Package 'scINSIGHT'

Create an scINSIGHT object.

Description

This function initializes an scINSIGHT object with normalized data passed in.

Usage

create_scINSIGHT(norm.data, condition)

Arguments

norm.data	List of normalized expression matrices (genes by cells). Gene names should be the same in all matrices.
condition	Vector specifying sample conditions.

Value

scINSIGHT object with norm.data slot set.

Examples

# Demonstration using matrices with randomly generated numbers
S1 <- matrix(runif(50000,0,2), 500,100)
S2 <- matrix(runif(60000,0,2), 500,120)
S3 <- matrix(runif(80000,0,2), 500,160)
S4 <- matrix(runif(75000,0,2), 500,150)
data = list(S1, S2, S3, S4)
sample = c("sample1", "sample2", "sample3", "sample4")
condition = c("control", "activation", "control", "activation")
names(data) = sample
names(condition) = sample
scINSIGHTx <- create_scINSIGHT(data, condition)

---

Perform scINSIGHT on normalized datasets

Description

Perform INterpreting single cell gene expresSIon bioloGically Heterogeneous daTa (scINSIGHT) to return factorized $W_{\ell1}$, $W_{\ell2}$, $H$ and $V$ matrices.

This factorization produces a $W_{\ell1}$ matrix (cells by $K_j$), a $W_{\ell2}$ matrix (cells by $K$), a shared $V$ matrix ($K$ by genes) for each sample, and a $H$ ($K_j$ by genes) matrix for each condition. $W_{\ell2}$ are the expression matrices of $K$ common gene modules for all samples, $V$ is the membership matrix of $K$ common gene modules, and it's shared by all samples. $W_{\ell1}$ are the expression matrices of $K_j$ condition-specific gene modules for all samples, and $H$ are the membership matrices of $K_j$ condition-specific gene modules for all conditions.

Usage

run_scINSIGHT(
  object,
  K = seq(5, 15, 2),
  K_j = 2,
  LDA = c(0.001, 0.01, 0.1, 1, 10),
  thre.niter = 500,
  thre.delta = 0.01,
  num.cores = 1,
  B = 5,
  out.dir = NULL,
  method = "increase"
)

Arguments

object	scINSIGHT object.
K	Number of common gene modules. (default c(5, 7, 9, 11, 13, 15))
K_j	Number of dataset-specific gene modules. (default 2)
LDA	Regularization parameters. (default c(0.001, 0.01, 0.1, 1, 10))
thre.niter	Maximum number of block coordinate descent iterations to perform. (default 500)
thre.delta	Stop iteration when the reduction of objective function is less than the threshold. (default 0.01)
num.cores	Number of cores used for optimizing factorizations in parallel (default 1).
B	Number of repeats with random seed from 1 to B. (default 5)
out.dir	Output directory of scINSIGHT results. (default NULL)
method	Method of updating the factorization (default "increase"). If provide multiple $K$, user can choose method between "increase" and "decrease". For "increase", the algorithm will first perform factorization with the least $K=K_1$. Then initialize $K_2-K_1$ facotrs, where $K_2$ is the $K$ sightly larger than $K_1$, and perform facotrization with these new facotrs. Continue this process until the largest $K$. For "increase", the algorithm will first perform factorization with the largest $K=K_1$. Then choose $K_2$ facotrs, where $K_2$ is the $K$ sightly less than $K_1$, and perform facotrization with these new facotrs. Continue this process until the least $K$.

Value

scINSIGHT object with $W_1$, $W_2$, $H$, $V$ and parameters slots set.

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The scINSIGHT Class

Description

The scINSIGHT object is created from two or more single cell datasets. To construct a scINSIGHT object, the user needs to provide at least two normalized expression (or another single-cell modality) matrices and the condition vector.

Details

The key slots used in the scINSIGHT object are described below.

Slots

norm.data

List of normalized expression matrices (genes by cells). Each matrix should have the same number and name of genes.

condition

Vector specifying each sample's condition name.

W_1

List of $W_{\ell1}$ estimated by scINSIGHT, names correspond to sample names.

W_2

List of $W_{\ell2}$ estimated by scINSIGHT, names correspond to sample names.

H

List of $H$ estimated by scINSIGHT, names correspond to condition names.

V

Matrix $V$ estimated by scINSIGHT.

norm.W_2

List of $W_{\ell2}$ after normalization. Recommended for downstream analysis.

clusters

List of cluster results.

parameters

List of selected parameters, including $K$ and $\lambda$.

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