| scTenifoldNet {scTenifoldNet} | R Documentation |
scTenifoldNet
Description
Construct and compare single-cell gene regulatory networks (scGRNs) using single-cell RNA-seq (scRNA-seq) data sets collected from different conditions based on principal component regression, tensor decomposition, and manifold alignment.
Usage
scTenifoldNet(
X,
Y,
qc = TRUE,
qc_minLibSize = 1000,
qc_removeOutlierCells = TRUE,
qc_minPCT = 0.05,
qc_maxMTratio = 0.1,
nc_nNet = 10,
nc_nCells = 500,
nc_nComp = 3,
nc_symmetric = FALSE,
nc_scaleScores = TRUE,
nc_q = 0.05,
td_K = 3,
td_nDecimal = 1,
td_maxIter = 1000,
td_maxError = 1e-05,
ma_nDim = 30,
nCores = parallel::detectCores()
)
Arguments
X |
Raw counts matrix with cells as columns and genes (symbols) as rows. |
Y |
Raw counts matrix with cells as columns and genes (symbols) as rows. |
qc |
A boolean value (TRUE/FALSE), if TRUE, a quality control is applied over the data. |
qc_minLibSize |
An integer value. Defines the minimum library size required for a cell to be included in the analysis. |
qc_removeOutlierCells |
A boolean value (TRUE/FALSE), if TRUE, the identified cells with library size greater than 1.58 IQR/sqrt(n) computed from the sample, are removed. For further details see: |
qc_minPCT |
A decimal value between 0 and 1. Defines the minimum fraction of cells where the gene needs to be expressed to be included in the analysis. |
qc_maxMTratio |
A decimal value between 0 and 1. Defines the maximum ratio of mitochondrial reads (mithocondrial reads / library size) present in a cell to be included in the analysis. It's computed using the symbol genes starting with 'MT-' non-case sensitive. |
nc_nNet |
An integer value. The number of networks based on principal components regression to generate. |
nc_nCells |
An integer value. The number of cells to subsample each time to generate a network. |
nc_nComp |
An integer value. The number of principal components in PCA to generate the networks. Should be greater than 2 and lower than the total number of genes. |
nc_symmetric |
A boolean value (TRUE/FALSE), if TRUE, the weights matrix returned will be symmetric. |
nc_scaleScores |
A boolean value (TRUE/FALSE), if TRUE, the weights will be normalized such that the maximum absolute value is 1. |
nc_q |
A decimal value between 0 and 1. Defines the cut-off threshold of top q% relationships to be returned. |
td_K |
An integer value. Defines the number of rank-one tensors used to approximate the data using CANDECOMP/PARAFAC (CP) Tensor Decomposition. |
td_nDecimal |
An integer value indicating the number of decimal places to be used. |
td_maxIter |
An integer value. Defines the maximum number of iterations if error stay above |
td_maxError |
A decimal value between 0 and 1. Defines the relative Frobenius norm error tolerance. |
ma_nDim |
An integer value. Defines the number of dimensions of the low-dimensional feature space to be returned from the non-linear manifold alignment. |
nCores |
An integer value. Defines the number of cores to be used. |
Value
A list with 3 slots as follows:
tensorNetworks: The generated weight-averaged denoised gene regulatory networks using CANDECOMP/PARAFAC (CP) Tensor Decomposition.
manifoldAlignment: The generated low-dimensional features result of the non-linear manifold alignment.
diffRegulation The results of the differential regulation analysis.
Examples
library(scTenifoldNet)
# Simulating of a dataset following a negative binomial distribution with high sparcity (~67%)
nCells = 2000
nGenes = 100
set.seed(1)
X <- rnbinom(n = nGenes * nCells, size = 20, prob = 0.98)
X <- round(X)
X <- matrix(X, ncol = nCells)
rownames(X) <- c(paste0('ng', 1:90), paste0('mt-', 1:10))
# Generating a perturbed network modifying the expression of genes 10, 2 and 3
Y <- X
Y[10,] <- Y[50,]
Y[2,] <- Y[11,]
Y[3,] <- Y[5,]
## Not run:
# scTenifoldNet
Output <- scTenifoldNet(X = X, Y = Y,
nc_nNet = 10, nc_nCells = 500,
td_K = 3, qc_minLibSize = 30)
# Structure of the output
str(Output)
# Accessing the computed weight-averaged denoised gene regulatory networks
# Network for sample X
igraph::graph_from_adjacency_matrix(adjmatrix = Output$tensorNetworks$X, weighted = TRUE)
# IGRAPH 15cbeea DNW- 100 2836 --
# + attr: name (v/c), weight (e/n)
# + edges from 15cbeea (vertex names):
# [1] ng6 ->ng1 ng12->ng1 ng14->ng1 ng24->ng1 ng28->ng1
# [6] ng31->ng1 ng42->ng1 ng44->ng1 ng49->ng1 ng55->ng1
# [11] ng56->ng1 ng59->ng1 ng62->ng1 ng63->ng1 ng72->ng1
# [16] ng73->ng1 ng74->ng1 ng77->ng1 ng80->ng1 ng82->ng1
# [21] ng83->ng1 ng87->ng1 ng89->ng1 mt-1->ng1 mt-5->ng1
# [26] mt-7->ng1 ng27->ng3 ng28->ng3 ng31->ng3 ng32->ng3
# [31] ng44->ng3 ng59->ng3 ng62->ng3 ng72->ng3 ng73->ng3
# [36] ng74->ng3 ng77->ng3 ng82->ng3 ng87->ng3 ng89->ng3
# + ... omitted several edges
# Network for sample Y
igraph::graph_from_adjacency_matrix(adjmatrix = Output$tensorNetworks$Y, weighted = TRUE)
#IGRAPH 3ad1533 DNW- 100 725 --
# + attr: name (v/c), weight (e/n)
# + edges from 3ad1533 (vertex names):
# [1] ng2 ->ng2 ng3 ->ng2 ng5 ->ng2 ng6 ->ng2
# [5] ng7 ->ng2 ng8 ->ng2 ng9 ->ng2 ng10->ng2
# [9] ng11->ng2 ng12->ng2 ng13->ng2 ng15->ng2
# [13] ng16->ng2 ng17->ng2 ng18->ng2 ng20->ng2
# [17] ng21->ng2 ng22->ng2 ng23->ng2 ng24->ng2
# [21] ng25->ng2 ng26->ng2 ng28->ng2 ng29->ng2
# [25] ng30->ng2 ng31->ng2 ng33->ng2 ng34->ng2
# [29] ng35->ng2 ng36->ng2 ng38->ng2 ng39->ng2
# + ... omitted several edges
# Accessing the manifold alignment result
head(Output$manifoldAlignment)
# NLMA 1 NLMA 2 NLMA 3 NLMA 4 NLMA 5
# X_ng1 0.0068499391 0.01096706 0.03077900 0.002655469 -0.0136455614
# X_ng2 0.3356288575 -0.03551752 -0.18463680 -0.193353751 0.3398606363
# X_ng3 -0.1285177133 -0.20064344 0.20926567 0.059542294 -0.0099528441
# X_ng4 0.0029881645 -0.01267593 0.01195683 0.007331123 0.0003031888
# X_ng5 -0.1192632208 -0.18475439 0.27616148 0.112944009 -0.0281827702
# X_ng6 0.0005911568 0.02557475 0.07527792 -0.191180647 -0.1165095115
# NLMA 6 NLMA 7 NLMA 8 NLMA 9 NLMA 10
# X_ng1 -0.029852128 0.007539925 0.009299591 -0.009813157 -0.01360414
# X_ng2 -0.313361443 0.146429589 0.006286777 0.162023788 -0.04307899
# X_ng3 -0.008733285 0.172084611 0.508056218 0.199322512 -0.07935797
# X_ng4 -0.004680652 0.005344541 0.002634755 -0.003376544 -0.01100757
# X_ng5 -0.126328797 0.190769152 -0.468107666 0.170278281 -0.06744795
# X_ng6 -0.051266264 0.063822269 0.011060924 -0.134880459 -0.02579998
# NLMA 11 NLMA 12 NLMA 13 NLMA 14 NLMA 15
# X_ng1 -0.0199528840 0.008035130 0.004631187 0.000807797 0.011960838
# X_ng2 -0.0138200390 -0.002847701 -0.004404942 0.008024704 0.006040799
# X_ng3 0.0232384468 -0.031398116 -0.007026934 0.028956700 -0.002112626
# X_ng4 0.0012864539 -0.018915289 0.003835404 0.004054159 -0.002546324
# X_ng5 0.0232899093 -0.040974531 -0.006759459 0.025415953 -0.007518957
# X_ng6 -0.0001650355 0.023277338 0.006646904 -0.002683418 -0.112688129
# NLMA 16 NLMA 17 NLMA 18 NLMA 19 NLMA 20
# X_ng1 -0.016962988 -0.016649748 0.01140020 -0.006632691 -0.0005015655
# X_ng2 0.007543775 -0.016188689 0.02517684 0.014814415 0.0162617154
# X_ng3 -0.005598267 -0.006975026 0.05218029 0.006731063 0.0183436415
# X_ng4 0.003207934 -0.001784120 0.01093237 -0.001192860 0.0028746990
# X_ng5 -0.009555879 -0.007429166 0.05206441 0.006534604 0.0170071357
# X_ng6 -0.065437425 0.110728870 -0.12746932 0.335610531 0.1341842827
# NLMA 21 NLMA 22 NLMA 23 NLMA 24 NLMA 25
# X_ng1 0.003113385 -0.023311350 -0.026415944 7.085995e-04 0.053898102
# X_ng2 0.001390569 0.001191301 -0.015621435 2.359703e-03 -0.013418093
# X_ng3 -0.007483171 0.011496519 0.004164546 2.764407e-02 -0.004527981
# X_ng4 0.020316634 -0.002796092 0.032119363 4.203867e-05 -0.002251366
# X_ng5 -0.004963436 0.016525449 0.009683698 2.564700e-02 0.002286340
# X_ng6 0.229199525 0.340639745 -0.041216345 3.599596e-03 0.008572652
# NLMA 26 NLMA 27 NLMA 28 NLMA 29 NLMA 30
# X_ng1 0.065832029 -0.0080248854 0.107300843 -0.02902323 -0.005337500
# X_ng2 -0.007982259 -0.0026295392 -0.001765851 0.01491257 -0.003546343
# X_ng3 0.009770602 0.0008819272 0.014564070 -0.01568192 -0.017450667
# X_ng4 0.015802609 0.0012975576 -0.003406675 -0.01774975 -0.003300053
# X_ng5 0.003401007 0.0001761177 0.013622016 -0.01224127 -0.013909178
# X_ng6 -0.089450710 -0.0763838722 -0.107751916 -0.05841353 -0.059217012
# Differential Regulation
head(Output$diffRegulation,n = 10)
# gene distance Z FC p.value p.adj
# 2 ng2 0.023526702 2.762449 12.193413 0.0004795855 0.02414332
# 50 ng50 0.023514429 2.761550 12.180695 0.0004828665 0.02414332
# 11 ng11 0.022443941 2.681598 11.096894 0.0008647241 0.02882414
# 3 ng3 0.020263415 2.508478 9.045415 0.0026335445 0.06583861
# 10 ng10 0.019194561 2.417929 8.116328 0.0043868326 0.07711821
# 5 ng5 0.019079975 2.407977 8.019712 0.0046270923 0.07711821
# 31 ng31 0.013632541 1.865506 4.094085 0.0430335257 0.61476465
# 96 mt-6 0.011401177 1.589757 2.863536 0.0906081350 0.90977795
# 59 ng59 0.009835354 1.368238 2.130999 0.1443466682 0.90977795
# 62 ng62 0.007995812 1.067193 1.408408 0.2353209153 0.90977795
# Plotting
# Genes with FDR < 0.1 are labeled as red
set.seed(1)
qChisq <- rchisq(100,1)
geneColor <- rev(ifelse(Output$diffRegulation$p.adj < 0.1, 10,1))
qqplot(qChisq, Output$diffRegulation$FC, pch = 16, main = 'H0', col = geneColor,
xlab = expression(X^2~Quantiles), ylab = 'FC', xlim=c(0,8), ylim=c(0,13))
qqline(qChisq)
legend('bottomright', legend = c('FDR < 0.1'), pch = 16, col = 'red', bty='n', cex = 0.7)
## End(Not run)