# 1 Introduction

Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. More information on the algorithm can be found in (Siska, Bowler, and Kechris (2015), Siska. and Kechris (2016)). The final output are posterior probabilities of differential correlation. This package can be used to determine differential correlation within one –omics data set or between two –omics data sets (provided that both –omics data sets were taken from the same samples). Also, the type of data can be any type of –omics with normal or non-normal distributions. Some examples are metabolomics, transcriptomic, proteomics, etc.

The functions in the Discordant package provide a simple pipeline for intermediate R users to determine differentially correlated pairs. The final output is a table of molecular feature pairs and their respective posterior probabilities. Functions have been written to allow flexibility for users in how they interpret results, which will be discussed further. Currently, the package only supports the comparison between two phenotypic groups (e.g., disease versus control, mutant versus wildtype).

# 2 Discordant Algorithm

Discordant is originally derived from the Concordant algorithm written by (Lai et al. (2007), Lai et al. (2014)). It was used to determine concordance between microarrays. We have applied it to determine differential correlation of features between groups (Siska, Bowler, and Kechris (2015), Siska. and Kechris (2016)).

Using a three component mixture model and the Expectation Maximization (EM) algorithm, the model predicts if the correlation coefficients in phenotypic groups 1 and 2 for a molecular feature pair are dissimilar (Siska, Bowler, and Kechris (2015)). The correlation coefficients are generated for all possible molecular feature pairs witin an -omics dataset or between two -omics data sets. The correlation coefficients are transformed into z scores using Fisher’s transformation. The three components are -, + and 0 which correspond respectively to a negative, positive or no correlation. Molecular features that have correlation coefficients in different components are considered differentially correlated, as opposed to when correlation coefficients are in the same component then they are equivalently correlated.

$\begin{array}{c|c c c} \text{} & \text{0} & \text{-} & \text{+}\\ \hline 0 & 1 & 2 & 3 \\ - & 4 & 5 & 6 \\ + & 7 & 8 & 9 \\ \end{array}$

The class matrix (above) contains the classes that represent all possible paired-correlation scenarios. These scenarios are based off the components in the mixture models. Molecular features that have correlation coefficients in different components are considered differentially correlated, as opposed to when correlation coefficients are in the same component they are equivalently correlated. This can be visualized in the class matrix, where the rows represent the components for group 1 and the columns represent the components for group 2. The classes on the diagonal represent equivalent correlation (1, 5 and 9), and classes in the off-diagonal represent differential correlation (2, 3, 4, 6, 8).

After running the EM algorithm, we have 9 posterior probabilities for each molecular feature pair that correspond to the 9 classes in the class matrix. Since we want to summarize the probability that the molecular feature pair is differentially correlated, we sum the posterior probabilities representing the off-diagonal classes in the class matrix.

# 3 Example Data

The following data sets are provided by discordant and will be used in the examples which follow. All data sets are originally from the Cancer Genome Atlas (TCGA) and can be found at http://cancergenome.nih.gov/.

TCGA_GBM_miRNA_microarray
miRNA expression values from 10 control and 20 tumor samples for a Glioblastoma multiforme (GBM) Agilent miRNA micorarray. The feature size was originally 470, but after features with outliers were filtered out feature size reduces to 331. In this sample data set provided in the package, we randomly selected 10 features.
TCGA_GBM_transcript_microarray
Transcript (or mRNA) expression values from 10 control and 20 tumor samples in a GBM Agilent 244k micorarray. The feature size was originally 90797, but after features with outliers were filtered out, feature size reduces to 72656. In this sample data set provided in the package, we randomly selected 20 features.
TCGA_Breast_miRNASeq
miRNA counts from 15 control and 45 tumor samples in a Breast Cancer Illumina HiSeq miRNASeq. The feature size was originally 212, but after features with outliers were filtered out feature size reduces to 200. In this sample data set provided in the package, we randomly selected 100 features.
TCGA_Breast_RNASeq
Transcript (or mRNA) counts from 15 control and 45 tumor samples in a Breast Cancer Illumina HiSeq RNASeq. The feature size was originally 19414, but after features with outliers were filtered out feature size reduces to 16656. In this sample data set provided in the package, we randomly selected 100 features.
TCGA_Breast_miRNASeq_voom
voom-transformed TCGA_Breast_miRNASeq
TCGA_Breast_RNASeq_voom
voom-transformed TCGA_Breast_RNASeq

The data sets are provided as described above with no other modifications. Throughout this vignette we will use the data sets TCGA_GBM_miRNA_microarray and TCGA_GBM_transcript_microarray to demonstrate discordant’s essential functionality. They are loaded as follows:

# Load data
data(TCGA_GBM_miRNA_microarray)
data(TCGA_GBM_transcript_microarray)

# 4 Before Starting

## 4.1 Types of Analysis

“Within” –omics refers to when Discordant analysis is performed within one –omics dataset where all molecular features within a -omics dataset are paired to each other (e.g. transcript-transcript pairs in a transcriptomics experiment).

“Between” -omics refers to analysis of two -omics data sets. Molecular feature pairs analyzed are between the two -omics, (e.g. transcript-protein, protein- metabolite) are paired.

discordant provides tools for both within and between -omics analysis that will be described in the sections that follow.

## 4.2 Outliers

In our work, we found that features with outliers would skew correlation and cause false positives. Our approach was to filter out features that had large outliers. With normal data, such as in gene expression data from microarrays, Grubbs’ test can be used. The null hypothesis is that there are no outliers in the data, and so features with p-value $$\ge$$ 0.05 are kept. A simple R function is found in the outliers R package as grubbs.test().

Determining outliers in non-normal data is more complicated. We used the median absolute deviation (MAD). Normally, features are filtered if they are outside 2 or 3 MADs from the median (Leys et al. (2013)). This is not completely applicable to sequencing data, because sequencing data has large variance and a non- symmetrical distribution. Therefore we used the ‘split MAD’ approach (Magwene et al. (2011)). A left MAD is determined based on data left to the median and a right MAD is determined based on data to the right of the median. If there are any feature outside a factor of the left or right MAD from the median, they are filtered out.

discordant provides splitMADOutlier() to identify features with outliers using MAD. The number of MAD outside of the median can be changed with option threshold. Another option is filter0 which if TRUE will filter out any feature with at least one 0. Arguments returned are mat.filtered, which is the filtered matrix and index which is the index of features that are retained in mat.filtered.

data(TCGA_Breast_miRNASeq)
filter0 = TRUE,
threshold = 4)

# 5 Correlation Vectors

To run the Discordant algorithm, correlation vectors respective to each group are necessary for input, which are easy to create using the function createVectors(). Each correlation coefficient represents the correlation between two molecular features. The type of molecular feature pairs depends if a within -omics or between -omics analysis is performed. Correlation between molecular features in the same -omics dataset is within -omics, and correlation between molecular features in two different -omics datasets is between -omics. Whether or not within -omics or between -omics analysis is performed depends on whether one or two matrices are parameters for this function. The arguments for createVectors() are:

x
$$m$$ by $$n$$ matrix where $$m$$ are features and $$n$$ are samples. If only this matrix is provided, a within -omics analysis is performed
y
$$m$$ by $$n$$ matrix where $$m$$ are features and $$n$$ are samples. This is an optional argument which will induce between -omics analysis. Samples must be matched with those in x
group
vector containing 1s and 2s that correspond to the location of samples in the columns of x (and y if provided). For example, the control is group 1 and the experimental group 2, and the locations of samples corresponding to the two groups matches the locations of 1s and 2s in the group vector

createVectors() is then run as follows:

groups <- c(rep(1,10), rep(2,20))

# Within -omics
wthn_vectors <- createVectors(x = TCGA_GBM_transcript_microarray,
groups = groups)
# Between -omics
btwn_vectors <- createVectors(x = TCGA_GBM_miRNA_microarray,
y = TCGA_GBM_transcript_microarray,
groups = groups)

createVectors() returns a list with two elements, v1 and v2, which are the correlation vectors of molecular feature pairs corresponding to samples labeled 1 and 2 using the groups argument, respectively. Each vector is a numeric named vector with names indicating each feature in the pair separated by an underscore. Below are the first few correlations for each group, first from the within -omics analysis and second from the between -omics analysis.

# Within -omics
head(wthn_vectors$v1) ## A_23_P138644_A_23_P24296 A_23_P138644_A_24_P345312 A_23_P138644_A_24_P571870 ## 0.36969697 -0.26060606 -0.13939394 ## A_23_P138644_A_32_P71885 A_23_P138644_A_32_P82889 A_23_P138644_A_23_P105264 ## -0.09090909 -0.04863244 -0.69696970 head(wthn_vectors$v2)
##  A_23_P138644_A_23_P24296 A_23_P138644_A_24_P345312 A_23_P138644_A_24_P571870
##               0.216541353              -0.433082707              -0.254135338
##  A_23_P138644_A_32_P71885  A_23_P138644_A_32_P82889 A_23_P138644_A_23_P105264
##              -0.285714286              -0.009022556              -0.154887218
# Between -omics
head(btwn_vectors$v1) ## hsa-miR-19b-5p_A_23_P138644 hsa-miR-19b-5p_A_23_P24296 ## -0.4303030 -0.6000000 ## hsa-miR-19b-5p_A_24_P345312 hsa-miR-19b-5p_A_24_P571870 ## 0.1878788 -0.3939394 ## hsa-miR-19b-5p_A_32_P71885 hsa-miR-19b-5p_A_32_P82889 ## 0.3818182 -0.4060606 head(btwn_vectors$v2)
## hsa-miR-19b-5p_A_23_P138644  hsa-miR-19b-5p_A_23_P24296
##                  0.44060150                 -0.02857143
## hsa-miR-19b-5p_A_24_P345312 hsa-miR-19b-5p_A_24_P571870
##                 -0.03308271                  0.26015038
##  hsa-miR-19b-5p_A_32_P71885  hsa-miR-19b-5p_A_32_P82889
##                 -0.08571429                 -0.37744361

## 5.1 Correlation Metrics

The function createVectors() provides several options for correlation metrics using the argument cor.method. The methods provided include "spearman" (the default metric), "pearson", "bwmc", and "sparcc". For information and comparison of Spearman, Pearson and biweight midcorrelation (bwmc) see Song et al (Song, Langfelder, and Horvath (2012)). We have also investigated correlation metrics in Discordant in relation to sequencing data and found Spearman’s correlation had the best performance (Siska. and Kechris (2016)).

The algorithm for SparCC was introduced by Friedman et al. (Friedman and Alm (2012)), and we use code provided by Huaying Fang (Fang et al. (2015)).

# 6 Calling Discordant

The Discordant Algorithm is implemented in the the function discordantRun() which requires two correlation vectors and the original data. If the user wishes to generate their own correlation vector before inputting the data set, they can do so. However, the function will return an error message if the dimensions of the data sets inserted do not match the correlation vector. discordantRun() is called as follows:

# Within -omics
wthn_result <- discordantRun(v1 = wthn_vectors$v1, v2 = wthn_vectors$v2,
x = TCGA_GBM_transcript_microarray)

# Between -omics
btwn_result <- discordantRun(v1 = btwn_vectors$v1, v2 = btwn_vectors$v2,
x = TCGA_GBM_miRNA_microarray,
y = TCGA_GBM_transcript_microarray)

## 6.1 Output

The posterior probability output of the Discordant algorithm are the differential correlation posterior probabilities (the sum of the off-diagonal of the class matrix described above). If the user wishes to observe more detailed information, alternative outputs are available. discordantRun() has six outputs:

discordPPMatrix

Matrix of differential correlation posterior probabilities where rows and columns reflect features. If only x was inputted, then the number of rows and columns are number of features in x. The rows and column names are the feature names, and the upper diagonal of the matrix are NAs to avoid repeating results. If x and y are inputted, the number of rows is the feature size of x and the number of columns the feature size of y. The row names are features from x and the column names are features from y.

# Within -omics
wthn_result$discordPPMatrix[1:5, 1:4] ## A_23_P138644 A_23_P24296 A_24_P345312 A_24_P571870 ## A_23_P138644 NA NA NA NA ## A_23_P24296 0.2878537 NA NA NA ## A_24_P345312 0.8912767 0.60018437 NA NA ## A_24_P571870 0.5241415 0.14509976 0.223684 NA ## A_32_P71885 0.6669908 0.09268763 0.808103 0.2945575 # Between -omics btwn_result$discordPPMatrix[1:5, 1:4]
##                 A_23_P138644 A_23_P24296 A_24_P345312 A_24_P571870
## hsa-miR-19b-5p    0.97651996  0.38597448   0.54913355   0.30547498
## hsa-miR-206-5p    0.86697876  0.03064989   0.11030081   0.21411118
## hsa-miR-369-5p    0.04519821  0.08113338   0.90726304   0.04050697
## hsa-miR-374-5p    0.70594948  0.03692205   0.07830365   0.58323497
## hsa-miR-376a-5p   0.21071194  0.04581947   0.90287297   0.05971681

discordPPVector

Vector of differential correlation posterior probabilities. The length is the number of feature pairs. The names of the vector are the feature pairs.

# Within -omics
head(wthn_result$discordPPVector) ## A_23_P138644_A_23_P24296 A_23_P138644_A_24_P345312 A_23_P138644_A_24_P571870 ## 0.28785373 0.89127669 0.52414148 ## A_23_P138644_A_32_P71885 A_23_P138644_A_32_P82889 A_23_P138644_A_23_P105264 ## 0.66699081 0.03938042 0.89322774 # Between -omics head(btwn_result$discordPPVector)
## hsa-miR-19b-5p_A_23_P138644  hsa-miR-19b-5p_A_23_P24296
##                  0.97651996                  0.86697876
## hsa-miR-19b-5p_A_24_P345312 hsa-miR-19b-5p_A_24_P571870
##                  0.04519821                  0.70594948
##  hsa-miR-19b-5p_A_32_P71885  hsa-miR-19b-5p_A_32_P82889
##                  0.21071194                  0.47601903

classMatrix

Matrix of classes with the highest posterior probability for each pair. Row and column names are the same as in discordPPMatrix and determined by whether only x is inputted or both x and y.

# Within -omics
wthn_result$classMatrix[1:5,1:4] ## A_23_P138644 A_23_P24296 A_24_P345312 A_24_P571870 ## A_23_P138644 NA NA NA NA ## A_23_P24296 1 NA NA NA ## A_24_P345312 4 3 NA NA ## A_24_P571870 4 1 1 NA ## A_32_P71885 4 1 3 1 # Between -omics btwn_result$classMatrix[1:5,1:4]
##                 A_23_P138644 A_23_P24296 A_24_P345312 A_24_P571870
## hsa-miR-19b-5p             7           1            2            1
## hsa-miR-206-5p             2           1            9            1
## hsa-miR-369-5p             1           1            2            1
## hsa-miR-374-5p             7           1            1            2
## hsa-miR-376a-5p            1           1            2            1

classVector

Vector of class with the highest posterior probability for each pair. The length is the number of feature pairs. Names of vector correspond to the feature pairs, similar to discordPPVector.

# Within -omics
head(wthn_result$classVector) ## A_23_P138644_A_23_P24296 A_23_P138644_A_24_P345312 A_23_P138644_A_24_P571870 ## 1 4 4 ## A_23_P138644_A_32_P71885 A_23_P138644_A_32_P82889 A_23_P138644_A_23_P105264 ## 4 1 2 # Between -omics head(btwn_result$classVector)
## hsa-miR-19b-5p_A_23_P138644  hsa-miR-19b-5p_A_23_P24296
##                           7                           2
## hsa-miR-19b-5p_A_24_P345312 hsa-miR-19b-5p_A_24_P571870
##                           1                           7
##  hsa-miR-19b-5p_A_32_P71885  hsa-miR-19b-5p_A_32_P82889
##                           1                           4

probMatrix

Matrix of all posterior probabilities, where the number of rows is the number of feature pairs and the columns represent the class within the class matrix. The number of columns can be 9 or 25, depending on how many mixture components are chosen (discussed later). The values across each row add up to 1. Posterior probabilities in discordPPMatrix and discordPPVector are the summation of columns that correspond to differential correlation classes (described above). Each column corresponds to the respectively numbered element from the class matrix above for three components or the class matrix described below for five components.

# Within -omics
round(head(wthn_result$probMatrix), 2) ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## A_23_P138644_A_23_P24296 0.44 0.00 0.24 0.00 0.00 0 0.04 0.00 0.27 ## A_23_P138644_A_24_P345312 0.10 0.00 0.00 0.89 0.01 0 0.00 0.00 0.00 ## A_23_P138644_A_24_P571870 0.48 0.00 0.01 0.51 0.00 0 0.00 0.00 0.00 ## A_23_P138644_A_32_P71885 0.33 0.00 0.01 0.66 0.00 0 0.00 0.00 0.00 ## A_23_P138644_A_32_P82889 0.96 0.00 0.03 0.00 0.00 0 0.01 0.00 0.00 ## A_23_P138644_A_23_P105264 0.05 0.88 0.00 0.00 0.05 0 0.00 0.01 0.00 # Between -omics round(head(btwn_result$probMatrix), 2)
##                             [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## hsa-miR-19b-5p_A_23_P138644 0.02 0.02 0.00 0.00 0.00    0 0.73 0.22 0.00
## hsa-miR-19b-5p_A_23_P24296  0.13 0.86 0.00 0.00 0.00    0 0.00 0.01 0.00
## hsa-miR-19b-5p_A_24_P345312 0.95 0.00 0.02 0.00 0.00    0 0.02 0.00 0.00
## hsa-miR-19b-5p_A_24_P571870 0.29 0.16 0.00 0.00 0.00    0 0.45 0.09 0.00
## hsa-miR-19b-5p_A_32_P71885  0.78 0.00 0.19 0.01 0.00    0 0.01 0.00 0.01
## hsa-miR-19b-5p_A_32_P82889  0.17 0.11 0.00 0.37 0.35    0 0.00 0.00 0.00

loglik

The log likelihood from the model fit.

# Within -omics
wthn_result$loglik ## [1] 1199.92 # Between -omics btwn_result$loglik
## [1] 1266.161

## 6.2 Subsampling

Subsampling is an option to run the EM algorithm with a random sample of independent feature pairs. This is repeated for a number of samplings, and then the average of these parameters are used to maximize posterior probabilities for all feature pairs. This option was introduced to speed up Discordant method and to also address the independence assumption.

The argument subsampling must be set to TRUE for subsampling to be used. By default, the number of independent feature pairs to be subsampled is half the total number of features divided by two for within -omics analysis and the number of features in the data set with fewer features for between -omics analysis. This number may be altered by users using the subSize argument, but the value set by users cannot exceed the default value, as the default value is the maximum number of independent sample pairs possible for a given analysis.

The number of random samplings to be run is set by the argument iter which has a default value of 100.

As discussed in the next section, the discordant method requires a sufficient number of features to estimate components, and using subsampling reduces the quantity of features used for analysis, so subsampling should be reserved for larger data sets. For some data sets, certain random samples will be sufficient, while others may not be. For those data sets, the subsampling algorithm will allow up to 10% of iterations to fail and be repeated. If more than 10% of iterations fail, discordantRun() will throw an error with potential solutions, as shown below.

# Between -omics
btwn_result <- discordantRun(v1 = btwn_vectors$v1, v2 = btwn_vectors$v2,
x = TCGA_GBM_miRNA_microarray,
y = TCGA_GBM_transcript_microarray,
components = 3,
subsampling = TRUE)
## Error in discordantRun(v1 = btwn_vectors$v1, v2 = btwn_vectors$v2, x = TCGA_GBM_miRNA_microarray, :
## Insufficient data for subsampling. Increase number of
## features, reduce numberof components used, or increase
## subSize if not at default value. Alternatively, set
## subsampling=FALSE.

Given the limited number of features in the TCGA_GBM data sets, they are not a suitable candidate for subsampling, so we will instead use the TCGA_Breast_miRNASeq and TCGA_Breast_RNASeq data sets to demonstrate a multi -omics analysis with subsampling. Note that a seed is set prior to calling discordantRun() with subsampling = TRUE, as there is a randomness involved in drawing subsamples, and results may differ using different seeds.

# Load Data
data(TCGA_Breast_miRNASeq_voom)
data(TCGA_Breast_RNASeq_voom)

# Prepare groups
groups <- c(rep(1, 15), rep(2, 42))

# Create correlation vectors
sub_vectors <- createVectors(x = TCGA_Breast_miRNASeq_voom,
y = TCGA_Breast_RNASeq_voom,
groups = groups)

# Run analysis with subsampling
set.seed(126)
sub_result <- discordantRun(sub_vectors$v1, sub_vectors$v2,
x = TCGA_Breast_miRNASeq_voom,
y = TCGA_Breast_RNASeq_voom,
components = 3, subsampling = TRUE)

# Results
round(head(sub_result$probMatrix), 2) ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] ## hsa-mir-1247_MAPK8IP1|9479 0.12 0.00 0.02 0.00 0.00 0 0.53 0 0.34 ## hsa-mir-1247_PPWD1|23398 0.98 0.01 0.00 0.00 0.00 0 0.00 0 0.00 ## hsa-mir-1247_CEACAM22P|388550 0.94 0.03 0.00 0.00 0.02 0 0.00 0 0.00 ## hsa-mir-1247_HTR2A|3356 0.94 0.00 0.01 0.00 0.00 0 0.02 0 0.03 ## hsa-mir-1247_SAMD12|401474 0.00 0.00 0.00 0.53 0.47 0 0.00 0 0.00 ## hsa-mir-1247_INTS7|25896 0.35 0.00 0.00 0.58 0.07 0 0.00 0 0.00 ## 6.3 Five Components We also provide the option to increase component size from three to five in the mixture model. The number of classes in the class matrix increases, as seen in the table below. Incorporating the extra components means that it is possible to identify elevated differential correlation, which is when there are associations in both groups in the same direction but one is more extreme. Using this option introduces more parameters, which does have an effect on run-time. We also found that using the five mixture component mixture model reduces performance compared to the three component mixture model(Siska. and Kechris (2016)). However, the option is available if users wish to explore more types of differential correlation. $\begin{array}{c|c c c c c} \text{} & \text{0} & \text{-} & \text{--} & \text{+} & \text{++} \\ \hline 0 & 1 & 2 & 3 & 4 & 5 \\ - & 6 & 7 & 8 & 9 & 10 \\ -- & 11 & 12 & 13 & 14 & 15 \\ + & 16 & 17 & 18 & 19 & 20 \\ ++ & 21 & 22 & 23 & 24 & 25 \end{array}$ By default, discordantRun() uses a three component mixture model, but this may be changed to a five component mixture model by setting the argument components = 5. A greater amount of data (specifically a greater number of features) is necessary to accurately estimate 5 components compared to 3. If an insufficient amount of data is used, discordantRun() will throw an error suggesting the user increase the number of features or reduce the chosen number of components. The data used for the within -omics analysis above does not have enough features to estimate 5 components, so an error is thrown below. # Within -omics wthn_result <- discordantRun(v1 = wthn_vectors$v1,
v2 = wthn_vectors$v2, x = TCGA_GBM_transcript_microarray, components = 5) ## Error in discordantRun(v1 = wthn_vectors$v1, v2 = wthn_vectors$v2, x = TCGA_GBM_transcript_microarray, : ## Insufficient data for component estimation. Increase ## number of features or reduce number of components used. However, the between -omics analysis above is a suitable candidate for further analysis using five components, so an example of such an analysis is provided below. # Between -omics btwn_result <- discordantRun(v1 = btwn_vectors$v1,
v2 = btwn_vectors$v2, x = TCGA_GBM_miRNA_microarray, y = TCGA_GBM_transcript_microarray, components = 5) # Between -omics round(head(btwn_result$probMatrix), 2)
##                             [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## hsa-miR-19b-5p_A_23_P138644 0.02 0.01 0.00 0.00    0  0.0 0.00    0    0     0
## hsa-miR-19b-5p_A_23_P24296  0.14 0.86 0.00 0.01    0  0.0 0.00    0    0     0
## hsa-miR-19b-5p_A_24_P345312 0.98 0.00 0.01 0.00    0  0.0 0.00    0    0     0
## hsa-miR-19b-5p_A_24_P571870 0.27 0.10 0.00 0.00    0  0.0 0.00    0    0     0
## hsa-miR-19b-5p_A_32_P71885  0.79 0.00 0.21 0.00    0  0.0 0.00    0    0     0
## hsa-miR-19b-5p_A_32_P82889  0.18 0.08 0.00 0.00    0  0.3 0.44    0    0     0
##                             [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
## hsa-miR-19b-5p_A_23_P138644  0.48  0.18     0     0     0     0     0     0
## hsa-miR-19b-5p_A_23_P24296   0.00  0.00     0     0     0     0     0     0
## hsa-miR-19b-5p_A_24_P345312  0.00  0.00     0     0     0     0     0     0
## hsa-miR-19b-5p_A_24_P571870  0.50  0.11     0     0     0     0     0     0
## hsa-miR-19b-5p_A_32_P71885   0.00  0.00     0     0     0     0     0     0
## hsa-miR-19b-5p_A_32_P82889   0.00  0.00     0     0     0     0     0     0
##                             [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## hsa-miR-19b-5p_A_23_P138644     0     0  0.24  0.06     0     0     0
## hsa-miR-19b-5p_A_23_P24296      0     0  0.00  0.00     0     0     0
## hsa-miR-19b-5p_A_24_P345312     0     0  0.00  0.00     0     0     0
## hsa-miR-19b-5p_A_24_P571870     0     0  0.01  0.00     0     0     0
## hsa-miR-19b-5p_A_32_P71885      0     0  0.00  0.00     0     0     0
## hsa-miR-19b-5p_A_32_P82889      0     0  0.00  0.00     0     0     0

# 7 Session Info

sessionInfo()
## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
##  [3] LC_TIME=en_GB              LC_COLLATE=C
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
## [1] discordant_1.21.0 BiocStyle_2.25.0
##
## loaded via a namespace (and not attached):
##  [1] pcaPP_2.0-1         Rcpp_1.0.8.3        pillar_1.7.0
##  [4] bslib_0.3.1         compiler_4.2.0      DEoptimR_1.0-11
##  [7] BiocManager_1.30.17 jquerylib_0.1.4     tools_4.2.0
## [10] digest_0.6.29       tibble_3.1.6        lifecycle_1.0.1
## [13] jsonlite_1.8.0      evaluate_0.15       lattice_0.20-45
## [16] pkgconfig_2.0.3     rlang_1.0.2         DBI_1.1.2
## [19] cli_3.3.0           yaml_2.3.5          mvtnorm_1.1-3
## [22] xfun_0.30           fastmap_1.1.0       stringr_1.4.0
## [25] dplyr_1.0.8         knitr_1.38          generics_0.1.2
## [28] sass_0.4.1          vctrs_0.4.1         gtools_3.9.2
## [31] tidyselect_1.1.2    stats4_4.2.0        grid_4.2.0
## [34] glue_1.6.2          robustbase_0.95-0   Biobase_2.57.0
## [37] rrcov_1.7-0         R6_2.5.1            fansi_1.0.3
## [40] rmarkdown_2.14      bookdown_0.26       purrr_0.3.4
## [43] magrittr_2.0.3      ellipsis_0.3.2      htmltools_0.5.2
## [46] MASS_7.3-57         BiocGenerics_0.43.0 assertthat_0.2.1
## [49] biwt_1.0            utf8_1.2.2          stringi_1.7.6
## [52] crayon_1.5.1

# References

Fang, Huaying, Chengcheng Huang, Hongyu Zhao, and Minghua Deng. 2015. “CCLasso: Correlation Inference for Compositional Data Through Lasso.” Bioinformatics 31 (19): 3172–80.

Friedman, Jonathan, and Eric J. Alm. 2012. “Inferring Correlation Networks from Genomic Survey Data.” PLoS Computational Biology.

Lai, Yinglei, Bao-ling Adam, Robert Podolsky, and Jin-Xiong She. 2007. “A Mixture Model Approach to the Tests of Concordance and Discordance Between Two Large-Scale Experiments with Two-Sample Groups.” Bioinformatics 23 (10): 1243–50.

Lai, Yinglei, Fanni Zhang, Tapan K. Naya, Reza Modarres, Norman H. Lee, and Timonthy A. McCaffrey. 2014. “Concordant Integrative Gene Set Enrichment Analysis of Multiple Large-Scale Two-Sample Expression Data Sets.” BMC Genomics 15.

Leys, Christophe, Olivier Klein, Philippe Bernard, and Laurent Licata. 2013. “Detecting Outliers: Do Not Use Standard Deviation Around the Mean, Use Absolute Deviation Around the Median.” Journal of Experimental Social Psychology 49 (4).

Magwene, Paul M., John H. Willis, John K. Kelley, and Adam Siepel. 2011. “The Statistics of Bulk Segregant Analysis Using Next Generation Sequencing.” PLoS Computational Biology 7 (11).

Siska, Charlotte, Russ Bowler, and Katerina Kechris. 2015. “The Discordant Method: A Novel Approach for Differential Correlation.” Bioinformatics 32 (5): 690–96.

Siska., Charlotte, and Katerina Kechris. 2016. “Differential Correlation for Sequencing Data.”

Song, Lin, Peter Langfelder, and Steve Horvath. 2012. “Comparison of Co-Expression Measures: Mutual Information, Correlation, and Model Based Indices.” BMC Bioinformatics 13 (328).