| Title: | Mitigating the Adverse Impact of Batch Effects in Sample Pattern Detection |
| Description: | Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' https://github.com/tengfei-emory/QuantNorm. |
| Version: | 1.0.5 |
| Depends: | R (≥ 3.3.0) |
| Imports: | stats, utils |
| Suggests: | GGally, ggplot2, network, pheatmap, rgl, sna |
| Date: | 2019-02-01 |
| Author: | Teng Fei, Tianwei Yu |
| Maintainer: | Teng Fei <tfei@emory.edu> |
| BugReports: | https://github.com/tengfei-emory/QuantNorm/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-02-01 20:07:57 UTC; TFEI |
| Repository: | CRAN |
| Date/Publication: | 2019-02-01 20:23:25 UTC |
Normalized ENCODE raw counts data for both human and mouse.
Description
Normalized ENCODE raw counts data for both human and mouse.
Usage
data(ENCODE)
Format
Large matrix with 10309 rows and 26 columns
Source
Reproduced according to Gilad, Yoav, and Orna Mizrahi-Man. "A reanalysis of mouse ENCODE comparative gene expression data." F1000Research 4 (2015).
Adjust the distance matrix by quantile normalization for data with batch effect
Description
This function applies quantile normalization on the distance matrix (dissimilarity matrix) and return the corrected distance matrix.
Usage
QuantNorm(dat, batch, method = "row/column", cor_method = "spearman",
tol = 0.01, max = 50, logdat = TRUE, standardize = FALSE)
Arguments
dat |
The original p*n batch effect data with n subjects and p RNA-seq measurements or the n by n distance matrix. |
batch |
The vector of length n indicating which batch the subjects belong to. |
method |
Method for the quantile normalization. There are two options: "row/column" and "vectorize". |
cor_method |
Method to calculate the correlation matrix, can be 'spearman'(default), 'pearson' or 'kendall'. |
tol |
The tolerance for the iterative method "row/column", which is the Euclidean distance of the vectorized two dissimilarity matrices before and after each iteration. |
max |
Maximum number of the iteration if the tolerance is not reached. |
logdat |
Whether conducting log transformation to data or not. |
standardize |
Whether conducting standardization [(dat - mean)/sqrt(var)] to data or not. |
Value
Returns the corrected 1-correlation matrix between subjects.
Author(s)
Teng Fei. Email: tfei@emory.edu
References
Fei et al (2018), Mitigating the adverse impact of batch effects in sample pattern detection, Bioinformatics, <https://doi.org/10.1093/bioinformatics/bty117>.
Examples
library(pheatmap) #drawing heatmap
data("ENCODE") #load the ENCODE data
#Before correction, the subjects are clustered by species
pheatmap(cor(ENCODE))
#Assigning the batches based on species
batches <- c(rep(1,13),rep(2,13))
#QuantNorm correction
corrected.distance.matrix <- QuantNorm(ENCODE,batches,method='row/column', cor_method='pearson',
logdat=FALSE, standardize = TRUE, tol=1e-4)
pheatmap(1-corrected.distance.matrix)
Brain RNA-Seq data for both human and mouse.
Description
Brain RNA-Seq data for both human and mouse.
Usage
data(brain)
Format
Large matrix with 15041 rows and 62 columns
Source
Zhang, Ye, et al. "Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse." Neuron 89.1 (2016): 37-53.
Construct connection matrix for network analysis
Description
For data with known labels, this function constructs a connection matrix between unique labels, such as unique cell types. The returned matrix can be used for subject-wise network construction.
Usage
connection.matrix(mat, label, threshold = 0.15, closest = TRUE)
Arguments
mat |
n*n dissimilarity (1-correlation) matrix (e.g. obtained by QuantNorm). |
label |
n-dimension vector for the labels of the n subjects. Replicates share the same label. |
threshold |
A number between 0 to 1. Two groups will be regarded as connected if average 1-correlation < threshold. |
closest |
True or False. Whether connect the closest group or not if the closest group cannot satisfy the threshold condition. |
Value
Returns the connection matrix between unique labels.
Author(s)
Teng Fei. Email: tfei@emory.edu
References
Fei et al (2018), Mitigating the adverse impact of batch effects in sample pattern detection, Bioinformatics, https://doi.org/10.1093/bioinformatics/bty117.
Examples
library(network); library(ggplot2); library(sna); library(GGally) #drawing network graph
data("ENCODE")
#Assigning the batches based on species
batches <- c(rep(1,13),rep(2,13))
#QuantNorm correction
corrected.distance.matrix <- QuantNorm(ENCODE,batches,method='row/column', cor_method='pearson',
logdat=FALSE,standardize = TRUE, tol=1e-4)
#Constructing connection matrix
mat <- connection.matrix(mat=corrected.distance.matrix,label=colnames(corrected.distance.matrix))
#Creating network object and plot
ENCODE.net=network(mat, directed=FALSE)
ENCODE.net %v% "Species" <- c(rep('Human',13),rep('Mouse',13))
p0 <- ggnet2(ENCODE.net,label=TRUE,color = 'Species', palette = "Set2",
size = 3, vjust = -0.6,mode = "kamadakawai",label.size = 3,
color.legend = 'Species')+theme(legend.position = 'bottom')
plot(p0)