Help for package SRTsim

Type:

Package

Title:

Simulator for Spatially Resolved Transcriptomics

Version:

0.99.7

Date:

2024-08-14

Maintainer:

Jiaqiang Zhu <jiaqiang@umich.edu>

Description:

An independent, reproducible, and flexible Spatially Resolved Transcriptomics (SRT) simulation framework that can be used to facilitate the development of SRT analytical methods for a wide variety of SRT-specific analyses. It utilizes spatial localization information to simulate SRT expression count data in a reproducible and scalable fashion. Two major simulation schemes are implemented in 'SRTsim': reference-based and reference-free.

License:

GPL (≥ 3)

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.2

Imports:

concaveman,sf,sp,spatstat.geom,parallel,pdist,MASS,S4Vectors,stats,Matrix,Morpho,matrixStats,plotly,shiny,viridis,shinydashboard,dashboardthemes,shinyBS, ggplot2, ggpubr, DT, spatstat.random,magrittr,FNN,dplyr, magick

Suggests:

knitr,rmarkdown,BiocStyle,RefManageR,BiocManager,sessioninfo

VignetteBuilder:

knitr

Depends:

R (≥ 3.5.0), methods

NeedsCompilation:

Collate:

'SRTaffine.R' 'SRTcount.R' 'SRTfit.R' 'SRTmodels.R' 'SRTsim_class.R' 'body.R' 'compareSRT.R' 'createSRT.R' 'data.R' 'globals.R' 'reGenCountshiny.R' 'runapp.R' 'server.R' 'shiny2srt.R' 'sidebar.R' 'simSRTLocs.R' 'subsetSRT.R' 'SRTcci.R' 'ui.R' 'utilies_func.R' 'visualize_gene.R' 'visualize_metrics.R'

Packaged:

2024-08-20 21:02:00 UTC; jiaqiangzhu

Author:

Jiaqiang Zhu

[aut, ctb, cre], Lulu Shang

[aut], Xiang Zhou

[aut]

Repository:

CRAN

Date/Publication:

2024-08-21 08:10:05 UTC

Access Model Fitting Parameters

Description

Access Model Fitting Parameters

Usage

EstParam(x)

Arguments

x

SRTsim object

Value

Returns a list of estimated parameters by fitting models

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
EstParam(toySRT)

Run the SRTsim Shiny Application

Description

Run the SRTsim Shiny Application

Usage

SRTsim_shiny()

Value

A list that contains a count matrix, a location dataframe, and all parameter specifications.

Examples


## Not run: 
 ## Will Load an Interactive Session
shinyOutput <- SRTsim_shiny()

## End(Not run)

Create a SRTsim object from reference-free shinyoutput

Description

Create a SRTsim object from reference-free shinyoutput

Usage

Shiny2SRT(shinyOutput)

Arguments

shinyOutput

A list of Shiny Output. Including a simCount, simInfo,simcountParam,simLocParam

Value

Returns a SRTsim object with user-specified parameters stored in metaParam slot.

Examples


shinySRT <- Shiny2SRT(toyShiny)

## Explore the new SRT object
shinySRT@metaParam
shinySRT@simCounts[1:3,1:3]
shinySRT@simcolData

Summarize metrics for reference data and synthetic data

Description

Summarize metrics for reference data and synthetic data

Usage

compareSRT(simsrt)

Arguments

simsrt

A SRTsim object

Value

Returns an object with summarized metrics

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
## Compute metrics 
toySRT   <- compareSRT(toySRT)

Convert continuous coordinate into integer, essential for BayesSpace to determine the neighborhood info

Description

Convert continuous coordinate into integer, essential for BayesSpace to determine the neighborhood info

Usage

convert_grid(x)

Arguments

x

A numeric vector of continuous coordinate

Value

Returns a numeric vector oof integer coordinate

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)

## Create New Locations within Profile
toySRT2  <- srtsim_newlocs(toySRT,new_loc_num=1000)

## Convert non-integer x-coordinates into an integer value
newGrid_x <- convert_grid(simcolData(toySRT2)$x)

Create simSRT object

Description

Create simSRT object

Usage

createSRT(count_in, loc_in, refID = "ref1")

Arguments

count_in

A gene expression count matrix

loc_in

A location dataframe with colnames x,y,label

refID

A character reference sample identifier. Default = ref1.

Value

Returns a spatialExperiment-based object

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)

## Explore the object
toySRT

Data used for creating vignettes

Description

A data list containing the a gene expression matrix and a location dataframe

Usage

exampleLIBD

Format

A data list

example_count: A sparse matrix with 80 rows and 3611 columns.
example_loc: A data frame with 3611 rows and 6 columns.

Source

created based on a SpatialLIBD data (SampleID: 151673) to serve as an example

Examples

data(exampleLIBD)       #Lazy loading. Data becomes visible as soon as called

fitting data with poisson through optim function

Description

fitting data with poisson through optim function

Usage

fit_pos_optim(x, maxiter = 500)

Arguments

x

A vector of count values to be fitted

maxiter

number of iteration

Value

Returns a vector with mean paramter lambda, loglikelihood value llk, convergence

Extracted summarized metrics for reference data and synthetic data

Description

Extracted summarized metrics for reference data and synthetic data

Usage

get_metrics_pd(simsrt, metric = "GeneMean")

Arguments

simsrt

A SRTsim object

metric

Specification of metrics to be plotted.

Value

Returns a dataframe for ggplot

Summarize gene-wise summary metrics

Description

Summarize gene-wise summary metrics

Usage

get_stats_gene(mat, group, log_trans = TRUE)

Arguments

mat

A count matrix

group

A group label

log_trans

A logical constant indicating whether to log transform the gene mean and variance

Value

Returns a n by 5 dataframe with location metrics

Summarize location-wise summary metrics

Description

Summarize location-wise summary metrics

Usage

get_stats_loc(mat, group, log_trans = TRUE)

Arguments

mat

A count matrix

group

A group label

log_trans

A logical constant indicating whether to log transform the libsize

Value

Returns a n by 3 dataframe with location metrics

Access User-Specified Parameters

Description

Access User-Specified Parameters

Usage

metaParam(x)

Arguments

x

SRTsim object

Value

Returns a list of user-specified parameters

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
metaParam(toySRT)

ReSimulate Count Data with Parameters Specification from Shiny

Description

ReSimulate Count Data with Parameters Specification from Shiny

Usage

reGenCountshiny(shinyOutput, NewSeed = NULL)

Arguments

shinyOutput

A list of Shiny Output. Including a simCount, simInfo,simcountParam,simLocParam

NewSeed

A new seed for data generation. Useful when multiple replicates are needed.

Value

Returns a Count DataFrame

Examples


## Re-generate Count Data based on ShinyOutput Parameters, should be same as simCount in ShinyOutput
cMat <- reGenCountshiny(toyShiny)

## Generate Count Data with A New Seed based on ShinyOutput Parameters 
cMat2 <- reGenCountshiny(toyShiny,NewSeed=2)

## Comparison across the output
toyShiny$simCount[1:3,1:3]
cMat[1:3,1:3]
cMat2[1:3,1:3]

Access reference count matrix

Description

Access reference count matrix

Usage

refCounts(x)

Arguments

x

SRTsim object

Value

Returns a reference count matrix

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
refCounts(toySRT)[1:3,1:3]

Access reference colData

Description

Access reference colData

Usage

refcolData(x)

Arguments

x

SRTsim object

Value

Returns the colData of reference data

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
refcolData(toySRT)

Access reference rowData

Description

Access reference rowData

Usage

refrowData(x)

Arguments

x

SRTsim object

Value

Returns the rowData of reference data

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
refrowData(toySRT)

Access synthetic count matrix

Description

Access synthetic count matrix

Usage

simCounts(x)

Arguments

x

SRTsim object

Value

Returns a synthetic count matrix

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
simCounts(toySRT)[1:3,1:3]

Fit the marginal distributions for single gene

Description

Fit the marginal distributions for single gene

Usage

simNewLocs(newN, lay_out = c("grid", "random"), preLoc)

Arguments

newN

A integer specifying the number of spatial locations in the synthetic data

lay_out

A character string specifying arrangement of new generated spatial locations. Default is "grid"

preLoc

A data frame of shape n by 3 that x, y coodinates and domain label

Value

Returns a n by 2 dataframe with newly generated spatial locations

Access synthetic colData

Description

Access synthetic colData

Usage

simcolData(x)

Arguments

x

SRTsim object

Value

Returns the colData of synthetic data

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
simcolData(toySRT)

Access synthetic rowData

Description

Access synthetic rowData

Usage

simrowData(x)

Arguments

x

SRTsim object

Value

Returns the rowData of synthetic data

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)
simrowData(toySRT)

Generate Data with Cell-Cell Interaction Under Reference-Free Mode

Description

Generate Data with Cell-Cell Interaction Under Reference-Free Mode

Usage

srtsim_cci_free(
  zero_prop_in = 0,
  disper_in = Inf,
  mu_in = 1,
  numGene = 1000,
  location_in,
  region_cell_map,
  fc = 3,
  LR_in,
  sim_seed = 1,
  numKNN = 4,
  numSingleCellType = 2000
)

Arguments

zero_prop_in

A number specifying zero proportion for the count model, default is 0

disper_in

A number specifying dispersion for the count model, default is Inf. Same as the size parameter in rnbinom.

mu_in

A number specifying mean for background count model, default is 1

numGene

An integer specifying the number of genes in the synthetic data, default is 1000

location_in

A dataframe with x, y, and region_label

region_cell_map

A dataframe specifying the cell type proportion in each region. Row: region,Column: cell type.

fc

A number specifying effect size for ligand-receptor pairs that mediate the cel-cell communication, default is 3

LR_in

A dataframe specifying ligand and receptor pairs, containing four columns: protein_a, protein_b, celltypeA, and celltype B

sim_seed

A number for reproducible purpose

numKNN

A number specifying number of nearest neighbors with elevated gene expressin levels, default is 4

numSingleCellType

A number specifying number of spots in the background pool. Gene expression count are then sampled from this background pool.

Value

Returns a SRTsim object with a newly generated count matrix and correspoding parameters

Generate Data with Cell-Cell Interaction Under Reference-Based Mode

Description

Generate Data with Cell-Cell Interaction Under Reference-Based Mode

Usage

srtsim_cci_ref(
  EstParam = NULL,
  numGene = 1000,
  location_in,
  region_cell_map,
  fc = 3,
  LR_in,
  sim_seed = 1,
  numKNN = 4,
  numSingleCellType = 2000
)

Arguments

EstParam

A list of estimated parameters from srtsim_fit function, EstParam slot if the simSRT object.

numGene

An integer specifying the number of genes in the synthetic data, default is 1000

location_in

A dataframe with x, y, and region_label

region_cell_map

A dataframe specifying the cell type proportion in each region. Row: region,Column: cell type.

fc

A number specifying effect size for ligand-receptor pairs that mediate the cel-cell communication, default is 3

LR_in

A dataframe specifying ligand and receptor pairs, containing four columns: protein_a, protein_b, celltypeA, and celltype B

sim_seed

A number for reproducible purpose

numKNN

A number specifying number of nearest neighbors with elevated gene expressin levels, default is 4

numSingleCellType

A number specifying number of spots in the background pool. Gene expression count are then sampled from this background pool.

Value

Returns a SRTsim object with a newly generated count matrix and correspoding parameters

Generate Data with Estimated Parameters

Description

Generate Data with Estimated Parameters

Usage

srtsim_count(
  simsrt,
  breaktie = "random",
  total_count_new = NULL,
  total_count_old = NULL,
  rrr = NULL,
  nn_num = 5,
  nn_func = c("mean", "median", "ransam"),
  numCores = 1,
  verbose = FALSE
)

Arguments

simsrt

A object with estimated parameters from fitting step

breaktie

A character string specifying how ties are treated. Same as the "tie.method" in rank function

total_count_new

The (expected) total number of reads or UMIs in the simulated count matrix.

total_count_old

The total number of reads or UMIs in the original count matrix.

rrr

The ratio applies to the gene-specific mean estimate, used for the fixing average sequencing depth simulation. Default is null. Its specification will override the specification of total_count_new and total_count_old.

nn_num

A integer of nearest neighbors, default is 5.

nn_func

A character string specifying how the psedo-count to be generated. options include 'mean','median' and 'ransam'.

numCores

The number of cores to use

verbose

Whether to show running information for srtsim_count

Value

Returns a SRTsim object with a newly generated count matrix

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")
## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)

## Explore the synthetic count matrix
simCounts(toySRT)[1:3,1:3]

Generate Data with Estimated Parameters For A New Designed Pattern

Description

Generate Data with Estimated Parameters For A New Designed Pattern

Usage

srtsim_count_affine(
  simsrt,
  reflabel,
  targetlabel,
  breaktie = "random",
  nn_func = c("mean", "median", "ransam"),
  nn_num = 5,
  local_sid = NULL,
  numCores = 1
)

Arguments

simsrt

A SRTsim object with estimated parameters from fitting step

reflabel

A character vector specifying labels for reference regions

targetlabel

A character vector specifying labels for target regions

breaktie

A character string specifying how ties are treated. Same as the "tie.method" in rank function

nn_func

A character string specifying how the psedo-count to be generated. options include 'mean','median' and 'ransam'.

nn_num

A integer of nearest neighbors, default is 5.

local_sid

A numberic seed used locally for the affine transformation. Default is NULL.

numCores

A number of cores to use

Value

Returns a SRTsim object with a newly generated count matrix

Examples


## Prepare Data From LIBD Sample
subinfo <- exampleLIBD$info[,c("imagecol","imagerow","layer")]
colnames(subinfo) <- c("x","y","label")
gns 	<- c("ENSG00000168314","ENSG00000183036", "ENSG00000132639" )

## Create a simSRT Object with Three Genes For a Fast Example
simSRT1 <- createSRT(count_in= exampleLIBD$count[gns,],loc_in =subinfo)

## Estimate model parameters for data generation: domain-specific 
simSRT1 <- srtsim_fit(simSRT1,sim_schem="domain")

## Define New Layer Structures
simSRT1@refcolData$target_label <- "NL1"
simSRT1@refcolData$target_label[simSRT1@refcolData$label %in% paste0("Layer",4:5)] <- "NL2"
simSRT1@refcolData$target_label[simSRT1@refcolData$label %in% c("Layer6","WM")] <- "NL3"

## Perform Data Generation for New Defined Layer Structures
## Reference: WM --> NL3, Layer5--> NL2, Layer3 --> NL1
simSRT1 <- srtsim_count_affine(simSRT1,
								reflabel=c("Layer3","Layer5","WM"),
								targetlabel=c("NL1","NL2","NL3"),
								nn_func="ransam"
								)

## Visualize the Expression Pattern for Gene of Interest
visualize_gene(simsrt=simSRT1,plotgn = "ENSG00000168314",rev_y=TRUE,ptsizeCount=1)

Fit the marginal distributions for each row of a count matrix

Description

Fit the marginal distributions for each row of a count matrix

Usage

srtsim_fit(
  simsrt,
  marginal = c("auto_choose", "zinb", "nb", "poisson", "zip"),
  sim_scheme = c("tissue", "domain"),
  min_nonzero_num = 2,
  maxiter = 500
)

Arguments

simsrt

A SRTsim object

marginal

Specification of the types of marginal distribution.Default value is 'auto_choose' which chooses between ZINB, NB, ZIP, and Poisson by a likelihood ratio test (lrt),AIC and whether there is underdispersion.'zinb' will fit the ZINB model. If there is underdispersion, it will fit the Poisson model. If there is no zero at all or an error occurs, it will fit an NB model instead.'nb' fits the NB model and chooses between NB and Poisson depending on whether there is underdispersion. 'poisson' simply fits the Poisson model.'zip' fits the ZIP model and chooses between ZIP and Poisson by a likelihood ratio test

sim_scheme

a character string specifying simulation scheme. "tissue" stands for tissue-based simulation; "domain" stands for domain-specific simulation. Default is "tissue".

min_nonzero_num

The minimum number of non-zero values required for a gene to be fitted. Default is 2.

maxiter

The number of iterations for the model-fitting. Default is 500.

Value

Returns an object with estimated parameters

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")

Fit the marginal distributions for each row of a count matrix

Description

Fit the marginal distributions for each row of a count matrix

Usage

srtsim_newlocs(
  simsrt,
  new_loc_num = NULL,
  loc_lay_out = c("grid", "random"),
  voting_nn = 3
)

Arguments

simsrt

A SRTsim object

new_loc_num

A integer specifying the number of spatial locations in the synthetic data

loc_lay_out

a character string specifying arrangement of new generated spatial locations. Default is "grid"

voting_nn

A integer of nearest neighbors used in label assignment for new generated locations. Default is 3.

Value

Returns a object with estimated parameters

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)

## Create New Locations within Profile
toySRT2  <- srtsim_newlocs(toySRT,new_loc_num=1000)

## Explore New Generated Locations
simcolData(toySRT2)

Subset SRT object based on domain labels of interest

Description

Subset SRT object based on domain labels of interest

Usage

subsetSRT(simsrt, sel_label = NULL)

Arguments

simsrt

A SRTsim object

sel_label

A vector of selected domain labels used for the data generation

Value

Returns a spatialExperiment-based object

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Only Keep the Spatial Locations labelled as "A" in the reference data
subtoySRT <- subsetSRT(toySRT,sel_label = "A")

A toyExample to showcase reference-based simulations

Description

A data list containing the a gene expression matrix and a location dataframe

Usage

toyData

Format

A data list

toyCount: A sparse matrix with 100 rows and 251 columns.
toyInfo: A data frame with 251 rows and 3 columns.

Source

created based on a ST Human Breast Cancer data to serve as an example

Examples

data(toyData)       #Lazy loading. Data becomes visible as soon as called

A toyExample to showcase reference-free simulations

Description

A list of shiny output

Usage

toyShiny

Format

A list of shiny output

simCount: A data frame with 150 rows and 980 columns.
simInfo: A data frame with 980 rows and 4 columns: x, y, group, foldchange
simcountParam: A list of user-specified parameters for count generation
simLocParam: A list of user-specified parameters for pattern design

Source

created based using the SRTsim_shiny()

Examples

data(toyShiny)       #Lazy loading. Data becomes visible as soon as called

Visualize expression pattern for the gene of interest in reference data and synthetic data

Description

Visualize expression pattern for the gene of interest in reference data and synthetic data

Usage

visualize_gene(
  simsrt,
  plotgn = NULL,
  ptsizeCount = 2,
  textsizeCount = 12,
  rev_y = FALSE,
  virOption = "D",
  virDirection = -1
)

Arguments

simsrt

A SRTsim object

plotgn

A gene name selected for visualization

ptsizeCount

Specification of point size. Default is 2.

textsizeCount

Specification of axis font size. Default is 12.

rev_y

Logical indicating whether to reverse the y axis. Default is FALSE. Useful for Visualize the LIBD data.

virOption

Specification of option in the scale_color_viridis. Default is "D". User can choose a letter from 'A' to 'H'.

virDirection

Specification of direction in the scale_color_viridis. Default is "-1". User can choose '1' or '-1'.

Value

Returns two expression plots for the gene of interest

Examples


## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Create New Locations within Profile
toySRT2 <- srtsim_newlocs(toySRT,new_loc_num=1000)

## Estimate model parameters for data generation
toySRT2 <- srtsim_fit(toySRT2,sim_schem="tissue")

## Generate synthetic data with estimated parameters
toySRT2     <- srtsim_count(toySRT2,rrr=1)

## compare the expression pattern of HLA-B in synthetic data and reference data
visualize_gene(simsrt=toySRT2,plotgn = "HLA-B")

Visualize summarized metrics for reference data and synthetic data

Description

Visualize summarized metrics for reference data and synthetic data

Usage

visualize_metrics(
  simsrt,
  metric_type = c("all", "genewise", "locwise", "GeneMean", "GeneVar", "GeneCV",
    "GeneZeroProp", "LocZeroProp", "LocLibSize"),
  colorpalette = "Set3",
  axistextsize = 12
)

Arguments

simsrt

A SRTsim object

metric_type

Specification of metrics to be plotted. Default value is 'all', which will plot all six metrics: including four gene-wise metrics and two location-wise metrics. "genewise" will produce violin plots for all four gene-wise metrics; "locwise" will produce violin plots for all two location-wise metrics; "GeneMean", "GeneVar", "GeneCV", "GeneZeroProp", "LocZeroProp", and "LocLibSize" will produce single violin plot for the corresponding metric.

colorpalette

Specification of color palette to be passed to palette in the scale_fill_brewer. Default is "Set3"

axistextsize

Specification of axis font size. Default is 12.

Value

Returns a list of ggplots

Examples



## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)

## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")

## Generate synthetic data with estimated parameters
toySRT <- srtsim_count(toySRT)

## Compute metrics 
toySRT   <- compareSRT(toySRT)

## Visualize Metrics
visualize_metrics(toySRT)