath>/<analysis.name>/cell_size_histogram.pdf" - A PDF file for the histogram that shows nUMI per cell distribution for each cell type

  • "<filepath>/<analysis.name>/cell_size.RDS" - RDS file of a named numeric vector object, total number of counts per cell type across all datasets.


Calculate the Wasserstein distance between cell-types and genes

Description

Calculate the Wasserstein distance between cell-types and genes

Usage

calculate_wasserstein(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  mat.use = "proportions",
  cell.types.use = NULL,
  genes.use = NULL,
  p = 2,
  min.samples = 1,
  return.objs = F
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

mat.use

A string, either "raw" or "proportions" referring to what version of the results to summarize

cell.types.use

A string of cell type labels to include in the plot, by default all cell types present

genes.use

A string of genes to include in a plot, by default none

p

The p exponent used for the Minkowski distance

min.samples

Integer value, the minimum number of samples a cell type can load on and be included in the analysis

return.objs

Logical, whether to return a list of matrices of derived data

Value

matrix of pairwise Wasserstein distances if return.objs = TRUE


Generate histograms of loading by cell type

Description

Generate histograms of loading by cell type

Usage

cell_type_loading_histogram(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  mat.use = "proportions",
  cell.types.plot = NULL,
  print.plots = TRUE,
  bin.num = 30
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

mat.use

A string, either "raw" or "proportions" referring to what version of the results to summarize

print.plots

Logical, whether to display results in the plots panel

bin.num

Integer number of bins to use in histogram

cell.types.use

A string of cell type labels to include in the plot, by default all cell types present

Value

nothing


Title

Description

Title

Usage

deconvolve_spatial(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  cell.size = T
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

cell.size

Logical, if to scale gene signatures by cell sizes

Value

nothing


Title

Description

Title

Usage

generate_label_gifs(
  filepath,
  analysis.name,
  spatial.data.name,
  labels.plot,
  dims = c(500, 500)
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

labels.plot

A named vector or matrix of labels to plot for the provided coordinates

dims

Integer vector of length 2 corresponding to the width and height of the RGL window

Value

nothing


Generate gifs of cell type distributions derived from deconvolution in space

Description

Generate gifs of cell type distributions derived from deconvolution in space

Usage

generate_loading_gifs(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  mat.use = "proportions",
  cell.types.plot = NULL,
  filter = NULL,
  dims = c(500, 500)
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

mat.use

A string, either "raw" or "proportions" referring to what version of the results to summarize

cell.types.plot

A character vector of cell types to plot

dims

Integer vector of length 2 corresponding to the width and height of the RGL window

Value

nothing


Title

Description

Title

Usage

learn_gene_signatures(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  lambda = 1,
  thresh = 1e-08,
  max.iters = 100,
  nrep = 1,
  print.obj = FALSE,
  verbose = FALSE
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

lambda

Double, regularization parameter for which increasing penalizes dataset-specific effects

thresh

Double, minimum fractional change in objective function to continue iteration

max.iters

Integer maximum of iterations to complete before pausing

nrep

Number of random starts to complete

print.obj

Logical, if to print current value of objective

verbose

Logical, if to print the final objective and best random seed

Value

nothing


Load data from one of multiple formats

Description

Load data from one of multiple formats

Usage

load_objs(objs, datasets.remove)

Arguments

objs

A named list of matrices (dgCMatrix), RDS file paths to matirces, H5 file paths to LIGER analyzed datasets.

Value

list object. List element type depends on input.


Flip axes in spatial data

Description

Flip axes in spatial data

Usage

mirror_spatial_coords(
  filepath,
  analysis.name,
  spatial.data.name,
  axes.flip = c(FALSE, FALSE, FALSE),
  overwrite = T
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

axes.flip

A vector with three logicals, corresponding to which of the axes to invert

overwrite

Logical, if the original data should be overwritten, otherwise "spatial.data.name_mirror⁠_x⁠/⁠_y⁠,⁠_z⁠is created

Value

nothing


Title

Description

Title

Usage

overlay_subregion_gifs(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  mat.use = "proportions",
  cell.types.plot = NULL,
  subregions.plot = NULL,
  filter = NULL,
  dims = c(500, 500)
)

Arguments

filepath

filepath

analysis.name

analysis.name

spatial.data.name

spatial.data.name

rand.seed

rand.seed

mat.use

mat.use

cell.types.plot

cell.types.plot

subregions.plot

subregions.plot

filter

filter

dims

dims

Value

nothing


Plot results of analyze_gene_signatures

Description

Plot results of analyze_gene_signatures

Usage

plot_analyze_gene_signatures(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  print.plots = T
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

print.plots

Logical, whether to display results in the plots panel

Value

nothing


Plot results of analyze_spatial_correlation

Description

Plot results of analyze_spatial_correlation

Usage

plot_analyze_spatial_correlation(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  print.plots = TRUE
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

print.plots

Logical, whether to display results in the plots panel

Value

nothing


Plot results of calculate_wasserstein

Description

Plot results of calculate_wasserstein

Usage

plot_calculate_wasserstein(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  print.plots = T
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

print.plots

Logical, whether to display results in the plots panel

Value

nothing


Plot results of summarize_by_layer

Description

Plot results of summarize_by_layer

Usage

plot_summarize_by_layer(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  print.plots = T
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

print.plots

Logical, whether to display results in the plots panel

Value

nothing


Quality-control spatial data

Description

Quality-control spatial data

Usage

qc_spatial_data(
  filepath,
  analysis.name,
  spatial.data.name,
  count.data = FALSE,
  z = 1,
  n.umi.thresh = 150,
  rand.seed = 123
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

count.data

Logical, if the spatial data is from a counts or intensity-based modality

z

Double, the standard deviations above the mean that the number of NAs in a gene can be before the gene is removed, for intensity data

n.umi.thresh

Integer number of counts below which to remove a sample, for counts based data

rand.seed

Integer random seed

Value

nothing


Generate silouhettes of the data along all three axes

Description

Generate silouhettes of the data along all three axes

Usage

reference_3d_coordinates(
  filepath,
  analysis.name,
  spatial.data.name,
  save.plots = FALSE
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

save.plots

A logical, corresponding with if to save requested plots upon generation

Value

nothing


Transfer labels from coarse-grained sampled

Description

Transfer labels from coarse-grained sampled

Usage

register_voxel_to_label(
  filepath,
  analysis.name,
  spatial.data.name,
  labels.use,
  label.name
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

labels.use

Named vector of labels for the prevoxelized data

label.name

String identifying the label set

Value

nothing


Sample from single cell reference datasets

Description

Sample from single cell reference datasets

Usage

sample_single_cell(
  data.list,
  annotations,
  filepath,
  analysis.name,
  datasets.remove = NULL,
  n.cells = 500,
  rand.seed = 123,
  chunk = 1000
)

Arguments

data.list

Various formats are allowed, including 1. a liger object; 2. a character vector containing file names to RDS/H5 files. 3. Named list of liger object, RDS/H5 file name, matrix/dgCMatrix. List option can have element types mixed. A liger object have to be of version older than 1.99. RDS files must contain base dense matrix or dgCMatrix supported by package "Matrix". H5 files must contain dataset processed by rliger < 1.99.

annotations

Named factor of cell type assignments.

filepath

Path to analysis directory where output sampling needs to be stored.

analysis.name

String identifying the analysis, used to make up a sub-folder name.

datasets.remove

Character vector of datasets to be excluded from sampling if data.list is a liger object. Named list of dataset names for exluding datasets in liger objects passed with a list data.list.

n.cells

Integer value corresponding to maximum number of samples per cell type. Default 500.

rand.seed

Integer random seed for reproducible sampling.

chunk

Integer chunk size for processing sparse data stored in H5. Number of cells to load into memory per iteration. Default 1000.

Value

Nothing is returned. File "norm_data.RDS" will be stored under "<filepath>/<analysis.name>/<rand.seed>/", containing a list of downsampled scaled (not centered) data matrix. File "sampled_cells.RDS" is stored at the same path, containing barcode vector of the sampled cells. File "source_annotations.RDS" is stored at "<filepath>/<analysis.name>/" which contains input annotations.

Examples

## Not run: 
# Explanation for how `datasets.remove` works with example:

names(lig@raw.data)
# above should show "data1", "data2", "data3", ...
# Then when sampling from `lig`, the first two datasets can be excluded with
sample_single_cell(data.list = lig, datasets.remove = c("data1", "data2"))

# If we got a list of liger object
sample_single_cell(data.list = list(human = lig1, mouse = lig2),
                   datasets.remove = list(human = c("data1", "data2"),
                                          mouse = c("10x1")))

## End(Not run)

Add a new spatial dataset to the analysis directory

Description

Add a new spatial dataset to the analysis directory

Usage

save_spatial_data(
  filepath,
  analysis.name,
  spatial.data.file,
  coords.file,
  spatial.data.name
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.file

Path to an RDS file containing desired expression data

coords.file

Path to an RDS file containing desired coordinate data

spatial.data.name

String identifying the spatial sample

Value

nothing


select variable genes with the Kruskal-Wallis test

Description

select variable genes with the Kruskal-Wallis test

Usage

select_defining_genes(
  filepath,
  analysis.name,
  deconv.gene.num = 2000,
  gene.num.tol = 50,
  rand.seed = 123
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

deconv.gene.num

Integer, the number of genes to select

gene.num.tol

Integer, the maximum difference between the number of genes selected and deconv.gene.num

rand.seed

Integer random seed

Value

nothing


Set up new analysis directory

Description

Set up new analysis directory

Usage

start_analysis(filepath, analysis.name)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

Value

nothing


Subset a spatial dataset by coordinates for analysis

Description

Subset a spatial dataset by coordinates for analysis

Usage

subset_spatial_data(
  filepath,
  analysis.name,
  spatial.data.name,
  subset.specs = list(c(NaN, NaN), c(NaN, NaN), c(NaN, NaN)),
  new.spatial.data.name = NULL,
  out.filepath = NULL
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

subset.specs

A list with length equal to the number of axes, with each entry a vector of length two, with the first element being the minimum value to include and the second being the maximum, or NaN to indicate a missing value

new.spatial.data.name

String, optional name for new analysis, otherwise the default "spatial.data.namesubsetn.samples" is used

out.filepath

Path to directory to save subset data to if not within the analysis

Value

nothing


Summarize cell-type and gene expression data by

Description

Summarize cell-type and gene expression data by

Usage

summarize_by_layer(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  layer.list,
  type = "mean",
  mat.use = "proportions",
  cell.types.use = NULL,
  genes.use = NULL,
  return.objs = FALSE
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

layer.list

A named list of spatial samples by layer of interest

type

A string, either "mean" or "sum", how results should be combined for summary

mat.use

A string, either "raw", "proportions", or "assignments" referring to what version of the results to summarize

cell.types.use

A string of cell type labels to include in the plot, by default all cell types present

genes.use

A string of genes to include in a plot, by default none

return.objs

Logical, whether to return a list of matrices of derived data

Value

cell-type and gene expression data summarized by layer in a named list, if return.objs = TRUE


Summarize cell types present in the source annotations

Description

Summarize cell types present in the source annotations

Usage

summarize_clusters(filepath, analysis.name, return.objs = F)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

return.objs

Logical, whether to return a vector of the names of clusters

Value

A vector of unique clusters in the source annotations, if return.objs = TRUE


Summarize subregions of a vector of regions of interest

Description

Summarize subregions of a vector of regions of interest

Usage

summarize_subregions(
  regions,
  ontology.file = "Downloads/allen_structure_ontology.csv",
  return.objs = F
)

Arguments

regions

A vector of region names

ontology.file

A csv describing the Allen structure ontology

return.objs

Logical, whether to return acronyms for all subregions found

Value

A vector of unique subregions within the provided regions, if return.objs = TRUE


Use predefined transformations to match some modalities to the Allen CCF

Description

Use predefined transformations to match some modalities to the Allen CCF

Usage

transform_coords_to_ccf(filepath, analysis.name, spatial.data.name, ish = T)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

ish

Logical, if the data comes from the Allen Institute quantified ISH dataset

Value

nothing


Title

Description

Title

Usage

view_in_rgl(
  filepath,
  analysis.name,
  spatial.data.name,
  rand.seed = 123,
  cell.type,
  mat.use = "proportions",
  filter.samples = NULL,
  dims = c(500, 500)
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

rand.seed

Integer random seed

cell.type

A string corresponding to one cell type found in the deconvolution results

mat.use

A string, either "raw" or "proportions" referring to what version of the results to summarize

filter.samples

Value for binarizing results, either presence above the provided threshold or absence below

dims

Integer vector of length 2 corresponding to the width and height of the RGL window

Value

nothing


Coarse-grain spatial data to a predetermined resolution

Description

Coarse-grain spatial data to a predetermined resolution

Usage

voxelize_single_cells(
  filepath,
  analysis.name,
  spatial.data.name,
  voxel.size,
  out.filepath = NULL,
  verbose = TRUE
)

Arguments

filepath

Path to analysis directory

analysis.name

String identifying the analysis

spatial.data.name

String identifying the spatial sample

voxel.size

Integer, side length of one voxel

out.filepath

Path to directory to save subset data to if not within the analysis

verbose

Logical, if to print several lines of metadata on results

Value

nothing