TFHAZ"

knitr::opts_chunk$set(echo = TRUE)
library(TFHAZ)
library(GenomicRanges)
library(IRanges)
library(S4Vectors)
data("data_man")

Introduction

Transcription factors (TFs) are proteins that bind the DNA in specific regions and regulate gene expression. The regolation of the gene expression is often controlled by many TFs interacting with each other. Recent high throughput methods like chromatin immunoprecipitation followed by sequencing (ChIP-seq) [@park2009chip] provide a large number of data regarding TF binding regions, which are available in public repositories such as ENCODE [@encode2004encode] or Roadmap Epigenomics [@bernstein2010nih].
Starting from a dataset containing the genomic positions of TF binding regions, the TFHAZ package allows finding trascription factor high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. In addition, some functions are provided in order to analyze and compare results obtained with different input parameters.

Dataset

Transcription factor dense DNA zones are found from a GRanges object that contains genomic regions of transcription factors at the ranges side and the name of the transcription factors at the metadata side. As in every object of GRanges class, the genomic coordinates are located on the left-hand side. The first column of the ranges side contains the chromosome of each region; the second column contains the genomic coordinates of each region (starting and ending position of the transcription factor binding region, considering a 1-based inclusive coordinate system); the third column contains the strand of each region ("+", "-", or "*" if unknown).
The dataset we consider (called Ishikawa) is obtained from computation of ENCODE ChIP-Seq data of the localization of transcription factor binding regions for the Ishikawa cell line. The data have been processed and extracted with GMQL (GenoMetric Query Language http://www.bioinformatics.deib.polimi.it/GMQL/) [@masseroli2015genometric] [@ceri2016data] [@masseroli2016modeling]. The Ishikawa dataset contains 283,009 ranges of 16 different transcription factors.

# load and visualize the dataset:
data("Ishikawa")
dim(as.data.frame(Ishikawa))
head(Ishikawa)

Transcription factor accumulation

The first step in finding transcription factor dense DNA zones is to count the accumulation of TFs for each chromosome base. The function accumulation in the TFHAZ package creates a vector in which, for each chromosome base, the accumulation of the TFs present in the input dataset is calculated. We considered three types of accumulation: TF accumulation, region accumulation and base accumulation.

TF accumulation: for each base, it is the number of different TFs present in the neighborhood of the considered base. The neighborhood is defined by a window with half-width w centered on the considered base.

Region accumulation: for each base, it is the number of regions containing TFs in the neighborhood of the considered base. If in the neighborhood of a base there are two input binding regions of the same TF, the accumulation value in that base is equal to 2 (differently from the TF accumulation, whose value in the same case is equal to 1).

Base accumulation: for each base, it is the total number of bases belonging to input regions containing TFs in the neighborhood of the considered base.

With w = 0, a single base approach is applied (no base neighborhood is considered). In this case, if in the input dataset overlapping regions for the same TF and chromosome do not exist, the results of TF, region and base accumulation are equal.

The function accumulation takes in input:

The result of the accumulation function is a list containing:

The accumulation vector obtained can be plotted using the function plot_accumulation.
This function takes in input the output of the accumulation function and saves the plot in a .png file. If the accumulation input is found with chr = "all", the chromosomes (one or more) to be considered can be chosen by the "chr" argument.

# calculate TF accumulation for the chromosome 21 for w=0
TF_acc_21_w_0 <- accumulation(Ishikawa, "TF", "chr21", 0)
# plot the accumulation vector
plot_accumulation(TF_acc_21_w_0)
knitr::include_graphics('./TF_w_0_chr21.png')

As we can see in Figure 1, in this example considering no base neighborhood (w=0), the maximum value of accumulation found is 14. It means that in the bases with that accumulation value there are overlapping binding regions of 14 out of the 16 transcription factors present in the dataset.

Transcription factor dense DNA zones

Once the accumulation for each chromosome base is calculated, we can find transcription factor dense zones with the dense_zones function. For each accumulation threshold value defined, the function finds transcription factor dense DNA zones (regions). Starting from the accumulation vector calculated with the accumulation function, each dense zone is formed by contiguous bases with accumulation equal or higher than the threshold. Threshold values are selected by setting the threshold_step parameter.
For each accumulation threshold value, a ".bed" file with the chromosome and genomic coordinates of the dense zones found can be created (Figure 2). The function finds also the number of dense zones, the number of total bases belonging to the dense zones, the minimum, maximum, mean, median and standard deviation of the dense zone lengths and of the distances between adjacent dense zones.

The function dense_zones takes in input:

The result of the dense_zones function is a list containing:

# find dense DNA zones, with threshold step equal to 1
TF_dense_21_w_0 <- dense_zones(TF_acc_w_0, 1, chr = "chr21")
TF_dense_21_w_0
knitr::include_graphics('./dense_zones_12.PNG')

We can plot the results present in the zones_count data frame with the function plot_n_zones (Figure 3) and we can see how the number of dense DNA zones decreases as the accumulation threshold value increases. The function also plots the point of the graph with maximum slope change, corresponding to the maximum second derivative of the curve, circulating it with a red full line.

plot_n_zones(TF_dense_w_0, chr = "chr21")

\newpage

Transcription factor dense DNA zones analysis

After finding transcription factor dense DNA zones, we can use two functions of the TFHAZ package to compare the results obtained with different values of w half-width of the base neighborhood window and different accumulation types.

With the function w_analysis we can plot the number of dense zones and the total number of bases belonging to these dense zones present in a set of inputs, obtained (all with accumulation threshold=1) using the dense_zones function, for the same accumulation type, same chromosome, and different values of w half-width of the window defining the neighborhood of each base. The function takes in input a list of multiple outputs of the dense_zones function and returns a plot (with x axis logarithmic-scale).

# l is a list with four objects obtained with the dense_zones function with 
# w = 10, 100, 1000, 10000.
l <- list(TF_dense_w_10, TF_dense_w_100, TF_dense_w_1000, TF_dense_w_10000)
# plot
w_analysis(l, chr = "chr21")

If we consider the four different values of w half-width of the base neighborhood window in the example, we can notice in Figure 4 that the two measures (i.e., the number of dense zones and the total number of bases belonging to these dense zones) are inversely correlated; the number of bases increases with the size of the neighborhood, while the number of dense zones decreases. Furthermore, observing the plot, we can notice that the highest increase or decrease of the two measures occurs when the half-width of the neighborhood assumes values higher than 1000 bases. So, w=1000 can be considered a good value in finding dense zones when using an accumulation approach with neighborhood (w different from 0). It is worth noting that the calculation time of the accumulation vector increases considerably with higher values of w.

In order to understand how to integrate the results obtained with the three different accumulation types, using the function n_zones_PCA we can perform the Principal Component Analysis (PCA) [@johnson2014applied] [@bro2014principal] of the number of dense zones obtained by varying the threshold on accumulation values obtained with the three methods of accumulation (TF, region, base).

The Principal Component Analysis produces a low dimensional representation of a dataset, finding a linear sequence of linear combinations of the variables that have maximal variance. With PCA we want to find if there is a possible way to combine the three measures (TF, region, base accumulation), or if the information obtained is the same, and we can use only one of these measures for our study. For this purpose it is useful to observe the variance associated with the first principal component and the loadings, the coefficients of the linear combination of each principal component, that explain the proportion of each variable along each principal component.

This function takes in input:

The outputs of the function are:

Note that the function n_zones_PCA works only if the number of different threshold values used to find the dense zones with the dense_zones function is the same for all the three accumulation types, while the threshold values can be different.

# TF_dense_21_w_10 is the output of dense_zones function applied to the
# accumulation vector found with w=10, chr="chr21", acctype="TF".
# reg_dense_21_w_10 is the output of dense_zones function applied to the
# accumulation vector found with w=10, chr="chr21", acctype="reg".   
# base_dense_21_w_10 is the output of dense_zones function (with 
# threshold_step=21 in order to have 14 threshold values as in the other two
# inputs) applied to the accumulation vector found with w=10, chr="chr21",
# acctype="base".
# PCA
n_zones_PCA(TF_dense_w_10, reg_dense_w_10, base_dense_w_10, chr = "chr21")
knitr::include_graphics('./PCA_1.png')
knitr::include_graphics('./PCA_2.png')
knitr::include_graphics('./PCA_3.png')

From this example we can see how the first principal component explains most of the variation (Figure 5 and Figure 6), so it accounts for maximum information. Furthermore, we can see how the values of loadings of the first principal component are very similar (Figure 7). Therefore, we can say that the information obtained with the three methods of accumulation is the same and for our study regarding transcription factor dense DNA zones we can use only one method of the three; we suggest using the TF or region accumulation because the running time of the accumulation function with acctype=base is higher, especially in chromosomes with an elevated number of input regions.

Transcription factor high accumulation DNA zones

In the previous part we found transcription factor dense DNA zones with different thresholds of transcription factor accumulation, and we compared the results using different input parameters in order to identify the best way to find regions of the genome where there is a high presence of different trascription factors. Now, with the function high_accumulation_zones, setting only one threshold value, we can find these regions; we call them transcription factor high accumulation DNA zones (TFHAZ). Starting from the accumulation vector calculated with the accumulation function, two different methods for the search of TF high accumulation DNA zones are available. The binding regions method is based on the identification of DNA regions with presence of TF binding (at least one TF) from which those with a high number of different TFs (above the threshold) are selected. This method works only if the accumulation vector is found with w=0. The overlaps method is the method used also in dense_zones function. It uses a single base local approach, identifying DNA bases, that form the dense zones, in which there is high overlap of TFs. For the binding regions method the high accumulation zones are the accumulation regions with values higher or equal to the threshold, while in the overlaps these zones are defined as sets of contiguous bases with accumulation value higher or equal to the considered threshold. The threshold value is found considering two methods. The std method considers all and only the bases of the accumulation vector (accvector) with values higher than zero, and the threshold is found with the following formula: TH = mean(accvector) + 2 std(accvector). The top_perc method considers the accumulation regions and selects those in the top x percentage, with x chosen by the user through the perc argument. The function finds also the number of high accumulation zones, the number of total bases belonging to these zones, the minimum, maximum, mean, median and standard deviation of these zone lengths and of the distances between adjacent high accumulation zones. In the case of binding regions method, it is needed to include the data input argument, that is the GRanges object used in the accumulation function. Furhermore, in the case of single chromosome accumulation vector, the function can plot, for each chromosome base (x axis), the value of accumulation (y axis) calculated with the accumulation function. On this graph there are also shown the threshold (with a red line) and, on the x axis, the bases belonging to the high accumulation zones (with red boxes). The plot can be saved in a ".png" file. The function also can generate a ".bed" file with the chromosome and genomic coordinates of the high accumulation zones found.

The function high_accumulation_zones takes in input:

The result of the high_accumulation_zones function is a list containing:

# find high accumulation DNA zones
TF_acc_21_w_0 <- accumulation(Ishikawa, "TF", "chr21", 0)
TFHAZ_21_w_0 <- high_accumulation_zones(TF_acc_21_w_0, method = "overlaps",
threshold = "std")
TFHAZ_21_w_0

From the results of this example we can see that for the overlaps method with a threshold equal to 7.3 (obtained with the std method) we find 93 high accumulation zones. We can see the distribution of these zones along the chromosome (in this case the chromosome 21) in Figure 8, while in Figure 9 it is shown a part (31 out of 93 zones) of the ".bed" file with the coordinates of the zones.

knitr::include_graphics('./high_accumulation_zones_TH_7.3_TF_acc_w_0_chr21.png')
knitr::include_graphics('./HAZ_bed.PNG')

Acknowledgement

We really appreciate the generous support and suggestions by Stefano Campaner and Stefano Perna.

References



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TFHAZ documentation built on Nov. 8, 2020, 5:05 p.m.