Package 'scGPS' reference manual

Title:	A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)
Description:	The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.
Authors:	Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut]
Maintainer:	Quan Nguyen <[email protected]>
License:	GPL-3
Version:	1.19.0
Built:	2024-10-03 05:52:34 UTC
Source:	https://github.com/bioc/scGPS

add_import

Description

import packages to namespace

annotate_clusters functionally annotates the identified clusters

Description

often we need to label clusters with unique biological characters. One of the common approach to annotate a cluster is to perform functional enrichment analysis. The annotate implements ReactomePA and clusterProfiler for this analysis type in R. The function require installation of several databases as described below.

Usage

annotate_clusters(
  DEgeneList,
  pvalueCutoff = 0.05,
  gene_symbol = TRUE,
  species = "human"
)
annotate_clusters(
  DEgeneList,
  pvalueCutoff = 0.05,
  gene_symbol = TRUE,
  species = "human"
)

Arguments

`DEgeneList`	is a vector of gene symbols, convertable to ENTREZID
`pvalueCutoff`	is a numeric of the cutoff p value
`gene_symbol`	logical of whether the geneList is a gene symbol
`species`	is the selection of 'human' or 'mouse', default to 'human' genes

Value

write enrichment test output to a file and an enrichment test object for plotting

Examples

genes <-training_gene_sample
genes <-genes$Merged_unique[seq_len(50)]
enrichment_test <- annotate_clusters(genes, pvalueCutoff=0.05, 
    gene_symbol=TRUE, species = 'human')
clusterProfiler::dotplot(enrichment_test, showCategory=15)

genes <-training_gene_sample
genes <-genes$Merged_unique[seq_len(50)]
enrichment_test <- annotate_clusters(genes, pvalueCutoff=0.05, 
    gene_symbol=TRUE, species = 'human')
clusterProfiler::dotplot(enrichment_test, showCategory=15)

BootStrap runs for both scGPS training and prediction with parallel option

Description

same as bootstrap_prediction, but with an multicore option

Usage

bootstrap_parallel(
  ncores = 4,
  nboots = 1,
  genes = genes,
  mixedpop1 = mixedpop1,
  mixedpop2 = mixedpop2,
  c_selectID,
  listData = list(),
  cluster_mixedpop1 = NULL,
  cluster_mixedpop2 = NULL
)
bootstrap_parallel(
  ncores = 4,
  nboots = 1,
  genes = genes,
  mixedpop1 = mixedpop1,
  mixedpop2 = mixedpop2,
  c_selectID,
  listData = list(),
  cluster_mixedpop1 = NULL,
  cluster_mixedpop2 = NULL
)

Arguments

`ncores`	a number specifying how many cpus to be used for running
`nboots`	a number specifying how many bootstraps to be run
`genes`	a gene list to build the model
`mixedpop1`	a SingleCellExperiment object from a mixed population for training
`mixedpop2`	a SingleCellExperiment object from a target mixed population for prediction
`c_selectID`	the root cluster in mixedpop1 to becompared to clusters in mixedpop2
`listData`	a `list` object, which contains trained results for the first mixed population
`cluster_mixedpop1`	a vector of cluster assignment for mixedpop1
`cluster_mixedpop2`	a vector of cluster assignment for mixedpop2

Value

a list with prediction results written in to the index out_idx

Author(s)

Quan Nguyen, 2017-11-25

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
#prl_boots <- bootstrap_parallel(ncores = 4, nboots = 1, genes=genes,
#    mixedpop1 = mixedpop2, mixedpop2 = mixedpop2,  c_selectID=1,
#    listData =list())
#prl_boots[[1]]$ElasticNetPredict
#prl_boots[[1]]$LDAPredict

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
#prl_boots <- bootstrap_parallel(ncores = 4, nboots = 1, genes=genes,
#    mixedpop1 = mixedpop2, mixedpop2 = mixedpop2,  c_selectID=1,
#    listData =list())
#prl_boots[[1]]$ElasticNetPredict
#prl_boots[[1]]$LDAPredict

BootStrap runs for both scGPS training and prediction

Description

ElasticNet and LDA prediction for each of all the subpopulations in the new mixed population after training the model for a subpopulation in the first mixed population. The number of bootstraps to be run can be specified.

Usage

bootstrap_prediction(
  nboots = 1,
  genes = genes,
  mixedpop1 = mixedpop1,
  mixedpop2 = mixedpop2,
  c_selectID = NULL,
  listData = list(),
  cluster_mixedpop1 = NULL,
  cluster_mixedpop2 = NULL,
  trainset_ratio = 0.5,
  LDA_run = TRUE,
  verbose = FALSE,
  log_transform = FALSE
)
bootstrap_prediction(
  nboots = 1,
  genes = genes,
  mixedpop1 = mixedpop1,
  mixedpop2 = mixedpop2,
  c_selectID = NULL,
  listData = list(),
  cluster_mixedpop1 = NULL,
  cluster_mixedpop2 = NULL,
  trainset_ratio = 0.5,
  LDA_run = TRUE,
  verbose = FALSE,
  log_transform = FALSE
)

Arguments

`nboots`	a number specifying how many bootstraps to be run
`genes`	a gene list to build the model
`mixedpop1`	a SingleCellExperiment object from a mixed population for training
`mixedpop2`	a SingleCellExperiment object from a target mixed population for prediction
`c_selectID`	the root cluster in mixedpop1 to becompared to clusters in mixedpop2
`listData`	a `list` object, which contains trained results for the first mixed population
`cluster_mixedpop1`	a vector of cluster assignment for mixedpop1
`cluster_mixedpop2`	a vector of cluster assignment for mixedpop2
`trainset_ratio`	a number specifying the proportion of cells to be part of the training subpopulation
`LDA_run`	logical, if the LDA prediction is added to compare to ElasticNet
`verbose`	a logical whether to display additional messages
`log_transform`	boolean whether log transform should be computed

Value

a list with prediction results written in to the index out_idx

Author(s)

Quan Nguyen, 2017-11-25

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
c_selectID <- 2
test <- bootstrap_prediction(nboots = 1, mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, listData =list(), 
    cluster_mixedpop1 = cluster_mixedpop1, 
    cluster_mixedpop2 = cluster_mixedpop2, c_selectID = c_selectID)
names(test)
test$ElasticNetPredict
test$LDAPredict
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
cluster_mixedpop1 <- colData(mixedpop1)[,1]
cluster_mixedpop2 <- colData(mixedpop2)[,1]
c_selectID <- 2
test <- bootstrap_prediction(nboots = 1, mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, listData =list(), 
    cluster_mixedpop1 = cluster_mixedpop1, 
    cluster_mixedpop2 = cluster_mixedpop2, c_selectID = c_selectID)
names(test)
test$ElasticNetPredict
test$LDAPredict

Compute Euclidean distance matrix by rows

Description

Compute Euclidean distance matrix by rows

Usage

calcDist(x)
calcDist(x)

Arguments

`x`	A numeric matrix

Value

a distance matrix

Examples

mat_test <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
calcDist(mat_test)

mat_test <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
calcDist(mat_test)

Compute Euclidean distance matrix by rows

Description

Compute Euclidean distance matrix by rows

Usage

calcDistArma(x)
calcDistArma(x)

Arguments

`x`	A numeric matrix

Value

a distance matrix

Examples

mat_test <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(calcDistArma(mat_test), dist(mat_test), times=3)


mat_test <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(calcDistArma(mat_test), dist(mat_test), times=3)

HC clustering for a number of resolutions

Description

performs 40 clustering runs or more depending on windows

Usage

clustering(
  object = NULL,
  ngenes = 1500,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  verbose = FALSE,
  log_transform = FALSE
)
clustering(
  object = NULL,
  ngenes = 1500,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  verbose = FALSE,
  log_transform = FALSE
)

Arguments

`object`	is a SingleCellExperiment object from the train mixed population
`ngenes`	number of top variable genes to be used
`windows`	a numeric specifying the number of windows to test
`remove_outlier`	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons
`nRounds`	number of iterations to remove a selected clusters
`PCA`	logical specifying if PCA is used before calculating distance matrix
`nPCs`	number of principal components from PCA dimensional reduction to be used
`verbose`	a logical whether to display additional messages
`log_transform`	boolean whether log transform should be computed

Value

clustering results

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering(mixedpop2, remove_outlier = c(0))
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering(mixedpop2, remove_outlier = c(0))

HC clustering for a number of resolutions

Description

subsamples cells for each bagging run and performs 40 clustering runs or more depending on windows.

Usage

clustering_bagging(
  object = NULL,
  ngenes = 1500,
  bagging_run = 20,
  subsample_proportion = 0.8,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  log_transform = FALSE
)
clustering_bagging(
  object = NULL,
  ngenes = 1500,
  bagging_run = 20,
  subsample_proportion = 0.8,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  log_transform = FALSE
)

Arguments

`object`	is a SingleCellExperiment object from the train mixed population.
`ngenes`	number of genes used for clustering calculations.
`bagging_run`	an integer specifying the number of bagging runs to be computed.
`subsample_proportion`	a numeric specifying the proportion of the tree to be chosen in subsampling.
`windows`	a numeric vector specifying the rages of each window.
`remove_outlier`	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons.
`nRounds`	a integer specifying the number rounds to attempt to remove outliers.
`PCA`	logical specifying if PCA is used before calculating distance matrix.
`nPCs`	an integer specifying the number of principal components to use.
`log_transform`	boolean whether log transform should be computed

Value

a list of clustering results containing each bagging run as well as the clustering of the original tree and the tree itself.

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering_bagging(mixedpop2, remove_outlier = c(0),
    bagging_run = 2, subsample_proportion = .7)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <-clustering_bagging(mixedpop2, remove_outlier = c(0),
    bagging_run = 2, subsample_proportion = .7)

Main clustering SCORE (CORE V2.0) Stable Clustering at Optimal REsolution with bagging and bootstrapping

Description

CORE is an algorithm to generate reproduciable clustering, CORE is first implemented in ascend R package. Here, CORE V2.0 uses bagging analysis to find a stable clustering result and detect rare clusters mixed population.

Usage

CORE_bagging(
  mixedpop = NULL,
  bagging_run = 20,
  subsample_proportion = 0.8,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  ngenes = 1500,
  log_transform = FALSE
)
CORE_bagging(
  mixedpop = NULL,
  bagging_run = 20,
  subsample_proportion = 0.8,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  ngenes = 1500,
  log_transform = FALSE
)

Arguments

`mixedpop`	is a SingleCellExperiment object from the train mixed population.
`bagging_run`	an integer specifying the number of bagging runs to be computed.
`subsample_proportion`	a numeric specifying the proportion of the tree to be chosen in subsampling.
`windows`	a numeric vector specifying the ranges of each window.
`remove_outlier`	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons.
`nRounds`	an integer specifying the number rounds to attempt to remove outliers.
`PCA`	logical specifying if PCA is used before calculating distance matrix.
`nPCs`	an integer specifying the number of principal components to use.
`ngenes`	number of genes used for clustering calculations.
`log_transform`	boolean whether log transform should be computed

Value

a list with clustering results of all iterations, and a selected optimal resolution

Author(s)

Quan Nguyen, 2018-05-11

Examples

day5 <- day_5_cardio_cell_sample
cellnames<-colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <- data.frame('cluster' = cluster, 'cellBarcodes' = cellnames)
#day5$dat5_counts needs to be in a matrix format
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_bagging(mixedpop2, remove_outlier = c(0), PCA=FALSE,
    bagging_run = 2, subsample_proportion = .7)
day5 <- day_5_cardio_cell_sample
cellnames<-colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <- data.frame('cluster' = cluster, 'cellBarcodes' = cellnames)
#day5$dat5_counts needs to be in a matrix format
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_bagging(mixedpop2, remove_outlier = c(0), PCA=FALSE,
    bagging_run = 2, subsample_proportion = .7)

Main clustering CORE V2.0 updated

Description

CORE is an algorithm to generate reproduciable clustering, CORE is first implemented in ascend R package. Here, CORE V2.0 introduces several new functionalities, including three key features: fast (and more memory efficient) implementation with C++ and paralellisation options allowing clustering of hundreds of thousands of cells (ongoing development), outlier revomal important if singletons exist (done), a number of dimensionality reduction methods including the imputation implementation (CIDR) for confirming clustering results (done), and an option to select the number of optimisation tree height windows for increasing resolution

Usage

CORE_clustering(
  mixedpop = NULL,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  ngenes = 1500,
  verbose = FALSE,
  log_transform = FALSE
)
CORE_clustering(
  mixedpop = NULL,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  remove_outlier = c(0),
  nRounds = 1,
  PCA = FALSE,
  nPCs = 20,
  ngenes = 1500,
  verbose = FALSE,
  log_transform = FALSE
)

Arguments

`mixedpop`	is a SingleCellExperiment object from the train mixed population
`windows`	a numeric specifying the number of windows to test
`remove_outlier`	a vector containing IDs for clusters to be removed the default vector contains 0, as 0 is the cluster with singletons.
`nRounds`	an integer specifying the number rounds to attempt to remove outliers.
`PCA`	logical specifying if PCA is used before calculating distance matrix
`nPCs`	an integer specifying the number of principal components to use.
`ngenes`	number of genes used for clustering calculations.
`verbose`	a logical whether to display additional messages
`log_transform`	boolean whether log transform should be computed

Value

a list with clustering results of all iterations, and a selected optimal resolution

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
#day5$dat5_counts needs to be in a matrix format
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame('Cluster'=cluster, 'cellBarcodes' = cellnames)
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_clustering(mixedpop2, remove_outlier = c(0), PCA=FALSE, nPCs=20,
    ngenes=1500)
day5 <- day_5_cardio_cell_sample
#day5$dat5_counts needs to be in a matrix format
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame('Cluster'=cluster, 'cellBarcodes' = cellnames)
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_clustering(mixedpop2, remove_outlier = c(0), PCA=FALSE, nPCs=20,
    ngenes=1500)

sub_clustering (optional) after running CORE 'test'

Description

CORE_subcluster allows re-cluster the CORE clustering result

Usage

CORE_subcluster(
  mixedpop = NULL,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  select_cell_index = NULL,
  ngenes = 1500
)
CORE_subcluster(
  mixedpop = NULL,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  select_cell_index = NULL,
  ngenes = 1500
)

Arguments

`mixedpop`	is a SingleCellExperiment object from the train mixed population
`windows`	a numeric specifying the number of windows to test
`select_cell_index`	a vector containing indexes for cells in selected clusters to be reclustered
`ngenes`	number of genes used for clustering calculations.

Value

a list with clustering results of all iterations, and a selected optimal resolution

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_clustering(mixedpop2,remove_outlier= c(0))
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test <- CORE_clustering(mixedpop2,remove_outlier= c(0))

One of the two example single-cell count matrices to be used for training scGPS model

Description

The count data set contains counts for 16990 genes for 590 cells randomly subsampled from day-2 cardio-differentiation population. The vector of clustering information contains corresponding to cells in the count matrix

Usage

day_2_cardio_cell_sample
day_2_cardio_cell_sample

Format

a list instance, containing a count matrix and a vector with clustering information

Value

a list, with the name day2

Author(s)

Quan Nguyen, 2017-11-25

Source

Dr Joseph Powell's laboratory, IMB, UQ

One of the two example single-cell count matrices to be used for scGPS prediction

Description

The count data set contains counts for 17402 genes for 983 cells (1 row per gene) randomly subsampled from day-5 cardio-differentiation population The vector of clustering information contains corresponding to cells in the count matrix

Usage

day_5_cardio_cell_sample
day_5_cardio_cell_sample

Format

a list instance, containing a count matrix and a vector with clustering information.

Value

a list, with the name day5

Author(s)

Quan Nguyen, 2017-11-25

Source

Dr Joseph Powell's laboratory, IMB, UQ

Compute Distance between two vectors

Description

Compute Distance between two vectors

Usage

distvec(x, y)
distvec(x, y)

Arguments

`x`	A numeric vector
`y`	A numeric vector

Value

a numeric distance

Examples

x <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
x <- x[1,]
y <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
y <- y[1,]
distvec(x, y)

x <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
x <- x[1,]
y <-matrix(rnbinom(1000,mu=0.01, size=10),nrow=1000)
y <- y[1,]
distvec(x, y)

find marker genes

Description

Find DE genes from comparing one clust vs remaining

Usage

find_markers(
  expression_matrix = NULL,
  cluster = NULL,
  selected_cluster = NULL,
  fitType = "local",
  dispersion_method = "per-condition",
  sharing_Mode = "maximum"
)
find_markers(
  expression_matrix = NULL,
  cluster = NULL,
  selected_cluster = NULL,
  fitType = "local",
  dispersion_method = "per-condition",
  sharing_Mode = "maximum"
)

Arguments

`expression_matrix`	is a normalised expression matrix.
`cluster`	corresponding cluster information in the expression_matrix by running CORE clustering or using other methods.
`selected_cluster`	a vector of unique cluster ids to calculate
`fitType`	string specifying 'local' or 'parametric' for DEseq dispersion estimation
`dispersion_method`	one of the options c( 'pooled', 'pooled-CR', per-condition', 'blind' )
`sharing_Mode`	one of the options c("maximum", "fit-only", "gene-est-only")

Value

a list containing sorted DESeq analysis results

Author(s)

Quan Nguyen, 2017-11-25

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
# depending on the data, the DESeq::estimateDispersions function requires
# suitable fitType
# and dispersion_method options
DEgenes <- find_markers(expression_matrix=assay(mixedpop1),
                        cluster = colData(mixedpop1)[,1],
                        selected_cluster=c(1), #can also run for more
                        #than one clusters, e.g.selected_cluster = c(1,2)
                        fitType = "parametric", 
                        dispersion_method = "blind",
                        sharing_Mode="fit-only"
                        )
names(DEgenes)
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
# depending on the data, the DESeq::estimateDispersions function requires
# suitable fitType
# and dispersion_method options
DEgenes <- find_markers(expression_matrix=assay(mixedpop1),
                        cluster = colData(mixedpop1)[,1],
                        selected_cluster=c(1), #can also run for more
                        #than one clusters, e.g.selected_cluster = c(1,2)
                        fitType = "parametric", 
                        dispersion_method = "blind",
                        sharing_Mode="fit-only"
                        )
names(DEgenes)

Find the optimal cluster

Description

from calculated stability based on Rand indexes for consecutive clustering run, find the resolution (window), where the stability is the highest

Usage

find_optimal_stability(
  list_clusters,
  run_RandIdx,
  bagging = FALSE,
  windows = seq(from = 0.025, to = 1, by = 0.025)
)
find_optimal_stability(
  list_clusters,
  run_RandIdx,
  bagging = FALSE,
  windows = seq(from = 0.025, to = 1, by = 0.025)
)

Arguments

`list_clusters`	is a `list` object containing 40 clustering results
`run_RandIdx`	is a `data frame` object from iterative clustering runs
`bagging`	is a logical that is true if bagging is to be performed, changes return
`windows`	a numeric vector specifying the ranges of each window.

Value

bagging == FALSE => a list with optimal stability, cluster count and summary stats bagging == TRUE => a list with high res cluster count, optimal cluster count and keystats

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)
stab_df <- find_stability(list_clusters=cluster_all$list_clusters,
                         cluster_ref = cluster_all$cluster_ref)
optimal_stab <- find_optimal_stability(list_clusters = 
    cluster_all$list_clusters, stab_df, bagging = FALSE)

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)
stab_df <- find_stability(list_clusters=cluster_all$list_clusters,
                         cluster_ref = cluster_all$cluster_ref)
optimal_stab <- find_optimal_stability(list_clusters = 
    cluster_all$list_clusters, stab_df, bagging = FALSE)

Calculate stability index

Description

from clustering results, compare similarity between clusters by adjusted Randindex

Usage

find_stability(list_clusters = NULL, cluster_ref = NULL)
find_stability(list_clusters = NULL, cluster_ref = NULL)

Arguments

`list_clusters`	is a object from the iterative clustering runs
`cluster_ref`	is a object from the reference cluster

Value

a data frame with stability scores and rand_index results

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)
stab_df <- find_stability(list_clusters=cluster_all$list_clusters,
                         cluster_ref = cluster_all$cluster_ref)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)
stab_df <- find_stability(list_clusters=cluster_all$list_clusters,
                         cluster_ref = cluster_all$cluster_ref)

Calculate mean

Description

Calculate mean

Usage

mean_cpp(x)
mean_cpp(x)

Arguments

x

integer.

Value

a scalar value

Examples

mean_cpp(seq_len(10^6))

mean_cpp(seq_len(10^6))

new_scGPS_object

Description

new_scGPS_object generates a scGPS object in the SingleCellExperiment class for use with the scGPS package. This object contains an expression matrix, associated metadata (cells, genes, clusters). The data are expected to be normalised counts.

Usage

new_scGPS_object(
  ExpressionMatrix = NULL,
  GeneMetadata = NULL,
  CellMetadata = NULL,
  LogMatrix = NULL
)
new_scGPS_object(
  ExpressionMatrix = NULL,
  GeneMetadata = NULL,
  CellMetadata = NULL,
  LogMatrix = NULL
)

Arguments

`ExpressionMatrix`	An expression matrix in data.frame or matrix format. Rows should represent a transcript and its normalised counts, while columns should represent individual cells.
`GeneMetadata`	A data frame or vector containing gene identifiers used in the expression matrix. The first column should hold the gene identifiers you are using in the expression matrix. Other columns contain information about the genes, such as their corresponding ENSEMBL transcript identifiers.
`CellMetadata`	A data frame containing cell identifiers (usually barcodes) and an integer representing which batch they belong to. The column containing clustering information needs to be the first column in the CellMetadata dataframe If clustering information is not available, users can run CORE function and add the information to the scGPS before running scGPS prediction
`LogMatrix`	optional input for a log matrix of the data. If no log matrix is supplied one will be created for the object

Value

This function generates an scGPS object belonging to the SingleCellExperiment.

Author(s)

Quan Nguyen, 2018-04-06

Examples

day2 <- day_2_cardio_cell_sample
t <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
colData(t); show(t); colnames(t)
day2 <- day_2_cardio_cell_sample
t <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
colData(t); show(t); colnames(t)

new_summarized_scGPS_object

Description

Usage

new_summarized_scGPS_object(
  ExpressionMatrix = NULL,
  GeneMetadata = NULL,
  CellMetadata = NULL
)
new_summarized_scGPS_object(
  ExpressionMatrix = NULL,
  GeneMetadata = NULL,
  CellMetadata = NULL
)

Arguments

`ExpressionMatrix`	An expression dataset in matrix format. Rows should represent a transcript and its normalised counts, while columns should represent individual cells.
`GeneMetadata`	A data frame or vector containing gene identifiers used in the expression matrix. The first column should hold the cell identifiers you are using in the expression matrix. Other columns contain information about the genes, such as their corresponding ENSEMBL transcript identifiers.
`CellMetadata`	A data frame containing cell identifiers (usually barcodes) and clustering information (the first column of the data frame contains clustering information). The column containing clustering information needs to be named as 'Cluster'. If clustering information is not available, users can run CORE function and add the information to the scGPS before running scGPS prediction

Value

This function generates an scGPS object belonging to the SingleCellExperiment.

Author(s)

Quan Nguyen, 2017-11-25

Examples

day2 <- day_2_cardio_cell_sample
t <-new_summarized_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
colData(t); show(t); colnames(t)
day2 <- day_2_cardio_cell_sample
t <-new_summarized_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
colData(t); show(t); colnames(t)

PCA

Description

Select top variable genes and perform prcomp

Usage

PCA(expression.matrix = NULL, ngenes = 1500, scaling = TRUE, npcs = 50)
PCA(expression.matrix = NULL, ngenes = 1500, scaling = TRUE, npcs = 50)

Arguments

`expression.matrix`	An expression matrix, with genes in rows
`ngenes`	number of genes used for clustering calculations.
`scaling`	a logical of whether we want to scale the matrix
`npcs`	an integer specifying the number of principal components to use.

Value

a list containing PCA results and variance explained

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
t <-PCA(expression.matrix=assay(mixedpop1))

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
t <-PCA(expression.matrix=assay(mixedpop1))

Plot dendrogram tree for CORE result

Description

This function plots CORE and all clustering results underneath

Usage

plot_CORE(original.tree, list_clusters = NULL, color_branch = NULL)
plot_CORE(original.tree, list_clusters = NULL, color_branch = NULL)

Arguments

`original.tree`	the original dendrogram before clustering
`list_clusters`	a list containing clustering results for each of the
`color_branch`	is a vector containing user-specified colors (the number of unique colors should be equal or larger than the number of clusters). This parameter allows better selection of colors for the display.

Value

a plot with clustering bars underneath the tree

Examples

day5 <- day_5_cardio_cell_sample
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame('Cluster'=cluster, 'cellBarcodes' = cellnames)
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
    GeneMetadata = day5$dat5geneInfo, CellMetadata = cellnames)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
plot_CORE(CORE_cluster$tree, CORE_cluster$Cluster)
day5 <- day_5_cardio_cell_sample
cellnames <- colnames(day5$dat5_counts)
cluster <-day5$dat5_clusters
cellnames <-data.frame('Cluster'=cluster, 'cellBarcodes' = cellnames)
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
    GeneMetadata = day5$dat5geneInfo, CellMetadata = cellnames)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
plot_CORE(CORE_cluster$tree, CORE_cluster$Cluster)

plot one single tree with the optimal clustering result

Description

after an optimal cluster has been identified, users may use this function to plot the resulting dendrogram with the branch colors represent clutering results

Usage

plot_optimal_CORE(
  original_tree,
  optimal_cluster = NULL,
  shift = -100,
  values = NULL
)
plot_optimal_CORE(
  original_tree,
  optimal_cluster = NULL,
  shift = -100,
  values = NULL
)

Arguments

`original_tree`	a dendrogram object
`optimal_cluster`	a vector of cluster IDs for cells in the dendrogram
`shift`	a numer specifying the gap between the dendrogram and the colored
`values`	a vector containing color values of the branches and the colored bar underneath the tree bar underneath the dendrogram. This parameter allows better selection of colors for the display.

Value

a plot with colored braches and bars for the optimal clustering result

Author(s)

Quan Nguyen, 2017-11-25

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
key_height <- CORE_cluster$optimalClust$KeyStats$Height
optimal_res <- CORE_cluster$optimalClust$OptimalRes
optimal_index = which(key_height == optimal_res)
plot_optimal_CORE(original_tree= CORE_cluster$tree, 
    optimal_cluster = unlist(CORE_cluster$Cluster[optimal_index]),
    shift = -2000)

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
CORE_cluster <- CORE_clustering(mixedpop2, remove_outlier = c(0))
key_height <- CORE_cluster$optimalClust$KeyStats$Height
optimal_res <- CORE_cluster$optimalClust$OptimalRes
optimal_index = which(key_height == optimal_res)
plot_optimal_CORE(original_tree= CORE_cluster$tree, 
    optimal_cluster = unlist(CORE_cluster$Cluster[optimal_index]),
    shift = -2000)

plot reduced data

Description

plot PCA, tSNE, and CIDR reduced datasets

Usage

plot_reduced(
  reduced_dat,
  color_fac = NULL,
  dims = c(1, 2),
  dimNames = c("Dim1", "Dim2"),
  palletes = NULL,
  legend_title = "Cluster"
)
plot_reduced(
  reduced_dat,
  color_fac = NULL,
  dims = c(1, 2),
  dimNames = c("Dim1", "Dim2"),
  palletes = NULL,
  legend_title = "Cluster"
)

Arguments

`reduced_dat`	is a matrix with genes in rows and cells in columns
`color_fac`	is a vector of colors corresponding to clusters to determine colors of scattered plots
`dims`	an integer of the number of dimestions
`dimNames`	a vector of the names of the dimensions
`palletes`	can be a customised color pallete that determine colors for density plots, if NULL it will use RColorBrewer colorRampPalette(RColorBrewer::brewer.pal(sample_num, 'Set1'))(sample_num)
`legend_title`	title of the plot's legend

Value

a matrix with the top 20 CIDR dimensions

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
#CIDR_dim <-CIDR(expression.matrix=assay(mixedpop1))
#p <- plot_reduced(CIDR_dim, color_fac = factor(colData(mixedpop1)[,1]),
#     palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
#plot(p)
tSNE_dim <-tSNE(expression.mat=assay(mixedpop1))
p2 <- plot_reduced(tSNE_dim, color_fac = factor(colData(mixedpop1)[,1]),
    palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
plot(p2)

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
#CIDR_dim <-CIDR(expression.matrix=assay(mixedpop1))
#p <- plot_reduced(CIDR_dim, color_fac = factor(colData(mixedpop1)[,1]),
#     palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
#plot(p)
tSNE_dim <-tSNE(expression.mat=assay(mixedpop1))
p2 <- plot_reduced(tSNE_dim, color_fac = factor(colData(mixedpop1)[,1]),
    palletes = seq_len(length(unique(colData(mixedpop1)[,1]))))
plot(p2)

Main prediction function applying the optimal ElasticNet and LDA models

Description

Predict a new mixed population after training the model for a subpopulation in the first mixed population. All subpopulations in the new target mixed population will be predicted, where each targeted subpopulation will have a transition score from the orginal subpopulation to the new subpopulation.

Usage

predicting(
  listData = NULL,
  cluster_mixedpop2 = NULL,
  mixedpop2 = NULL,
  out_idx = NULL,
  standardize = TRUE,
  LDA_run = FALSE,
  c_selectID = NULL,
  log_transform = FALSE
)
predicting(
  listData = NULL,
  cluster_mixedpop2 = NULL,
  mixedpop2 = NULL,
  out_idx = NULL,
  standardize = TRUE,
  LDA_run = FALSE,
  c_selectID = NULL,
  log_transform = FALSE
)

Arguments

`listData`	a `list` object containing trained results for the selected subpopulation in the first mixed population
`cluster_mixedpop2`	a vector of cluster assignment for mixedpop2
`mixedpop2`	a SingleCellExperiment object from the target mixed population of importance, e.g. differentially expressed genes that are most significant
`out_idx`	a number to specify index to write results into the list output. This is needed for running bootstrap.
`standardize`	a logical of whether to standardize the data
`LDA_run`	logical, if the LDA prediction is added to compare to ElasticNet, the LDA model needs to be trained from the training before inputting to this prediction step
`c_selectID`	a number to specify the trained cluster used for prediction
`log_transform`	boolean whether log transform should be computed

Value

a list with prediction results written in to the index out_idx

Author(s)

Quan Nguyen, 2017-11-25

Examples

c_selectID<-1
out_idx<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
listData  <- training(genes, 
    cluster_mixedpop1 = colData(mixedpop1)[, 1], mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, c_selectID, listData =list(), out_idx=out_idx)
listData  <- predicting(listData =listData,  mixedpop2 = mixedpop2, 
    out_idx=out_idx, cluster_mixedpop2 = colData(mixedpop2)[, 1], 
    c_selectID = c_selectID)

c_selectID<-1
out_idx<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
listData  <- training(genes, 
    cluster_mixedpop1 = colData(mixedpop1)[, 1], mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, c_selectID, listData =list(), out_idx=out_idx)
listData  <- predicting(listData =listData,  mixedpop2 = mixedpop2, 
    out_idx=out_idx, cluster_mixedpop2 = colData(mixedpop2)[, 1], 
    c_selectID = c_selectID)

Principal component analysis

Description

This function provides significant speed gain if the input matrix is big

Usage

PrinComp_cpp(X)
PrinComp_cpp(X)

Arguments

`X`	an R matrix (expression matrix), rows are genes, columns are cells

Value

a list with three list pca lists

Examples

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(PrinComp_cpp(mat_test), prcomp(mat_test), times=3)

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(PrinComp_cpp(mat_test), prcomp(mat_test), times=3)

Calculate rand index

Description

Comparing clustering results Function for calculating randindex (adapted from the function by Steve Horvath and Luohua Jiang, UCLA, 2003)

Usage

rand_index(tab, adjust = TRUE)
rand_index(tab, adjust = TRUE)

Arguments

`tab`	a table containing different clustering results in rows
`adjust`	a logical of whether to use the adjusted rand index

Value

a rand_index value

Author(s)

Quan Nguyen and Michael Thompson, 2018-05-11

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)

rand_index(table(unlist(cluster_all$list_clusters[[1]]), 
cluster_all$cluster_ref))

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
cluster_all <-clustering(object=mixedpop2)

rand_index(table(unlist(cluster_all$list_clusters[[1]]), 
cluster_all$cluster_ref))

Function to calculate Eucledean distance matrix without parallelisation

Description

Function to calculate Eucledean distance matrix without parallelisation

Usage

rcpp_Eucl_distance_NotPar(mat)
rcpp_Eucl_distance_NotPar(mat)

Arguments

mat

an R matrix (expression matrix), with cells in rows and genes in columns

Value

a distance matrix

Examples

mat_test <-matrix(rnbinom(100000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(rcpp_Eucl_distance_NotPar(mat_test), dist(mat_test), times=3)

mat_test <-matrix(rnbinom(100000,mu=0.01, size=10),nrow=1000)
#library(microbenchmark)
#microbenchmark(rcpp_Eucl_distance_NotPar(mat_test), dist(mat_test), times=3)

distance matrix using C++

Description

This function provides fast and memory efficient distance matrix calculation

Usage

rcpp_parallel_distance(mat)
rcpp_parallel_distance(mat)

Arguments

mat

an R matrix (expression matrix), rows are genes, columns are cells

Value

a distance matrix

Examples

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=10000)
#library(microbenchmark)
#microbenchmark(rcpp_parallel_distance(mat_test), dist(mat_test), times=3)

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=10000)
#library(microbenchmark)
#microbenchmark(rcpp_parallel_distance(mat_test), dist(mat_test), times=3)

summarise bootstrap runs for Lasso model, from `n` bootstraps

Description

the training and prediction results from bootstrap were written to the object LSOLDA_dat, the reformat_LASSO summarises prediction for n bootstrap runs

Usage

reformat_LASSO(
  c_selectID = NULL,
  mp_selectID = NULL,
  LSOLDA_dat = NULL,
  nPredSubpop = NULL,
  Nodes_group = "#7570b3",
  nboots = 2
)
reformat_LASSO(
  c_selectID = NULL,
  mp_selectID = NULL,
  LSOLDA_dat = NULL,
  nPredSubpop = NULL,
  Nodes_group = "#7570b3",
  nboots = 2
)

Arguments

`c_selectID`	is the original cluster to be projected
`mp_selectID`	is the target mixedpop to project to
`LSOLDA_dat`	is the results from the bootstrap
`nPredSubpop`	is the number of clusters in the target mixedpop `row_cluster <-length(unique(target_cluster))`
`Nodes_group`	string representation of hexidecimal color code for node
`nboots`	is an integer for how many bootstraps are run

Value

a dataframe containg information for the Lasso prediction results, each column contains prediction results for all subpopulations from each bootstrap run

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 2, mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
reformat_LASSO(LSOLDA_dat=LSOLDA_dat, 
    nPredSubpop=length(unique(colData(mixedpop2)[,1])), c_selectID = 1, 
    mp_selectID =2, nboots = 2)

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 2, mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
reformat_LASSO(LSOLDA_dat=LSOLDA_dat, 
    nPredSubpop=length(unique(colData(mixedpop2)[,1])), c_selectID = 1, 
    mp_selectID =2, nboots = 2)

sub_clustering for selected cells

Description

performs 40 clustering runs or more depending on windows

Usage

sub_clustering(
  object = NULL,
  ngenes = 1500,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  select_cell_index = NULL
)
sub_clustering(
  object = NULL,
  ngenes = 1500,
  windows = seq(from = 0.025, to = 1, by = 0.025),
  select_cell_index = NULL
)

Arguments

`object`	is a SingleCellExperiment object from the train mixed population
`ngenes`	number of genes used for clustering calculations.
`windows`	a numeric vector specifying the ranges of each window.
`select_cell_index`	a vector containing indexes for cells in selected clusters to be reclustered

Value

clustering results

Author(s)

Quan Nguyen, 2018-01-31

Examples

day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test_sub_clustering <-sub_clustering(mixedpop2,
    select_cell_index = c(seq_len(100)))
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_summarized_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
test_sub_clustering <-sub_clustering(mixedpop2,
    select_cell_index = c(seq_len(100)))

Subset a matrix

Description

Subset a matrix

Usage

subset_cpp(m1in, rowidx_in, colidx_in)
subset_cpp(m1in, rowidx_in, colidx_in)

Arguments

`m1in`	an R matrix (expression matrix)
`rowidx_in`	a numeric vector of rows to keep
`colidx_in`	a numeric vector of columns to keep

Value

a subsetted matrix

Examples

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=100)
subset_mat <- subset_cpp(mat_test, rowidx_in=c(1:10), colidx_in=c(100:500))
dim(subset_mat)
mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=100)
subset_mat <- subset_cpp(mat_test, rowidx_in=c(1:10), colidx_in=c(100:500))
dim(subset_mat)

get percent accuracy for Lasso model, from `n` bootstraps

Description

The training results from training were written to

Usage

summary_accuracy(object = NULL)
summary_accuracy(object = NULL)

Arguments

object

is a list containing the training results from the summary_accuracy summarise n bootstraps

Value

a vector of percent accuracy for the selected subpopulation

Author(s)

Quan Nguyen, 2017-11-25

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_accuracy(LSOLDA_dat)
summary_deviance(LSOLDA_dat)
c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_accuracy(LSOLDA_dat)
summary_deviance(LSOLDA_dat)

get percent deviance explained for Lasso model, from `n` bootstraps

Description

the training results from training were written to the object LSOLDA_dat, the summary_devidance summarises deviance explained for n bootstrap runs and also returns the best deviance matrix for plotting, as well as the best matrix with Lasso genes and coefficients

Usage

summary_deviance(object = NULL)
summary_deviance(object = NULL)

Arguments

object

is a list containing the training results from training

Value

a list containing three elements, with a vector of percent maximum deviance explained, a dataframe containg information for the full deviance, and a dataframe containing gene names and coefficients of the best model

Author(s)

Quan Nguyen, 2017-11-25

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo,
                    CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_deviance(LSOLDA_dat)
c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo,
                    CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_deviance(LSOLDA_dat)

get percent deviance explained for Lasso model, from `n` bootstraps

Description

the training results from training were written to the object LSOLDA_dat, the summary_prediction summarises prediction for n bootstrap runs

Usage

summary_prediction_lasso(LSOLDA_dat = NULL, nPredSubpop = NULL)
summary_prediction_lasso(LSOLDA_dat = NULL, nPredSubpop = NULL)

Arguments

`LSOLDA_dat`	is a list containing the training results from `training`
`nPredSubpop`	is the number of subpopulations in the target mixed population

Value

a dataframe containg information for the Lasso prediction results, each column contains prediction results for all subpopulations from each bootstrap run

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_prediction_lasso(LSOLDA_dat=LSOLDA_dat, nPredSubpop=4)

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_prediction_lasso(LSOLDA_dat=LSOLDA_dat, nPredSubpop=4)

get percent deviance explained for LDA model, from `n` bootstraps

Description

the training results from training were written to the object LSOLDA_dat, the summary_prediction summarises prediction explained for n bootstrap runs and also returns the best deviance matrix for plotting, as well as the best matrix with Lasso genes and coefficients

Usage

summary_prediction_lda(LSOLDA_dat = NULL, nPredSubpop = NULL)
summary_prediction_lda(LSOLDA_dat = NULL, nPredSubpop = NULL)

Arguments

`LSOLDA_dat`	is a list containing the training results from `training`
`nPredSubpop`	is the number of subpopulations in the target mixed population

Value

a dataframe containg information for the LDA prediction results, each column contains prediction results for all subpopulations from each bootstrap run

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_prediction_lda(LSOLDA_dat=LSOLDA_dat, nPredSubpop=4)

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 1,mixedpop1 = mixedpop1, 
mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
summary_prediction_lda(LSOLDA_dat=LSOLDA_dat, nPredSubpop=4)

select top variable genes

Description

subset a matrix by top variable genes

Usage

top_var(expression.matrix = NULL, ngenes = 1500)
top_var(expression.matrix = NULL, ngenes = 1500)

Arguments

`expression.matrix`	is a matrix with genes in rows and cells in columns
`ngenes`	number of genes used for clustering calculations.

Value

a subsetted expression matrix with the top n most variable genes

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
SortedExprsMat <-top_var(expression.matrix=assay(mixedpop1))
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
SortedExprsMat <-top_var(expression.matrix=assay(mixedpop1))

Transpose a matrix

Description

Transpose a matrix

Usage

tp_cpp(X)
tp_cpp(X)

Arguments

`X`	an R matrix (expression matrix)

Value

a transposed matrix

Examples

mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=100)
tp_mat <- tp_cpp(mat_test)
mat_test <-matrix(rnbinom(1000000,mu=0.01, size=10),nrow=100)
tp_mat <- tp_cpp(mat_test)

Main model training function for finding the best model that characterises a subpopulation

Description

Training a haft of all cells to find optimal ElasticNet and LDA models to predict a subpopulation

Usage

training(
  genes = NULL,
  cluster_mixedpop1 = NULL,
  mixedpop1 = NULL,
  mixedpop2 = NULL,
  c_selectID = NULL,
  listData = list(),
  out_idx = 1,
  standardize = TRUE,
  trainset_ratio = 0.5,
  LDA_run = FALSE,
  log_transform = FALSE
)
training(
  genes = NULL,
  cluster_mixedpop1 = NULL,
  mixedpop1 = NULL,
  mixedpop2 = NULL,
  c_selectID = NULL,
  listData = list(),
  out_idx = 1,
  standardize = TRUE,
  trainset_ratio = 0.5,
  LDA_run = FALSE,
  log_transform = FALSE
)

Arguments

`genes`	a vector of gene names (for ElasticNet shrinkage); gene symbols must be in the same format with gene names in subpop2. Note that genes are listed by the order of importance, e.g. differentially expressed genes that are most significan, so that if the gene list contains too many genes, only the top 500 genes are used.
`cluster_mixedpop1`	a vector of cluster assignment in mixedpop1
`mixedpop1`	is a SingleCellExperiment object from the train mixed population
`mixedpop2`	is a SingleCellExperiment object from the target mixed population
`c_selectID`	a selected number to specify which subpopulation to be used for training
`listData`	list to store output in
`out_idx`	a number to specify index to write results into the list output. This is needed for running bootstrap.
`standardize`	a logical value specifying whether or not to standardize the train matrix
`trainset_ratio`	a number specifying the proportion of cells to be part of the training subpopulation
`LDA_run`	logical, if the LDA run is added to compare to ElasticNet
`log_transform`	boolean whether log transform should be computed

Value

a list with prediction results written in to the indexed out_idx

Author(s)

Quan Nguyen, 2017-11-25

Examples


c_selectID<-1
out_idx<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
listData  <- training(genes, 
    cluster_mixedpop1 = colData(mixedpop1)[, 1],
    mixedpop1 = mixedpop1, mixedpop2 = mixedpop2, c_selectID,
    listData =list(), out_idx=out_idx, trainset_ratio = 0.5)
names(listData)
listData$Accuracy
c_selectID<-1
out_idx<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts,
GeneMetadata = day5$dat5geneInfo, CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
listData  <- training(genes, 
    cluster_mixedpop1 = colData(mixedpop1)[, 1],
    mixedpop1 = mixedpop1, mixedpop2 = mixedpop2, c_selectID,
    listData =list(), out_idx=out_idx, trainset_ratio = 0.5)
names(listData)
listData$Accuracy

Input gene list for training scGPS, e.g. differentially expressed genes

Description

Genes should be ordered from most to least important genes (1 row per gene)

Usage

training_gene_sample
training_gene_sample

Format

a list instance, containing a count matrix and a vector with clustering information.

Value

a vector containing gene symbols

Author(s)

Quan Nguyen, 2017-11-25

Source

Dr Joseph Powell's laboratory, IMB, UQ

tSNE

Description

calculate tSNE from top variable genes

Usage

tSNE(
  expression.mat = NULL,
  topgenes = 1500,
  scale = TRUE,
  thet = 0.5,
  perp = 30
)
tSNE(
  expression.mat = NULL,
  topgenes = 1500,
  scale = TRUE,
  thet = 0.5,
  perp = 30
)

Arguments

`expression.mat`	An expression matrix, with genes in rows
`topgenes`	number of genes used for clustering calculations.
`scale`	a logical of whether we want to scale the matrix
`thet`	numeric; Speed/accuracy trade-off (increase for less accuracy)
`perp`	numeric; Perplexity parameter (should not be bigger than 3 * perplexity < nrow(X) - 1, see details for interpretation)

Value

a tSNE reduced matrix containing three tSNE dimensions

Examples

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
t <-tSNE(expression.mat = assay(mixedpop1))

day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
t <-tSNE(expression.mat = assay(mixedpop1))

Calculate variance

Description

Calculate variance

Usage

var_cpp(x, bias = TRUE)
var_cpp(x, bias = TRUE)

Arguments

`x`	a vector of gene expression.
`bias`	degree of freedom

Value

a variance value

Examples

var_cpp(seq_len(10^6))

var_cpp(seq_len(10^6))

Package 'scGPS'

Help Index

add_import

Description

annotate_clusters functionally annotates the identified clusters

Description

Usage

Arguments

Value

Examples

BootStrap runs for both scGPS training and prediction with parallel option

Description

Usage

Arguments

Value

Author(s)

Examples

BootStrap runs for both scGPS training and prediction

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Compute Euclidean distance matrix by rows

Description

Usage

Arguments

Value

Examples

Compute Euclidean distance matrix by rows

Description

Usage

Arguments

Value

Examples

HC clustering for a number of resolutions

Description

Usage

Arguments

Value

Author(s)

Examples

HC clustering for a number of resolutions

Description

Usage

Arguments

Value

Author(s)

Examples

Main clustering SCORE (CORE V2.0) Stable Clustering at Optimal REsolution with bagging and bootstrapping

Description

Usage

Arguments

Value

Author(s)

Examples

Main clustering CORE V2.0 updated

Description

Usage

Arguments

Value

Author(s)

Examples

sub_clustering (optional) after running CORE 'test'

Description

Usage

Arguments

Value

Author(s)

Examples

One of the two example single-cell count matrices to be used for training scGPS model

Description

Usage

Format

Value

Author(s)

Source

One of the two example single-cell count matrices to be used for scGPS prediction