dcanr 1.14.0

This package provides methods to perform differential co-expression analysis and to evaluate differential co-expression methods using simulated data. Differential co-expression analysis attempts to identify gene-gene associations that change across conditions. Currently, 10 methods that identify changes between binary conditions are included: 8 are novel Bioconductor implementations of previously published methods, and; 2 are accessed through interfaces to existing packages.

This vignette focuses on the application of differential co-expression inference methods to real data. Available methods, putative pipelines, and visualisations provided by the method are introduced.

Download the package from Bioconductor

```
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("dcanr")
```

Or install the development version of the package from Github.

`BiocManager::install("DavisLaboratory/dcanr")`

Load the installed package into an R session.

`library(dcanr)`

The package implements 10 methods to infer differential co-expression networks
across binary conditions. The list of available methods can be accessed by the
`dcMethods()`

function.

```
library(dcanr)
dcMethods()
```

```
## [1] "dicer" "diffcoex" "ebcoexpress" "entropy" "ftgi"
## [6] "ggm-based" "ldgm" "magic" "mindy" "zscore"
```

A differential co-expression pipeline generally consists of 4 steps:

- Generating scores/statistics for each pair of genes
- Assessing scores using statistical tests
- Correcting for multiple hypothesis testing
- Selecting differential associations

Not all methods follow this pipeline. EBcoexpress computes posterior probabilities therefore no statistical test needs to be performed and steps 2-3 can be skipped. Like-wise DiffCoEx does not perform any statistical tests and instead performs a soft-thresholding on the scores. FTGI performs a statistical test and \(p\)-values from this test are used as scores, therefore step 2 is skipped. A standard analysis with the z-score method using all 4 steps is shown here.

We first load an example simulated dataset (included in the package) to extract
the expression matrix and condition vector. Please note that multiple knock-down
experiments are performed per simulation and we use one such knock-down as a
condition here. The list of all knock-downs can be retrieved using
`getConditionNames()`

.

```
#load data
data(sim102)
#get available conditions
getConditionNames(sim102)
```

`## [1] "ADR1" "UME6"`

```
#get expression data and conditions for 'UME6' knock-down
simdata <- getSimData(sim102, cond.name = 'UME6', full = FALSE)
emat <- simdata$emat
ume6_kd <- simdata$condition
print(emat[1:5, 1:5]) #149 genes and 406 samples
```

```
## sample_1 sample_2 sample_3 sample_4 sample_5
## ADR1 0.4997864 0.4692072 0.3937255 0.1390989 0.05291159
## FLO8 0.3340156 0.2845828 0.3682575 0.2263656 0.19292056
## GIS1 0.3955383 0.4922471 0.5006614 0.2672858 0.48041978
## IME4 0.4658504 0.3529325 0.4188025 0.3824827 0.33371681
## KAR4 0.3705384 0.5812146 0.5282095 0.4945329 0.46677489
```

`head(ume6_kd) #NOTE: binary conditions encoded with 1's and 2's`

```
## sample_1 sample_2 sample_3 sample_4 sample_5 sample_6
## 2 2 1 1 1 1
```

All inference methods can be accessed using the same call therefore making it
easier to change between methods. Method specific parameters can be passed to
this function and will be managed accordingly. The default inference method is
z-score therefore it does not need to be specified via `dc.method`

. We recommend
using the Spearman correlation as a measure of correlation as it is robust to
outliers which may be present in RNA-seq data.

```
#apply the z-score method with Spearman correlations
z_scores <- dcScore(emat, ume6_kd, dc.method = 'zscore', cor.method = 'spearman')
print(z_scores[1:5, 1:5])
```

```
## ADR1 FLO8 GIS1 IME4 KAR4
## ADR1 NA -0.05890109 1.0645602 -0.6869672 -0.2843683
## FLO8 -0.05890109 NA 0.1855110 0.9256449 0.6803593
## GIS1 1.06456016 0.18551097 NA 0.6336496 -1.2508845
## IME4 -0.68696722 0.92564487 0.6336496 NA 0.4433642
## KAR4 -0.28436825 0.68035927 -1.2508845 0.4433642 NA
```

Appropriate statistical tests are automatically selected for the method applied.
Tests are applied on the result of the `dcScore()`

function (z-test for the
z-score method and permutation tests for other methods). The testing
function returns the score matrix (unmodified) if the method is either
EBcoexpress, FTGI or DiffCoEx.

*NOTE: Do NOT modify the result of the scoring method as this will result in
failure of the testing function. This is intended as tests should be performed
for all computed scores to prevent bias in the subsequent correction for
multiple hypothesis testing. The same applies for the next step.*

```
#perform a statistical test: the z-test is selected automatically
raw_p <- dcTest(z_scores, emat, ume6_kd)
print(raw_p[1:5, 1:5])
```

```
## ADR1 FLO8 GIS1 IME4 KAR4
## ADR1 NA 0.9530309 0.2870750 0.4921034 0.7761282
## FLO8 0.9530309 NA 0.8528283 0.3546306 0.4962770
## GIS1 0.2870750 0.8528283 NA 0.5263095 0.2109766
## IME4 0.4921034 0.3546306 0.5263095 NA 0.6575023
## KAR4 0.7761282 0.4962770 0.2109766 0.6575023 NA
```

For methods such as MINDy that require a permutation test, the number of
permutations can be specified by the `B`

parameter. Permutation tests are
computationally expensive therefore we also provide a parallelised
implementation. See the help page of `dcTest`

for examples.

Since all pairwise combinations of genes are tested, \(p\)-values need to be
adjusted. Given \(n\) genes, the total number of hypothesis is \(\frac{n(n-1)}{2}\)
as the score matrices are symmetric. Adjustment is performed accordingly. The
default adjustment function is `stats::p.adjust`

with the â€˜fdrâ€™ method used,
however, custom functions and their parameters can be specified instead.
`dcAdjust`

provides a wrapper to apply an adjustment method to the raw \(p\)-value
matrix. Results from EBcoexpress and DiffCoEx remain unmodified.

```
#adjust p-values (raw p-values from dcTest should NOT be modified)
adj_p <- dcAdjust(raw_p, f = p.adjust, method = 'fdr')
print(adj_p[1:5, 1:5])
```

```
## ADR1 FLO8 GIS1 IME4 KAR4
## ADR1 NA 0.9933963 0.8879726 0.9289498 0.9655700
## FLO8 0.9933963 NA 0.9748335 0.9116214 0.9289498
## GIS1 0.8879726 0.9748335 NA 0.9328853 0.8494282
## IME4 0.9289498 0.9116214 0.9328853 NA 0.9512398
## KAR4 0.9655700 0.9289498 0.8494282 0.9512398 NA
```

The last step is thresholding the score/adjusted \(p\)-value matrix to select
differential associations. Default adjusted \(p\)-value thresholds of 0.1 are applied
where statistical tests are performed (to control for FDR at 0.1). Results are
presented as an `igraph`

object shown below where edges are coloured based on
the score (negative to positive scores are represented using the purple to green
gradient of colours).

```
library(igraph)
#get the differential network
dcnet <- dcNetwork(z_scores, adj_p)
plot(dcnet, vertex.label = '')
```

```
#convert to an adjacency matrix
adjmat <- as_adj(dcnet, sparse = FALSE)
print(adjmat[1:5, 1:5])
```

```
## ADR1 FLO8 GIS1 IME4 KAR4
## ADR1 0 0 0 0 0
## FLO8 0 0 0 0 0
## GIS1 0 0 0 0 0
## IME4 0 0 0 0 0
## KAR4 0 0 0 0 0
```

```
#convert to a data.frame
edgedf <- as_data_frame(dcnet, what = 'edges')
print(head(edgedf))
```

```
## from to score color
## 1 ADR1 ACS1 -3.648617 #671F73B3
## 2 ADR1 CTA1 -4.399843 #40004BB3
## 3 ADR1 FOX2 -4.712234 #40004BB3
## 4 ADR1 GUT1 -5.999899 #40004BB3
## 5 STE12 BAR1 4.426577 #00441BB3
## 6 SWI5 RME1 -3.780175 #5E186AB3
```

```
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] igraph_1.3.5 dcanr_1.14.0 BiocStyle_2.26.0
##
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```