knitr::opts_chunk$set( collapse = FALSE, comment = "#>", fig.height = 5, fig.width = 5 ) old.ops <- options(width = 100)

The capabilities of MAnorm2 result primarily from two basic utilities implemented in it. One is for the normalization of ChIP-seq samples, and the other is for modeling the mean-variance trend associated with normalized ChIP-seq signal intensities. Several downstream analyses could be performed based on these two utilities, including

- differential ChIP-seq analysis between individual samples or groups of samples;
- identification of genomic regions with hypervariable ChIP-seq signals across individual samples or groups of samples;
- clustering of individual ChIP-seq samples or groups of samples.

We'd like to emphasize here that each of the above analyses takes advantage of the observed mean-variance trend to improve the assessment of within-group variability (i.e., the variation of ChIP-seq signals across samples of the same group). In practice, the strategy could compensate for the lack of sufficient replicates for accurately assessing within-group variability.

For employing the machinery implemented in MAnorm2, you need to prepare a table
that profiles the ChIP-seq signal in each of a list of genomic intervals for
each of a set of ChIP-seq samples. The `H3K27Ac`

dataset bundled with MAnorm2
provides such an instance:

library(MAnorm2) head(H3K27Ac)

To be specific, each row of the above table represents a genomic interval; each
of the `read_cnt`

variables corresponds to a ChIP-seq sample and records the
numbers of reads from the sample that fall within the genomic intervals (i.e.,
the raw read counts for the sample); the `occupancy`

variables correspond to
the `read_cnt`

variables one by one and specify the occupancy status of each
interval in each sample (an occupancy status of 1 indicates that the interval
is enriched with reads in the sample). In practice, the occupancy status of a
genomic interval in a certain ChIP-seq sample could be determined by its
overlap with the peaks [@MACS_GB] of the sample. Note also that MAnorm2 refers
to an interval as occupied by a sample if the interval is enriched with reads
in the sample.

MAnorm2_utils is specifically designed to coordinate with MAnorm2, and we strongly recommend using it to create input tables of MAnorm2.

Note also that, although the above table records raw read counts, MAnorm2 does not impose a restriction that the input measurements of ChIP-seq signals must be integers (see also the section of Continuous Distribution below).

Although MAnorm2 has been designed to process ChIP-seq data, it could be applied in principle to the analysis of any type of data with a similar structure, including DNase-seq, ATAC-seq and RNA-seq data. The only problem associated with such extensions is how to naturally define "peaks" for specific data types.

Most of the peak callers originally devised for ChIP-seq data
(e.g., MACS 1.4) also
work for DNase-seq and ATAC-seq data. For RNA-seq data, each row of the input
table should stand for a gene, and we recommend setting a cutoff (e.g., 20) of
*raw read count* to define "peak" genes.

In spite of the discrete nature of read counts, MAnorm2 uses continuous distribution to model ChIP-seq data by first transforming raw read counts into raw signal intensities. By default, MAnorm2 completes the transformation by simply adding an offset count to each raw count and taking a base-2 logarithm. Practical ChIP-seq data sets, however, may be associated with various confounding factors, including batch effects, local sequence compositions and background signals measured by input samples. On this account, the MAnorm2 machinery has been designed to be independent of the specific transformation employed. And any methods for correcting for confounding factors could be applied before invoking MAnorm2, as long as the resulting signal intensities could be approximately modeled as following the normal distribution (in particular, consider carefully whether it is necessary to apply a logarithmic transformation in the final step). In the extreme case, you can even accomplish the normalization of ChIP-seq data by yourself and use MAnorm2, for example, only for the following differential analysis.

The primary reason for which MAnorm2 models ChIP-seq signals as continuous random variables is that the mathematical theory of count distributions is far less tractable than that of the normal distribution. For example, current statistical methods based on the negative binomial distribution are frequently relied on approximations of various kinds. Specifically, variance (or dispersion) estimates for individual genomic intervals are typically treated as known parameters, and their uncertainty can hardly be incorporated into the statistical tests for identifying differential signals.

Besides, after an extensive correction for confounding factors, the resulting data range is almost certainly not limited to non-negative integers, and the data may have lost their discrete nature and be more akin to a continuous distribution. Moreover, transforming read counts towards the normal distribution unlocks the application of a large repository of mature statistical methods that are initially developed for analyzing continuous measurements (e.g., intensity data from microarray experiments). Refer to the voom article [@voom] for a detailed discussion of this topic.

This section explains in detail the working principle of MAnorm2 and
demonstrates the use of it for various analyses. Note that all demonstrations
are based on the `H3K27Ac`

dataset bundled with MAnorm2 (see also the section
of Input Data):

library(MAnorm2) head(H3K27Ac)

This dataset profiles H3K27Ac ChIP-seq signals on a genome wide scale for three human lymphoblastoid cell lines (LCLs), each derived from a separate Caucasian individual (associated ChIP-seq data obtained from [@kasowski2013extensive]). For meta information regarding these cell lines, type

attr(H3K27Ac, "metaInfo")

For details about the generation of this dataset, type `?H3K27Ac`

.

Here we show the standard workflow for a differential ChIP-seq analysis between two groups of samples. We use the comparison between the H3K27Ac ChIP-seq samples for GM12891 LCL and those for GM12892 LCL as example:

# Perform within-group normalization. norm <- normalize(H3K27Ac, count = 5:6, occupancy = 10:11) norm <- normalize(norm, count = 7:8, occupancy = 12:13) # Construct a bioCond for each group of samples. conds <- list(GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"), GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892")) # Perform between-group normalization. # Restrict common peak regions to autosomes only when the two groups # being compared are associated with different genders. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) conds <- normBioCond(conds, common.peak.regions = autosome) # Fit a mean-variance curve. # If the following function call raises an error, # set init.coef = c(0.1, 10) or try method = "local". conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE) # Perform differential tests. res <- diffTest(conds[[1]], conds[[2]]) head(res)

**Note:** rows of the above table of differential analysis results correspond
to the genomic intervals in `H3K27Ac`

one by one with the same order. See also
a detailed explanation below.

MAnorm2 normalizes two individual ChIP-seq samples by removing the overall M-A trend (M and A values refer to log2 fold change and average log2 read count, respectively) associated with their common peak regions, which are the genomic intervals occupied by both of them. For normalization of a set of any number of ChIP-seq samples, MAnorm2 selects one of the samples as baseline and normalizes each other sample against it. Taking the comparison of H3K27Ac ChIP-seq signals between GM12891 and GM12892 LCLs as example, you may choose to normalize all the related samples once for all, by supplying raw read counts and occupancy states associated with the samples:

# One-step normalization. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) norm <- normalize(H3K27Ac, count = 5:8, occupancy = 10:13, common.peak.regions = autosome)

**Note:** here we exclude the genomic intervals in sex chromosomes from common
peak regions, since these ChIP-seq samples are associated with different
genders.

By default, MAnorm2 uses the median-ratio strategy [@DESeq] to estimate the
size factor of each ChIP-seq sample and selects the sample whose log2 size
factor is closest to 0 as baseline. In practice, you can avoid using
a sample with bad quality as baseline by explicitly specifying the `baseline`

argument of `normalize()`

. Besides, when the number of samples to be normalized
is large (e.g., >5), you can reduce the variability of normalization results by
setting `baseline`

to `"pseudo-reference"`

, in
which case MAnorm2 constructs a pseudo ChIP-seq profile as baseline
by "averaging" all the samples (type `?normalize`

for details).

Check some information regarding the performed normalization by

names(attributes(norm)) attributes(norm)[5:8] # This statement requires the gplots package (>= 3.0.1). plot(attr(norm, "MA.cor"), symbreaks = TRUE, margins = c(8, 8), cexRow = 1, cexCol = 1)

The `norm.coef`

attribute records the linear transformation applied to the
log2 read counts of each ChIP-seq sample as well as the number of common peak
regions between each sample and the baseline. The `MA.cor`

attribute is a
matrix recording the Pearson correlation coefficient (PCC) between M and A
values across the common peak regions of each pair of samples. The upper and
lower triangles of this matrix are calculated from raw and normalized log2
read counts, respectively. In general, it indicates a good normalization
performance that the M-A PCCs are all close to 0 after normalization.

We can also draw MA plots to visualize the normalization effects. Here we use the two biological replicates of GM12892 LCL as example:

# Before normalization. raw <- log(H3K27Ac[7:8] + 0.5, base = 2) MAplot(raw[[1]], raw[[2]], norm[[12]], norm[[13]], ylim = c(-2, 2), main = "Before normalization") abline(h = 0, lwd = 2, lty = 5) # After normalization. MAplot(norm[[7]], norm[[8]], norm[[12]], norm[[13]], ylim = c(-2, 2), main = "After normalization") abline(h = 0, lwd = 2, lty = 5)

In comparison to this one-step normalization, we prefer to adopt a hierarchical normalization process that takes advantage of the similarity structure among samples. Specifically, we first separately normalize the samples of each LCL:

# Within-group normalization. norm <- normalize(H3K27Ac, count = 5:6, occupancy = 10:11) norm <- normalize(norm, count = 7:8, occupancy = 12:13)

The key data type designed by MAnorm2 is `bioCond`

, which is for grouping
ChIP-seq samples belonging to the same biological condition. We next construct
a `bioCond`

object for each LCL to group its biological replicates, by
supplying their normalized signal intensities and occupancy states:

# Construct a bioCond for each LCL. conds <- list(GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"), GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892"))

**Note:** `bioCond`

objects do *not* design a data field to specifically record
the associated genomic intervals. The list and order of genomic intervals in a
`bioCond`

are the same as with the signal intensities for constructing it and
will never be changed. In principle, all the MAnorm2 functions accepting
multiple `bioCond`

objects expect them to be associated with the same list and
order of genomic intervals (e.g., `normBioCond()`

), and it is your job to make
sure of that. Note also that all the MAnorm2 functions applying a statistical
test to each individual genomic interval generate a table that is associated
with the same list and order of intervals as with the supplied `bioCond`

(s)
(e.g., `diffTest()`

).

We can summarize a `bioCond`

by

summary(conds$GM12891)

**Note:** as indicated in the summary, MAnorm2 defines the occupancy states of
genomic intervals in each `bioCond`

, which are determined by the number of
samples in the `bioCond`

occupying each interval (see the `occupy.num`

argument
of `bioCond()`

). The occupancy states of genomic intervals in `bioCond`

objects
matter for the following between-group normalization and mean-variance curve
(MVC) fitting. When the samples to be grouped into a `bioCond`

are biological
replicates for the same experiment, we recommend using the default setting,
which is `occupy.num = 1`

.

Finally, we normalize the resulting `bioCond`

objects to make them comparable
between each other:

# Between-group normalization. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) conds <- normBioCond(conds, common.peak.regions = autosome)

We can also draw MA plots on `bioCond`

objects to visualize the normalization
results:

MAplot(conds[[1]], conds[[2]], ylim = c(-12, 12), main = "GM12891 vs. GM12892") abline(h = 0, lwd = 2, lty = 5)

After normalization, MAnorm2 next models the relationship between mean signal intensities of genomic intervals and their variances by fitting a smooth mean-variance curve (MVC):

# Fit an MVC. # The "parametric" method sometimes requires the users to explicitly specify # initial coefficients. Try setting init.coef = c(0.1, 10) in these cases. conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE)

This function call associates an MVC with each `bioCond`

in `conds`

. After the
call, all `bioCond`

objects in `conds`

are associated with the same MVC, as
indicated by the MVC ID:

summary(conds$GM12891) summary(conds$GM12892)

**Note:** for each `bioCond`

that has been associated with an MVC, MAnorm2 uses
a variance ratio factor to quantify its global within-group variability
(relative to the MVC). When samples in each `bioCond`

are biological
replicates, the `bioCond`

objects associated with the same MVC should have
similar variance ratio factors. Otherwise, there might be batch effects and/or
samples with bad quality.

To improve the unbiasedness of MVC fitting, MAnorm2 calculates observed means
and variances of genomic intervals separately for each `bioCond`

, and it pools
the resulting mean-variance pairs from different `bioCond`

objects (after
adjusting for different global within-group variability) into a regression
process. Currently, we provide two candidate methods for the regression
process, which are `"parametric fit"`

and `"local regression"`

. We also design
the argument `occupy.only`

to control whether to use all genomic intervals or
only the occupied ones from each `bioCond`

for the regression process. In cases
where the samples in each `bioCond`

are biological replicates, the underlying
variance structure could be expected to be very regular, and we recommend using
the `"parametric fit"`

method, with setting `occupy.only`

to `TRUE`

to further
enhance the data regularity. See the section of
Combining Replicates and Using Local Regression below
for an application scenario of local regression.

The number of prior degrees of freedom is used to assess the overall goodness of fit of the associated MVC. You can also visualize the mean-variance trend along with the MVC by

# Plot only occupied genomic intervals, # as only these intervals have been used to fit the MVC. plotMeanVarCurve(conds, subset = "occupied", ylim = c(-7, 0.5))

In practice, number of prior degrees of freedom amounts to the number of
additional samples gained by borrowing information between genomic intervals,
and it should be large (relative to the number of *real* samples) when samples
in each `bioCond`

are biological replicates.

Finally, we call genomic intervals with differential signal intensities between
the two `bioCond`

objects by

res <- diffTest(conds[[1]], conds[[2]])

This function call performs a statistical test separately for each genomic
interval, with the null hypothesis that the interval is non-differential
between the supplied two `bioCond`

objects. It returns a data frame that
records the test results for each interval by each row:

```
head(res)
```

In this data frame, `Mval`

could be interpreted as log2 fold change; `pval`

assesses the statistical significance of each test; `padj`

refers to adjusted
*p*-value with the `"BH"`

method.

You can visualize the overall test results by

MAplot(res, padj = 0.001) abline(h = 0, lwd = 2, lty = 5, col = "green3")

We can see from this figure that differential ChIP-seq signals could be abundant even between very similar cellular contexts.

MAnorm2 compares two individual ChIP-seq samples by treating them as replicates and fitting an MVC based on their common peak regions. This strategy is basically the same as used by DESeq [@DESeq]. Here we give the standard workflow for comparing two individual ChIP-seq samples. We use the comparison of the first replicates of GM12891 and GM12892 LCLs as example:

# Perform normalization and create bioConds to represent the two LCLs. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) norm <- normalize(H3K27Ac, c(5, 7), c(10, 12), common.peak.regions = autosome) conds <- list(GM12891 = bioCond(norm[5], norm[10], name = "GM12891"), GM12892 = bioCond(norm[7], norm[12], name = "GM12892")) # Create a "blind" bioCond that treats the two samples as replicates and fit an # MVC accordingly. Only common peak regions of the two samples are considered # to be occupied by the "blind" bioCond, and only these regions are used to fit # the MVC. This setting is for capturing underlying non-differential intervals # as accurately as possible and avoiding over-estimation of prior variances # (i.e., variances read from MVC). conds$blind <- bioCond(norm[c(5, 7)], norm[c(10, 12)], occupy.num = 2, name = "blind") conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE, init.coef = c(0.1, 10)) # Note the dramatic decrease of number of prior degrees of freedom. summary(conds$blind) # Visualize mean-variance trend along with the fitted MVC. plotMeanVarCurve(conds[3], subset = "occupied", ylim = c(-7, 1)) # Perform differential tests. res2 <- diffTest(conds[[1]], conds[[2]]) head(res2) # Visualize the overall test results. # We use a cutoff of raw p-value here to select significant intervals. MAplot(res2, pval = 0.01) abline(h = 0, lwd = 2, lty = 5, col = "green3")

We can see from the last figure a dramatic decrease of statistical power for
identifying differential genomic intervals, owing to the lack of *true*
replicates. If you rank intervals in order of statistical significance,
however, you will find that this differential analysis without replicates
lead to very similar rankings to those from the previous analysis with
replicates:

cor(res$pval, res2$pval, method = "spearman") plot(-log10(res$pval), -log10(res2$pval), col = "#0000FF14", pch = 20, xlab = "With Reps", ylab = "Without Reps")

MAnorm2 can also be used to simultaneously compare more than two groups of ChIP-seq samples. Here we give the standard workflow for the cases where at least one of the groups to be compared contains two or more samples. We use the comparison of H3K27Ac ChIP-seq signals among GM12890, GM12891 and GM12892 LCLs as example:

# Perform within-group normalization. norm <- normalize(H3K27Ac, count = 4, occupancy = 9) norm <- normalize(norm, count = 5:6, occupancy = 10:11) norm <- normalize(norm, count = 7:8, occupancy = 12:13) # Construct a bioCond for each group of samples. conds <- list(GM12890 = bioCond(norm[4], norm[9], name = "GM12890"), GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"), GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892")) # Perform between-group normalization. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) conds <- normBioCond(conds, common.peak.regions = autosome) # Fit an MVC. conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE) # Perform differential tests. res <- aovBioCond(conds) head(res)

We can see that this workflow is basically the same as that for two-group
comparisons, except the final procedure for performing differential tests
(`aovBioCond()`

is called instead of `diffTest()`

).

You can visualize the overall test results by

plot(res, padj = 1e-6)

In cases where each group contains a single ChIP-seq sample, try the strategy
of constructing "blind" `bioCond`

objects (see the section of
When No Replicates Are Available) or calling hypervariable
ChIP-seq signals across the samples (see the section of
Identifying Hypervariable ChIP-seq Signals below).

In practice, chances are that you want to combine biological replicates to get a reference ChIP-seq profile for each biological condition. For example, with ChIP-seq samples for tissues or cells obtained from different individuals, you can classify the individuals according to the age, gender, health status or disease subtype of each of them, and then perform a differential analysis between groups of individuals to identify differential ChIP-seq signals associated with the group characteristics. Suppose that each individual is associated with multiple biological replicates. A reasonable analysis strategy is to separately create a reference profile for each individual by combining the associated biological replicates.

Here we use the comparison of H3K27Ac ChIP-seq signals between male and female LCLs as example to demonstrate how to use MAnorm2 to perform such analyses:

# Use the regular routine for normalization and construction of bioConds. norm <- normalize(H3K27Ac, count = 4, occupancy = 9) norm <- normalize(norm, count = 5:6, occupancy = 10:11) norm <- normalize(norm, count = 7:8, occupancy = 12:13) conds <- list(GM12890 = bioCond(norm[4], norm[9], name = "GM12890"), GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"), GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892")) autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) conds <- normBioCond(conds, common.peak.regions = autosome) # Group LCLs into different genders. genders <- list(male = cmbBioCond(conds[2], name = "male"), female = cmbBioCond(conds[c(1, 3)], name = "female")) # Fit an MVC by using local regression. genders <- fitMeanVarCurve(genders, method = "local", occupy.only = TRUE) summary(genders$female) plotMeanVarCurve(genders, subset = "occupied") # Perform differential tests. res <- diffTest(genders[[1]], genders[[2]]) head(res) MAplot(res, pval = 0.01) abline(h = 0, lwd = 2, lty = 5, col = "green3")

**Note:** `cmbBioCond()`

is designed for combining a list of `bioCond`

objects
into a single `bioCond`

, such that each of the supplied `bioCond`

objects
serves as an individual ChIP-seq sample in the combined `bioCond`

. Technically,
the function integrates the ChIP-seq samples contained in each `bioCond`

into a
reference ChIP-seq profile and groups the resulting profiles into a new
`bioCond`

object. Note that the argument list of `bioCond`

objects to
`cmbBioCond()`

must be normalized to be comparable with each other before
calling the function.

Here we use local regression to adaptively capture the mean-variance trend, as the dependence of ChIP-seq signal variability across individual LCLs on mean signal intensity is not as regular as in the previous case for modeling the variability across biological replicates. The above settings for employing local regression should be flexible enough to handle most practical cases. The following are some considerations regarding advanced usage of local regression.

In practice, good chances are that the underlying extrapolation algorithm of local regression results in overestimated prior variances for non-occupied genomic intervals. Naturally, performing the regression on all genomic intervals (rather than only occupied intervals) can avoid the problem:

genders2 <- fitMeanVarCurve(genders, method = "local", occupy.only = FALSE) plotMeanVarCurve(genders, subset = "non-occupied", main = "Use occupied intervals") plotMeanVarCurve(genders2, subset = "non-occupied", main = "Use all intervals")

However, using all genomic intervals to fit MVC may considerably reduce the estimated number of prior degrees of freedom as well as the statistical power for identifying differential intervals, owing to the fact that ChIP-seq signal measurements in non-occupied intervals are generally of less data regularity compared with those in occupied intervals:

genders[[1]]$fit.info$df.prior genders2[[1]]$fit.info$df.prior

To split the difference, you can perform local regression on all genomic intervals and re-estimate the number of prior degrees of freedom using only occupied intervals:

genders3 <- estimatePriorDf(genders2, occupy.only = TRUE) plotMeanVarCurve(genders3, subset = "non-occupied", main = "Re-estimate prior df") genders3[[1]]$fit.info$df.prior

**Note:** in fact, when calling `fitMeanVarCurve()`

to fit an MVC,
`estimatePriorDf()`

is automatically invoked for the associated parameter
estimation. There is also a *robust* version of `estimatePriorDf()`

, named
`estimatePriorDfRobust()`

. It renders the estimation procedure robust to
potential outliers by applying the Winsorization technique [@robustLimma].
Type `?estimatePriorDfRobust`

for details.

Since MAnorm2 1.1.0, one of the new changes is the implementation of
HyperChIP, which is a method developed for identifying genomic intervals
with hypervariable ChIP-seq signals across a set of samples. Compared with
the old workflow shown in the vignette for MAnorm2 1.0.0,
HyperChIP has made specific efforts to increase the statistical power for
identifying hypervariable intervals (refer to `?estParamHyperChIP`

for
details). Here we use all the H3K27Ac ChIP-seq
samples as an example to demonstrate the standard workflow of HyperChIP:

# Normalize all ChIP-seq samples once for all. # Considering the number of samples in a hypervariable ChIP-seq analysis is # typically large, HyperChIP uses a pseudo-reference profile as baseline in the # MA normalization process to reduce the variability of normalization results. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) norm <- normalize(H3K27Ac, count = 4:8, occupancy = 9:13, baseline = "pseudo-reference", common.peak.regions = autosome) # Construct a bioCond to group all the samples. cond <- bioCond(norm[4:8], norm[9:13], occupy.num = 1, name = "all") # Fit an MVC by using local regression. # Set "nn = 1" to increase the smoothness of the resulting MVC. cond <- fitMeanVarCurve(list(cond), method = "local", occupy.only = TRUE, args.lp = list(nn = 1))[[1]] summary(cond) # Apply the parameter estimation framework of HyperChIP. # Note the dramatic increase in the estimated number of prior degrees of # freedom. cond <- estParamHyperChIP(cond) summary(cond) # Perform statistical tests. res <- varTestBioCond(cond) head(res)

The `fold.change`

variable gives the ratio of the observed variance of each
genomic interval to its prior variance. Note that the `pval`

variable gives
*two-sided* *p*-values. Therefore, for the genomic intervals with small
*p*-values, those associated with a `fold.change`

larger than 1 suggest
hypervariable ChIP-seq signals across samples, and the others suggest lowly
variable or so-called invariant ChIP-seq signals:

# Visualize the overall test results. hist(res$pval, breaks = 100, col = "red") plot(res, padj = 0.01)

You can get one-sided *p*-values for identifying hypervariable ChIP-seq
signals by

df <- attr(res, "df") df one.sided.pval <- pf(res$fold.change, df[1], df[2], lower.tail = FALSE)

Compared with the genomic intervals occupied by all the ChIP-seq samples, those
intervals occupied only by part of the samples should be associated with higher
signal variability and, thus, tend to have more significant *p*-values:

n <- rowSums(norm[9:13]) x <- list(All = -log10(one.sided.pval[n == 5]), Partially = -log10(one.sided.pval[n > 0 & n < 5])) wilcox.test(x$All, x$Partially, alternative = "less") boxplot(x, ylab = "-Log10(p-value)") boxplot(x, ylab = "-Log10(p-value)", outline = FALSE)

In practice, you can also call hypervariable signals across groups of ChIP-seq
samples, by first using `cmbBioCond()`

to integrate the samples of each group
into a reference profile (see also the section of
Combining Replicates and Using Local Regression).

Here we give the standard workflow for performing hierarchical clustering on a set of ChIP-seq samples. We use all the H3K27Ac ChIP-seq samples as an example:

# Normalize all ChIP-seq samples once for all. autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) norm <- normalize(H3K27Ac, count = 4:8, occupancy = 9:13, baseline = "pseudo-reference", common.peak.regions = autosome) # Construct a bioCond to group all the samples. cond <- bioCond(norm[4:8], norm[9:13], occupy.num = 1, name = "all") # Fit an MVC by using local regression. cond <- fitMeanVarCurve(list(cond), method = "local", occupy.only = TRUE, args.lp = list(nn = 1))[[1]] # Measure the distance between each pair of samples. d <- distBioCond(cond, method = "prior") d # Perform hierarchical clustering. plot(hclust(d, method = "average"), hang = -1)

**Note:** `distBioCond()`

quantifies the distance between each pair of samples
contained in the supplied `bioCond`

. Each distance derived by `distBioCond()`

could be interpreted as *absolute* difference in signal intensity
between two samples averaged across genomic intervals.
In the above example, the average fold change of
H3K27Ac ChIP-seq signal between the 1st and 2nd replicates of GM12891 is about
$2^{0.2790962} \approx 1.2$, while the average fold change between the 1st
replicates of GM12891 and GM12892 is about $2^{0.8673543} \approx 1.8$.
Technically, `distBioCond()`

calculates a *p*-norm distance for each pair of
samples while using the reciprocal of variance to weight each genomic interval.
Suppose $x_{i}$ and $y_{i}$ represent the signal intensities of interval $i$ in
two samples. $w_{i}$ is the reciprocal of the variance of interval $i$. The
function derives the distance between the two samples by

$$ d = \sqrt[p]{\frac{ \sum_{i}w_{i}|y_{i} - x_{i}|^{p} }{ \sum_{i}w_{i} }} $$

By default, `distBioCond()`

uses $p=2$ and the `"prior"`

method to calculate
the variance of each interval (type `?distBioCond`

for details).

In practice, you may want to use only the genomic intervals that are associated with hypervariable signal intensities across samples to perform clustering, as such intervals should be most helpful for distinguishing between samples:

# Select hypervariable genomic intervals. cond <- estParamHyperChIP(cond) res <- varTestBioCond(cond) f <- res$fold.change > 1 & res$padj < 0.01 # The hierarchical structure among samples remains unmodified, # but note the change of magnitude of the distances between cell lines. d2 <- distBioCond(cond, subset = f, method = "prior") d2 plot(hclust(d2, method = "average"), hang = -1)

You can also perform hierarchical clustering on groups of ChIP-seq
samples, by first using `cmbBioCond()`

to integrate the samples of
each group into a reference profile (see also the section of
Combining Replicates and Using Local Regression).

Here we provide a formal description of the statistical model designed in MAnorm2. Suppose $X_{j}$ is an $n \times m_{j}$ matrix recording normalized ChIP-seq signal intensities (by default, normalized signal intensities derived by MAnorm2 are normalized log2 read counts) at $n$ genomic intervals for $m_{j}$ samples belonging to group $j$. Let $X_{i,j}$ be a column vector representing the transpose of row $i$ of $X_{j}$. We assume

$$ X_{i,j}|\sigma^{2}*{i,j} \sim
MVN(1\mu*{i,j}, (\gamma_{j}\sigma^{2}*{i,j})S*{i,j}) \
\frac{1}{\sigma^{2}*{i,j}} \sim
\frac{1}{f(\mu*{i,j})} \cdot \frac{\chi^{2}*{d*{0}}}{d_{0}} $$

Here $MVN$ refers to the multivariate normal distribution. $\mu_{i,j}$ and
$\sigma^{2}*{i,j}$ are two unknown scalars that parametrize the mean signal
intensity of interval $i$ in group $j$ and the associated signal variability,
respectively. $1$ is a column vector of ones. $\gamma*{j}$, termed variance
ratio factor, is a scalar that quantifies the global within-group variability
of group $j$. $S_{i,j}$, termed structure matrix, is an $m_{j} \times m_{j}$
matrix designed for modeling precision weights of signal measurements from
different samples as well as correlations among the measurements (by default,
MAnorm2 simply sets each structure matrix to an identity matrix). $f(\cdot)$
refers to an *unscaled* MVC common to different groups of samples and
$f(\mu_{i,j})$ is called the prior variance of interval $i$ in group $j$.
$d_{0}$, termed number of prior degrees of freedom, is a hyperparameter that
assesses how well in general the variance of an individual interval could be
predicted by its mean signal intensity. $\chi^{2}*{d*{0}}$ refers to the
chi-squared distribution with $d_{0}$ degrees of freedom. For the convenience
of devising statistical tests for identifying differential genomic intervals
between groups of samples, we further assume that *unscaled* variances of
non-differential intervals remain invariant across groups. Formally, we assume
that $\sigma^{2}*{i,j*{1}}$ equals $\sigma^{2}*{i,j*{2}}$ with a probability of
one (i.e., they refer to the same random variable) whenever
$\mu_{i,j_{1}} = \mu_{i,j_{2}}$. This assumption is consistent with the fact
that $\sigma^{2}*{i,j*{1}}$ and $\sigma^{2}*{i,j*{2}}$ follow the same prior
distribution on condition that $\mu_{i,j_{1}} = \mu_{i,j_{2}}$.

Overall, the above model is similar to limma-trend [@limmaTrend; @voom], except that MAnorm2 allows for different global within-group variability between groups of samples.

To cite the MAnorm2 package in publications, please use

Tu, S., et al.,

MAnorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res, 2021.31(1): p. 131-145.

If you have performed MA normalization with a pseudo-reference profile as baseline, or have employed a Winsorization-based robust parameter estimation framework, or have performed a hypervariable analysis, please cite additionally

Chen, H., et al.,

HyperChIP for identifying hypervariable signals across ChIP/ATAC-seq samples. bioRxiv, 2021: p. 2021.07.27.453915.

In devising the underlying statistical methods of MAnorm2, we have learned a lot from limma, limma-trend, voom, DESeq and DESeq2 [@limma; @limmaTrend; @voom; @DESeq; @DESeq2]. Special thanks to the authors of these fantastic tools.

We would also like to sincerely thank the following individuals, for their help and feedback on the MAnorm2 package:

Zhen Shao, Yijing Zhang, Mushan Li, Haojie Chen, Fengxiang Tan.

```
sessionInfo()
```

```
options(old.ops)
```

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