metagene: a package to produce metagene plots
26 October 2021
Contents
Package: metagene
Modified: 18 september, 2015
Compiled: Tue Oct 26 17:51:11 2021
License: Artistic-2.0 | file LICENSE
1 Introduction
This package produces metagene-like plots to compare the behavior of DNA-interacting proteins at selected groups of features. A typical analysis can be done in viscinity of transcription start sites (TSS) of genes or at any regions of interest (such as enhancers). Multiple combinations of group of features and/or group of bam files can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. In order to increase the sensitivity of the analysis, alignment data is used instead of peaks produced with peak callers (i.e.: MACS2 or PICS). The metagene package uses bootstrap to obtain a better estimation of the mean enrichment and the confidence interval for every group of samples.
This vignette will introduce all the main features of the metagene package.
2 Loading the metagene package
library(metagene)3 Inputs
3.1 Alignment files (BAM files)
There is no hard limit in the number of BAM files that can be included in an
analysis (but with too many BAM files, memory may become an issue). BAM files
must be indexed. For instance, if you use a file names file.bam, a file
named file.bam.bai or file.baimust be present in the same directory.
The path (relative or absolute) to the BAM files must be in a vector:
bam_files <- get_demo_bam_files()
bam_files## [1] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep1.bam"
## [2] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep2.bam"
## [3] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep1.bam"
## [4] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep2.bam"
## [5] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/ctrl.bam"For this demo, we have 2 samples (each with 2 replicates). It is also possible to use a named vector to add your own names to each BAM files:
named_bam_files <- bam_files
names(named_bam_files) <- letters[seq_along(bam_files)]
named_bam_files## a
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep1.bam"
## b
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep2.bam"
## c
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep1.bam"
## d
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep2.bam"
## e
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/ctrl.bam"Using named BAM files can simplify the use of the metagene helper functions and the creation of the design.
3.2 Genomic regions
3.2.1 BED files
To compare custom regions of interest, it is possible to use a list of one or more BED files.
regions <- get_demo_regions()
regions## [1] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/list1.bed"
## [2] "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/list2.bed"The name of the files (without the extension) will be used to name each groups.
metagene also support the
narrowPeak
and the broadPeak.
3.2.2 GRanges or GRangesList objects - Regions
As an alternative to a list of BED files, GRanges or GRangesList objects
can be used.
3.2.3 Available datasets
Some common datasets are already available with the metagene package:
promoters_hg19promoters_hg18promoters_mm10promoters_mm9
data(promoters_hg19)
promoters_hg19## GRanges object with 23056 ranges and 1 metadata column:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## 1 chr19 58873215-58875214 - | 1
## 10 chr8 18247755-18249754 + | 10
## 100 chr20 43279377-43281376 - | 100
## 1000 chr18 25756446-25758445 - | 1000
## 10000 chr1 244005887-244007886 - | 10000
## ... ... ... ... . ...
## 9991 chr9 115094945-115096944 - | 9991
## 9992 chr21 35735323-35737322 + | 9992
## 9993 chr22 19108968-19110967 - | 9993
## 9994 chr6 90538619-90540618 + | 9994
## 9997 chr22 50963906-50965905 - | 9997
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genomeFor more details about each datasets, please refer to their documentation
(i.e.:?promoters_hg19).
3.3 Design groups
A design group contains a set of BAM files that, when put together, represent a logical analysis. Furthermore, a design group contains the relationship between every BAM files present. Samples (with or without replicates) and controls can be assigned to a same design group. There can be as many groups as necessary. A BAM file can be assigned to more than one group.
To represent the relationship between every BAM files, design groups must have the following columns:
- The list of paths to every BAM files related to an analysis.
- One column per group of files (replicates and/or controls).
There is two possible way to create design groups, by reading a file or by directly creating a design object in R.
3.3.1 Design groups from a file
Design groups can be loaded into the metagene package by using a text file. As the relationship between BAM files as to be specified, the following columns are mandatory:
- First column: The list of paths (absolute or relative) to every BAM files for all the design groups, the BAM filenames or the BAM names (if a named BAM. file was used).
- Following columns: One column per design group (replicates and/or controls).
The column can take only three values:
- 0: ignore file
- 1: input
- 2: control
The file must also contain a header. It is recommanded to use Samples for the
name of the first column, but the value is not checked. The other columns in
the design file will be used for naming design groups, and must be unique.
fileDesign <- system.file("extdata/design.txt", package="metagene")
design <- read.table(fileDesign, header=TRUE, stringsAsFactors=FALSE)
design$Samples <- paste(system.file("extdata", package="metagene"),
design$Samples, sep="/")
kable(design)| Samples | align1 | align2 |
|---|---|---|
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep1.bam | 1 | 0 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep2.bam | 1 | 0 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep1.bam | 0 | 1 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep2.bam | 0 | 1 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/ctrl.bam | 2 | 2 |
3.3.2 Design groups from R
It is not obligatory to use a design file, you can create the design
data.frame using your prefered method (as long as the restrictions on the
values mentioned previously are respected).
For instance, the previous design data.frame could have been create directly in R:
design <- data.frame(Samples = c("align1_rep1.bam", "align1_rep2.bam",
"align2_rep1.bam", "align2_rep2.bam", "ctrl.bam"),
align1 = c(1,1,0,0,2), align2 = c(0,0,1,1,2))
design$Samples <- paste0(system.file("extdata", package="metagene"), "/",
design$Samples)
kable(design)| Samples | align1 | align2 |
|---|---|---|
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep1.bam | 1 | 0 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep2.bam | 1 | 0 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep1.bam | 0 | 1 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep2.bam | 0 | 1 |
| /tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/ctrl.bam | 2 | 2 |
4 Analysis steps
A typical metagene analysis will consist steps:
- Extraction the read count of every BAM files in selected regions.
- Conversion in coverage.
- Noise removal
- Normalization of the coverage values.
- Table production.
- Data frame production.
- Generation of the metagene plot.
4.1 Minimal analysis
A minimal metagene analysis can be performed in 2 steps:
- Initialization (the
newfunction). plot
regions <- get_demo_regions()
bam_files <- get_demo_bam_files()
# Initialization
mg <- metagene$new(regions = regions, bam_files = bam_files)
# Plotting
mg$plot(title = "Demo metagene plot")## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
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## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
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## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## produce data table : ChIP-Seq## produce data frame : ChIP-Seq## Plot : ChIP-Seq
As you can see, it is not mandatory to explicitly call each step of the
metagene analysis. For instance, in the previous example, the plot function
call the other steps automatically with default values (the next section will
describe the steps in more details).
In this specific case, the plot is messy since by default metagene will produce a curve for each possible combinations of BAM file and regions. Since we have 5 BAM files and 2 regions, this gives us 10 curves.
If we want more control on how every step of the analysis are performed, we have to call each functions directly.
4.2 Complete analysis
In order to fully control every step of a metagene analysis, it is important to understand how a complete analysis is performed. If we are satisfied with the default values, it is not mandatory to explicitly call every step (as was shown in the previous section).
4.2.1 Initialization
During this step, the coverages for every regions specified are extracted from
every BAM files. More specifically, a new GRanges is created by combining
all the regions specified with the regions param of the new function.
regions <- get_demo_regions()
bam_files <- get_demo_bam_files()
mg <- metagene$new(regions = regions, bam_files = bam_files)4.2.2 Producing the table
To produce the table, coverages (produced from Genomics regions (.BED),
Alignment Files (.BAM) and Design Sheet) was treated for noise removal
and normalized. Furthermore, to reduce the computation time during the
following steps, the positions are also binned. Regions, designs, bins,
associated values and orientation of strands are pulled into a data.table
called ‘table’ and accessible thanks to the getter get_table.
We can control the size of the bins with the bin_count argument. By
default, a bin_count of 100 will be used during this step.
mg$produce_table()## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
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## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## produce data table : ChIP-SeqWe can also use the design we produced earlier to remove background signal and combine replicates:
mg$produce_table(design = design)## produce data table : ChIP-Seq4.2.3 Producing the data.frame
The metagene plot are produced using the ggplot2 package, which require a
data.frame as input. During this step, the values of the ribbon are
calculated. Metagene uses “bootstrap” to obtain a better estimation of the
mean of enrichment for every positions in each groups.
mg$produce_data_frame()## produce data frame : ChIP-Seq4.2.4 Plotting
During this step, metagene will use the data.frame to plot the calculated
values using ggplot2. We show a subset of the regions by using the
region_names and design_names parameter. The region_names correspond to
the names of the regions used during the initialization. The design_name
will vary depending if a design was added. If no design was added, this param
correspond to the BAM name or BAM filenames. Otherwise, we have to use the
names of the columns from the design.
mg$plot(region_names = "list1", title = "Demo plot subset")## Plot : ChIP-Seq
5 Manipulating the metagene objects
5.1 Getters
Multiple getters functions are available to access the data that is stored in a
metagene object.
5.1.1 get_table
To get the data.table containing regions, designs, bins, values at bins and orientation of strands.
mg <- get_demo_metagene()
mg$produce_table()## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
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## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
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## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
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## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
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## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## produce data table : ChIP-Seqmg$get_table()## region design bin value strand
## 1: list1 align1_rep1 1 0.00 *
## 2: list1 align1_rep1 2 0.75 *
## 3: list1 align1_rep1 3 1.55 *
## 4: list1 align1_rep1 4 1.15 *
## 5: list1 align1_rep1 5 1.25 *
## ---
## 49996: list2 ctrl 96 0.00 *
## 49997: list2 ctrl 97 0.00 *
## 49998: list2 ctrl 98 0.00 *
## 49999: list2 ctrl 99 0.00 *
## 50000: list2 ctrl 100 0.00 *5.1.2 get_matrices
To get the data.table as matrices (the former data structure)
mg <- get_demo_metagene()
mg$produce_table()## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## produce data table : ChIP-Seqm <- mg$get_matrices()
# m$list1$ctrl$input to access to region 'list1' and 'ctrl' design5.1.3 get_data_frame
get_data_frame = function(region_names = NULL, design_names = NULL) To get the data.frame containing regions and design
mg <- get_demo_metagene()
mg$produce_table()## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory## produce data table : ChIP-Seqmg$produce_data_frame()## produce data frame : ChIP-Seqmg$get_data_frame()## region design bin value strand qinf qsup
## 1 list1 align1_rep1 1 0.449 * 0.40389125 0.49641000
## 2 list1 align1_rep1 2 0.552 * 0.50051750 0.59348875
## 3 list1 align1_rep1 3 0.609 * 0.56056500 0.65235375
## 4 list1 align1_rep1 4 0.534 * 0.49530125 0.56960750
## 5 list1 align1_rep1 5 0.522 * 0.48168125 0.55173125
## 6 list1 align1_rep1 6 0.589 * 0.53649875 0.62987875
## 7 list1 align1_rep1 7 0.704 * 0.62104000 0.78509625
## 8 list1 align1_rep1 8 0.591 * 0.53386750 0.64785250
## 9 list1 align1_rep1 9 0.562 * 0.50988500 0.62804125
## 10 list1 align1_rep1 10 0.488 * 0.42359000 0.52955375
## 11 list1 align1_rep1 11 0.614 * 0.53790875 0.66312375
## 12 list1 align1_rep1 12 0.745 * 0.66899625 0.82375625
## 13 list1 align1_rep1 13 0.772 * 0.69582375 0.89005375
## 14 list1 align1_rep1 14 0.659 * 0.58507750 0.73414000
## 15 list1 align1_rep1 15 0.481 * 0.42106250 0.54137875
## 16 list1 align1_rep1 16 0.558 * 0.49834750 0.59976625
## 17 list1 align1_rep1 17 0.614 * 0.58325625 0.65911000
## 18 list1 align1_rep1 18 0.708 * 0.65959625 0.76522125
## 19 list1 align1_rep1 19 0.847 * 0.78655500 0.89679875
## 20 list1 align1_rep1 20 0.745 * 0.69748625 0.80137000
## 21 list1 align1_rep1 21 0.731 * 0.68653875 0.78321625
## 22 list1 align1_rep1 22 0.795 * 0.74367625 0.85661750
## 23 list1 align1_rep1 23 0.772 * 0.71957500 0.84727375
## 24 list1 align1_rep1 24 0.866 * 0.81559125 0.91089250
## 25 list1 align1_rep1 25 0.872 * 0.81473125 0.92716250
## 26 list1 align1_rep1 26 0.841 * 0.77317625 0.91011750
## 27 list1 align1_rep1 27 0.808 * 0.72176375 0.91511125
## 28 list1 align1_rep1 28 0.835 * 0.74404500 0.93892375
## 29 list1 align1_rep1 29 0.915 * 0.83195125 1.00812000
## 30 list1 align1_rep1 30 1.123 * 0.98233375 1.22158750
## 31 list1 align1_rep1 31 1.161 * 0.97092125 1.35507500
## 32 list1 align1_rep1 32 1.122 * 0.99403625 1.28539875
## 33 list1 align1_rep1 33 1.301 * 1.21117125 1.41383750
## 34 list1 align1_rep1 34 1.235 * 1.16792500 1.30738875
## 35 list1 align1_rep1 35 1.285 * 1.23034750 1.35925000
## 36 list1 align1_rep1 36 1.497 * 1.42875375 1.59344750
## 37 list1 align1_rep1 37 1.480 * 1.36782125 1.59926625
## 38 list1 align1_rep1 38 1.433 * 1.26657625 1.57765250
## 39 list1 align1_rep1 39 1.234 * 1.10722875 1.34003250
## 40 list1 align1_rep1 40 1.227 * 1.16212250 1.30104250
## 41 list1 align1_rep1 41 1.302 * 1.24238625 1.40626750
## 42 list1 align1_rep1 42 1.267 * 1.20139625 1.35129375
## 43 list1 align1_rep1 43 1.584 * 1.47926250 1.68300375
## 44 list1 align1_rep1 44 1.602 * 1.52523000 1.71748375
## 45 list1 align1_rep1 45 1.727 * 1.65834750 1.80530500
## 46 list1 align1_rep1 46 2.020 * 1.93436125 2.09916000
## 47 list1 align1_rep1 47 2.120 * 2.01922375 2.20953500
## 48 list1 align1_rep1 48 2.044 * 1.95932125 2.12469125
## 49 list1 align1_rep1 49 2.196 * 2.10698625 2.28035875
## 50 list1 align1_rep1 50 2.340 * 2.22612000 2.43783750
## 51 list1 align1_rep1 51 2.367 * 2.26802125 2.46291250
## 52 list1 align1_rep1 52 2.374 * 2.24342750 2.47921375
## 53 list1 align1_rep1 53 2.086 * 1.93328875 2.17821875
## 54 list1 align1_rep1 54 2.053 * 1.95652875 2.16636875
## 55 list1 align1_rep1 55 2.126 * 2.00412875 2.21342000
## 56 list1 align1_rep1 56 1.710 * 1.57795125 1.80625750
## 57 list1 align1_rep1 57 1.429 * 1.34395750 1.50763375
## 58 list1 align1_rep1 58 1.425 * 1.32366125 1.50376125
## 59 list1 align1_rep1 59 1.618 * 1.52149250 1.70617750
## 60 list1 align1_rep1 60 1.707 * 1.57844750 1.87799625
## 61 list1 align1_rep1 61 1.716 * 1.63345750 1.85900000
## 62 list1 align1_rep1 62 1.699 * 1.58743750 1.82133875
## 63 list1 align1_rep1 63 1.383 * 1.29594875 1.48809750
## 64 list1 align1_rep1 64 1.316 * 1.21631125 1.43798375
## 65 list1 align1_rep1 65 1.293 * 1.19635875 1.36624500
## 66 list1 align1_rep1 66 1.136 * 1.07184875 1.21251125
## 67 list1 align1_rep1 67 1.095 * 1.03634375 1.16223250
## 68 list1 align1_rep1 68 1.028 * 0.96136375 1.08077375
## 69 list1 align1_rep1 69 0.895 * 0.84471375 0.95909625
## 70 list1 align1_rep1 70 0.825 * 0.75759500 0.88057125
## 71 list1 align1_rep1 71 0.898 * 0.84304375 0.97346125
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## 375 list1 align2_rep2 75 0.140 * 0.10485750 0.17029750
## 376 list1 align2_rep2 76 0.069 * 0.04217625 0.09927375
## 377 list1 align2_rep2 77 0.105 * 0.06275625 0.13265625
## 378 list1 align2_rep2 78 0.137 * 0.08908250 0.18118375
## 379 list1 align2_rep2 79 0.137 * 0.11492000 0.16964000
## 380 list1 align2_rep2 80 0.472 * 0.38099500 0.54550625
## 381 list1 align2_rep2 81 0.456 * 0.38500375 0.52792625
## 382 list1 align2_rep2 82 0.157 * 0.11604125 0.19065375
## 383 list1 align2_rep2 83 0.046 * 0.03245000 0.06037250
## 384 list1 align2_rep2 84 0.151 * 0.11861750 0.18804250
## 385 list1 align2_rep2 85 0.193 * 0.15593625 0.24282750
## 386 list1 align2_rep2 86 0.220 * 0.17966000 0.29417750
## 387 list1 align2_rep2 87 0.499 * 0.40004625 0.56371250
## 388 list1 align2_rep2 88 0.337 * 0.25203500 0.41287500
## 389 list1 align2_rep2 89 0.140 * 0.10703500 0.18198250
## 390 list1 align2_rep2 90 0.203 * 0.14573875 0.24157375
## 391 list1 align2_rep2 91 0.378 * 0.30565625 0.44855250
## 392 list1 align2_rep2 92 0.556 * 0.45046500 0.65619875
## 393 list1 align2_rep2 93 0.284 * 0.23331125 0.32365875
## 394 list1 align2_rep2 94 0.081 * 0.05728875 0.10551000
## 395 list1 align2_rep2 95 0.138 * 0.10799250 0.17426250
## 396 list1 align2_rep2 96 0.361 * 0.27087625 0.47024500
## 397 list1 align2_rep2 97 0.298 * 0.24634375 0.35586875
## 398 list1 align2_rep2 98 0.606 * 0.50365250 0.74314000
## 399 list1 align2_rep2 99 0.536 * 0.42912750 0.61083250
## 400 list1 align2_rep2 100 0.174 * 0.14161750 0.22617000
## 401 list1 ctrl 1 0.584 * 0.53901375 0.62993875
## 402 list1 ctrl 2 0.515 * 0.46471625 0.54806250
## 403 list1 ctrl 3 0.592 * 0.54961375 0.64157875
## 404 list1 ctrl 4 0.654 * 0.60214750 0.71747750
## 405 list1 ctrl 5 0.651 * 0.58667250 0.69722250
## 406 list1 ctrl 6 0.660 * 0.58042750 0.72105500
## 407 list1 ctrl 7 0.614 * 0.58154875 0.65172625
## 408 list1 ctrl 8 0.574 * 0.52188250 0.61174375
## 409 list1 ctrl 9 0.567 * 0.52555750 0.60629875
## 410 list1 ctrl 10 0.568 * 0.53479125 0.61022375
## 411 list1 ctrl 11 0.644 * 0.59449750 0.69688375
## 412 list1 ctrl 12 0.504 * 0.45942625 0.54954000
## 413 list1 ctrl 13 0.487 * 0.44111500 0.51434875
## 414 list1 ctrl 14 0.530 * 0.48657625 0.57562125
## 415 list1 ctrl 15 0.620 * 0.58202625 0.67162875
## 416 list1 ctrl 16 0.708 * 0.66010875 0.75851500
## 417 list1 ctrl 17 0.798 * 0.73774250 0.86940250
## 418 list1 ctrl 18 0.796 * 0.74965875 0.84122625
## 419 list1 ctrl 19 0.855 * 0.80574125 0.90429750
## 420 list1 ctrl 20 0.790 * 0.72955750 0.83111625
## 421 list1 ctrl 21 0.792 * 0.71369750 0.85076500
## 422 list1 ctrl 22 0.854 * 0.77758375 0.93777375
## 423 list1 ctrl 23 0.786 * 0.72775125 0.83353500
## 424 list1 ctrl 24 0.984 * 0.92820500 1.09173500
## 425 list1 ctrl 25 1.143 * 1.04780125 1.23051375
## 426 list1 ctrl 26 0.919 * 0.82286625 0.98891625
## 427 list1 ctrl 27 0.940 * 0.85095000 1.05786250
## 428 list1 ctrl 28 1.023 * 0.93376500 1.11605125
## 429 list1 ctrl 29 1.112 * 1.04930750 1.19367750
## 430 list1 ctrl 30 1.076 * 1.00956750 1.13877750
## 431 list1 ctrl 31 1.020 * 0.93143375 1.08035875
## 432 list1 ctrl 32 1.217 * 1.14257875 1.29889500
## 433 list1 ctrl 33 1.373 * 1.28571000 1.43851375
## 434 list1 ctrl 34 1.663 * 1.58559500 1.72618625
## 435 list1 ctrl 35 1.750 * 1.67619250 1.85454000
## 436 list1 ctrl 36 1.727 * 1.60427000 1.83209625
## 437 list1 ctrl 37 1.786 * 1.67999625 1.88965000
## 438 list1 ctrl 38 2.013 * 1.82134000 2.17456750
## 439 list1 ctrl 39 2.331 * 2.20678250 2.48282125
## 440 list1 ctrl 40 2.336 * 2.20933375 2.48646875
## 441 list1 ctrl 41 2.092 * 1.96544250 2.22646625
## 442 list1 ctrl 42 2.298 * 2.19843375 2.39628250
## 443 list1 ctrl 43 2.821 * 2.64710125 3.00662875
## 444 list1 ctrl 44 3.097 * 2.94004250 3.21022375
## 445 list1 ctrl 45 3.621 * 3.46816875 3.76515000
## 446 list1 ctrl 46 4.533 * 4.29064375 4.72868000
## 447 list1 ctrl 47 4.631 * 4.47783750 4.81843250
## 448 list1 ctrl 48 4.739 * 4.60024500 4.94843750
## 449 list1 ctrl 49 4.906 * 4.72135625 5.11519375
## 450 list1 ctrl 50 5.254 * 5.05095000 5.51775750
## 451 list1 ctrl 51 5.477 * 5.22686625 5.70400250
## 452 list1 ctrl 52 4.813 * 4.64776750 5.00392000
## 453 list1 ctrl 53 4.509 * 4.27515625 4.65213750
## 454 list1 ctrl 54 3.935 * 3.76974250 4.14463750
## 455 list1 ctrl 55 3.140 * 2.92967000 3.27792875
## 456 list1 ctrl 56 2.912 * 2.77982500 3.05411000
## 457 list1 ctrl 57 2.945 * 2.82309750 3.12855250
## 458 list1 ctrl 58 2.748 * 2.65467250 2.87066750
## 459 list1 ctrl 59 2.555 * 2.43921875 2.63486250
## 460 list1 ctrl 60 2.708 * 2.55868875 2.83690875
## 461 list1 ctrl 61 2.500 * 2.36647625 2.65145000
## 462 list1 ctrl 62 2.279 * 2.14417250 2.37930125
## 463 list1 ctrl 63 2.672 * 2.50524250 2.79072500
## 464 list1 ctrl 64 2.878 * 2.72617250 3.02576500
## 465 list1 ctrl 65 2.269 * 2.18355250 2.40059125
## 466 list1 ctrl 66 1.775 * 1.64331875 1.89809625
## 467 list1 ctrl 67 1.684 * 1.58545500 1.84039000
## 468 list1 ctrl 68 1.558 * 1.42902250 1.65459125
## 469 list1 ctrl 69 1.326 * 1.24558250 1.41060125
## 470 list1 ctrl 70 1.319 * 1.20063375 1.45733375
## 471 list1 ctrl 71 1.376 * 1.26171500 1.51221000
## 472 list1 ctrl 72 1.121 * 1.05306000 1.17451375
## 473 list1 ctrl 73 1.067 * 1.00203625 1.13336625
## 474 list1 ctrl 74 1.111 * 1.05035000 1.17093250
## 475 list1 ctrl 75 1.069 * 1.00964625 1.14501375
## 476 list1 ctrl 76 1.086 * 1.05334625 1.16450250
## 477 list1 ctrl 77 0.938 * 0.88595625 1.01173500
## 478 list1 ctrl 78 0.836 * 0.74651875 0.88455500
## 479 list1 ctrl 79 0.831 * 0.76040875 0.87673875
## 480 list1 ctrl 80 0.868 * 0.79222125 0.93400625
## 481 list1 ctrl 81 0.918 * 0.82886500 0.99526625
## 482 list1 ctrl 82 0.690 * 0.63977500 0.76268375
## 483 list1 ctrl 83 0.764 * 0.71601875 0.84186000
## 484 list1 ctrl 84 0.957 * 0.89071875 1.01620500
## 485 list1 ctrl 85 0.839 * 0.78690250 0.89718375
## 486 list1 ctrl 86 0.984 * 0.90134250 1.04605750
## 487 list1 ctrl 87 1.081 * 0.99028250 1.14207375
## 488 list1 ctrl 88 0.868 * 0.80177500 0.93243375
## 489 list1 ctrl 89 0.779 * 0.71763625 0.82770000
## 490 list1 ctrl 90 1.013 * 0.97667000 1.05494375
## 491 list1 ctrl 91 0.984 * 0.92242000 1.04739250
## 492 list1 ctrl 92 0.995 * 0.91712125 1.06851000
## 493 list1 ctrl 93 1.035 * 0.95386125 1.15463375
## 494 list1 ctrl 94 0.810 * 0.74404250 0.89926500
## 495 list1 ctrl 95 0.902 * 0.83383375 0.95366375
## 496 list1 ctrl 96 1.034 * 0.93360750 1.12136625
## 497 list1 ctrl 97 0.903 * 0.82714125 1.00752875
## 498 list1 ctrl 98 0.808 * 0.74664500 0.89296625
## 499 list1 ctrl 99 0.876 * 0.81325875 0.94498750
## 500 list1 ctrl 100 0.655 * 0.58371250 0.71174375
## 501 list2 align1_rep1 1 0.525 * 0.45731500 0.61302750
## 502 list2 align1_rep1 2 0.495 * 0.44055500 0.55262250
## 503 list2 align1_rep1 3 0.469 * 0.42651250 0.50226375
## 504 list2 align1_rep1 4 0.442 * 0.38554625 0.53039125
## 505 list2 align1_rep1 5 0.482 * 0.40073500 0.55158000
## 506 list2 align1_rep1 6 0.627 * 0.49872875 0.81152625
## 507 list2 align1_rep1 7 0.719 * 0.54661375 0.86653500
## 508 list2 align1_rep1 8 0.704 * 0.53872375 0.85619250
## 509 list2 align1_rep1 9 0.777 * 0.62387000 0.98412250
## 510 list2 align1_rep1 10 0.825 * 0.68710750 0.99227500
## 511 list2 align1_rep1 11 0.668 * 0.52147500 0.83069875
## 512 list2 align1_rep1 12 0.564 * 0.47329250 0.67095875
## 513 list2 align1_rep1 13 0.497 * 0.43465500 0.59079000
## 514 list2 align1_rep1 14 0.580 * 0.49758375 0.65194000
## 515 list2 align1_rep1 15 0.652 * 0.54477625 0.79512875
## 516 list2 align1_rep1 16 0.419 * 0.37517875 0.47930875
## 517 list2 align1_rep1 17 0.443 * 0.38404250 0.48763125
## 518 list2 align1_rep1 18 0.517 * 0.43039750 0.60438750
## 519 list2 align1_rep1 19 0.437 * 0.37483375 0.48622500
## 520 list2 align1_rep1 20 0.500 * 0.45443750 0.53902750
## 521 list2 align1_rep1 21 0.702 * 0.64870375 0.74467125
## 522 list2 align1_rep1 22 0.666 * 0.63166125 0.72320375
## 523 list2 align1_rep1 23 0.572 * 0.51797375 0.64089500
## 524 list2 align1_rep1 24 0.594 * 0.52535125 0.65071000
## 525 list2 align1_rep1 25 0.507 * 0.46225000 0.54290875
## 526 list2 align1_rep1 26 0.497 * 0.45392625 0.54901875
## 527 list2 align1_rep1 27 0.602 * 0.56614125 0.63813250
## 528 list2 align1_rep1 28 0.518 * 0.47978750 0.56210750
## 529 list2 align1_rep1 29 0.353 * 0.31035125 0.40070250
## 530 list2 align1_rep1 30 0.329 * 0.29919000 0.35892375
## 531 list2 align1_rep1 31 0.398 * 0.35696875 0.42839375
## 532 list2 align1_rep1 32 0.426 * 0.39018750 0.46326250
## 533 list2 align1_rep1 33 0.404 * 0.37921750 0.44263625
## 534 list2 align1_rep1 34 0.448 * 0.41049000 0.47907000
## 535 list2 align1_rep1 35 0.622 * 0.56691750 0.67979375
## 536 list2 align1_rep1 36 0.621 * 0.55854125 0.70844000
## 537 list2 align1_rep1 37 0.686 * 0.61955000 0.73908125
## 538 list2 align1_rep1 38 0.706 * 0.65036000 0.78917500
## 539 list2 align1_rep1 39 0.596 * 0.54199500 0.67010500
## 540 list2 align1_rep1 40 0.655 * 0.60673500 0.72273000
## 541 list2 align1_rep1 41 0.687 * 0.64269375 0.73674750
## 542 list2 align1_rep1 42 0.784 * 0.72162750 0.86051750
## 543 list2 align1_rep1 43 0.687 * 0.64421375 0.73001500
## 544 list2 align1_rep1 44 0.623 * 0.54294500 0.68645750
## 545 list2 align1_rep1 45 0.844 * 0.77228625 0.91276875
## 546 list2 align1_rep1 46 0.989 * 0.88478375 1.06924250
## 547 list2 align1_rep1 47 1.054 * 0.95045875 1.13723375
## 548 list2 align1_rep1 48 1.022 * 0.92524375 1.10116875
## 549 list2 align1_rep1 49 1.129 * 1.00754375 1.22602375
## 550 list2 align1_rep1 50 1.097 * 0.92836875 1.22633750
## 551 list2 align1_rep1 51 0.865 * 0.81386125 0.92199375
## 552 list2 align1_rep1 52 0.872 * 0.79930125 0.91791250
## 553 list2 align1_rep1 53 0.926 * 0.88224250 0.99275500
## 554 list2 align1_rep1 54 0.811 * 0.75604625 0.85856000
## 555 list2 align1_rep1 55 0.660 * 0.61759750 0.70015000
## 556 list2 align1_rep1 56 0.695 * 0.63731250 0.73446875
## 557 list2 align1_rep1 57 0.696 * 0.62906125 0.75580750
## 558 list2 align1_rep1 58 0.737 * 0.67144250 0.77910000
## 559 list2 align1_rep1 59 0.593 * 0.55259125 0.63932875
## 560 list2 align1_rep1 60 0.756 * 0.68902125 0.84339250
## 561 list2 align1_rep1 61 0.943 * 0.84405625 1.04051000
## 562 list2 align1_rep1 62 0.850 * 0.76377000 0.93514875
## 563 list2 align1_rep1 63 0.826 * 0.74443000 0.88694625
## 564 list2 align1_rep1 64 0.568 * 0.52144375 0.63513250
## 565 list2 align1_rep1 65 0.590 * 0.54401750 0.64241250
## 566 list2 align1_rep1 66 0.691 * 0.63872875 0.75359000
## 567 list2 align1_rep1 67 0.668 * 0.61002875 0.71675625
## 568 list2 align1_rep1 68 0.566 * 0.50643875 0.60839500
## 569 list2 align1_rep1 69 0.512 * 0.46723125 0.57917875
## 570 list2 align1_rep1 70 0.630 * 0.57799875 0.68884000
## 571 list2 align1_rep1 71 0.720 * 0.67099625 0.76102125
## 572 list2 align1_rep1 72 0.652 * 0.60065750 0.72342875
## 573 list2 align1_rep1 73 0.460 * 0.43133125 0.51372125
## 574 list2 align1_rep1 74 0.388 * 0.35936625 0.42322000
## 575 list2 align1_rep1 75 0.589 * 0.52909375 0.63425375
## 576 list2 align1_rep1 76 0.691 * 0.63173250 0.74545625
## 577 list2 align1_rep1 77 0.645 * 0.59806000 0.70860125
## 578 list2 align1_rep1 78 0.520 * 0.47105375 0.55020625
## 579 list2 align1_rep1 79 0.590 * 0.53581375 0.65355000
## 580 list2 align1_rep1 80 0.609 * 0.54486750 0.66568000
## 581 list2 align1_rep1 81 0.530 * 0.48658875 0.58176750
## 582 list2 align1_rep1 82 0.513 * 0.46936875 0.55624375
## 583 list2 align1_rep1 83 0.491 * 0.45067625 0.53615875
## 584 list2 align1_rep1 84 0.433 * 0.38825625 0.48160375
## 585 list2 align1_rep1 85 0.399 * 0.35862000 0.44843500
## 586 list2 align1_rep1 86 0.394 * 0.35910000 0.41975625
## 587 list2 align1_rep1 87 0.454 * 0.42134125 0.49753875
## 588 list2 align1_rep1 88 0.499 * 0.44546375 0.53739250
## 589 list2 align1_rep1 89 0.541 * 0.49069000 0.59893375
## 590 list2 align1_rep1 90 0.465 * 0.43291500 0.50999625
## 591 list2 align1_rep1 91 0.518 * 0.48190625 0.57372750
## 592 list2 align1_rep1 92 0.433 * 0.40263750 0.47295750
## 593 list2 align1_rep1 93 0.477 * 0.43285250 0.51871500
## 594 list2 align1_rep1 94 0.479 * 0.43230500 0.53240750
## 595 list2 align1_rep1 95 0.598 * 0.52891125 0.67110375
## 596 list2 align1_rep1 96 0.736 * 0.64344625 0.86644375
## 597 list2 align1_rep1 97 0.519 * 0.42000500 0.60950125
## 598 list2 align1_rep1 98 0.637 * 0.54289500 0.71881000
## 599 list2 align1_rep1 99 0.771 * 0.68790125 0.86653375
## 600 list2 align1_rep1 100 0.677 * 0.57810375 0.75904000
## 601 list2 align1_rep2 1 0.062 * 0.04401625 0.07689375
## 602 list2 align1_rep2 2 0.112 * 0.09690000 0.13055500
## 603 list2 align1_rep2 3 0.141 * 0.12096375 0.15937125
## 604 list2 align1_rep2 4 0.112 * 0.09040500 0.13552000
## 605 list2 align1_rep2 5 0.155 * 0.11871750 0.19778625
## 606 list2 align1_rep2 6 0.228 * 0.16333500 0.28829500
## 607 list2 align1_rep2 7 0.219 * 0.16922500 0.28113750
## 608 list2 align1_rep2 8 0.207 * 0.15525625 0.25300500
## 609 list2 align1_rep2 9 0.264 * 0.17705625 0.36811625
## 610 list2 align1_rep2 10 0.245 * 0.19248000 0.30143750
## 611 list2 align1_rep2 11 0.309 * 0.27353000 0.35602875
## 612 list2 align1_rep2 12 0.279 * 0.23092250 0.32255750
## 613 list2 align1_rep2 13 0.206 * 0.17365375 0.23255250
## 614 list2 align1_rep2 14 0.243 * 0.20200125 0.27865375
## 615 list2 align1_rep2 15 0.231 * 0.20378375 0.26298625
## 616 list2 align1_rep2 16 0.106 * 0.08899000 0.12789000
## 617 list2 align1_rep2 17 0.087 * 0.07105000 0.10072125
## 618 list2 align1_rep2 18 0.218 * 0.18457500 0.25836875
## 619 list2 align1_rep2 19 0.296 * 0.25246250 0.32909375
## 620 list2 align1_rep2 20 0.250 * 0.22646750 0.27665250
## 621 list2 align1_rep2 21 0.196 * 0.17041125 0.23169250
## 622 list2 align1_rep2 22 0.180 * 0.15351125 0.20863875
## 623 list2 align1_rep2 23 0.182 * 0.16356125 0.20910500
## 624 list2 align1_rep2 24 0.202 * 0.18067000 0.22734250
## 625 list2 align1_rep2 25 0.164 * 0.14515125 0.18971375
## 626 list2 align1_rep2 26 0.178 * 0.15035500 0.20366625
## 627 list2 align1_rep2 27 0.176 * 0.13751375 0.22014375
## 628 list2 align1_rep2 28 0.150 * 0.13193500 0.16718500
## 629 list2 align1_rep2 29 0.146 * 0.12112750 0.16161875
## 630 list2 align1_rep2 30 0.195 * 0.16171125 0.22045875
## 631 list2 align1_rep2 31 0.171 * 0.14452500 0.19119875
## 632 list2 align1_rep2 32 0.155 * 0.13016750 0.17829875
## 633 list2 align1_rep2 33 0.212 * 0.18238125 0.23885000
## 634 list2 align1_rep2 34 0.218 * 0.19146125 0.23912750
## 635 list2 align1_rep2 35 0.198 * 0.16269125 0.23103000
## 636 list2 align1_rep2 36 0.211 * 0.18712500 0.24187125
## 637 list2 align1_rep2 37 0.282 * 0.25426250 0.31229250
## 638 list2 align1_rep2 38 0.289 * 0.24338125 0.32258125
## 639 list2 align1_rep2 39 0.306 * 0.25666250 0.35611625
## 640 list2 align1_rep2 40 0.261 * 0.21900000 0.29528500
## 641 list2 align1_rep2 41 0.223 * 0.18212750 0.25880500
## 642 list2 align1_rep2 42 0.426 * 0.37092500 0.47149750
## 643 list2 align1_rep2 43 0.374 * 0.34060500 0.40688750
## 644 list2 align1_rep2 44 0.340 * 0.30743125 0.37764375
## 645 list2 align1_rep2 45 0.486 * 0.43889500 0.54605750
## 646 list2 align1_rep2 46 0.428 * 0.39145875 0.47601000
## 647 list2 align1_rep2 47 0.386 * 0.33980875 0.45267750
## 648 list2 align1_rep2 48 0.398 * 0.34790125 0.44054500
## 649 list2 align1_rep2 49 0.386 * 0.33582875 0.43790750
## 650 list2 align1_rep2 50 0.447 * 0.39718000 0.48704500
## 651 list2 align1_rep2 51 0.417 * 0.36248250 0.46457125
## 652 list2 align1_rep2 52 0.378 * 0.34944375 0.42103125
## 653 list2 align1_rep2 53 0.299 * 0.26291875 0.33631500
## 654 list2 align1_rep2 54 0.310 * 0.26982750 0.35536750
## 655 list2 align1_rep2 55 0.356 * 0.31673125 0.38915250
## 656 list2 align1_rep2 56 0.444 * 0.40937250 0.48543500
## 657 list2 align1_rep2 57 0.367 * 0.33825750 0.40099125
## 658 list2 align1_rep2 58 0.285 * 0.26181500 0.31353125
## 659 list2 align1_rep2 59 0.271 * 0.24187000 0.29910750
## 660 list2 align1_rep2 60 0.234 * 0.20131875 0.27751250
## 661 list2 align1_rep2 61 0.317 * 0.26893000 0.36820750
## 662 list2 align1_rep2 62 0.388 * 0.34922625 0.42883000
## 663 list2 align1_rep2 63 0.314 * 0.26860250 0.35974500
## 664 list2 align1_rep2 64 0.289 * 0.26017875 0.33198000
## 665 list2 align1_rep2 65 0.316 * 0.28787375 0.35518875
## 666 list2 align1_rep2 66 0.388 * 0.34032750 0.43243500
## 667 list2 align1_rep2 67 0.361 * 0.32471500 0.38735000
## 668 list2 align1_rep2 68 0.252 * 0.21815500 0.27536625
## 669 list2 align1_rep2 69 0.217 * 0.19461375 0.24380625
## 670 list2 align1_rep2 70 0.246 * 0.22587250 0.27687000
## 671 list2 align1_rep2 71 0.227 * 0.20105500 0.26663375
## 672 list2 align1_rep2 72 0.179 * 0.16222500 0.20417625
## 673 list2 align1_rep2 73 0.162 * 0.13693500 0.19097625
## 674 list2 align1_rep2 74 0.189 * 0.16063750 0.21495250
## 675 list2 align1_rep2 75 0.216 * 0.18706875 0.25068250
## 676 list2 align1_rep2 76 0.279 * 0.23653125 0.31189250
## 677 list2 align1_rep2 77 0.346 * 0.30869500 0.40820125
## 678 list2 align1_rep2 78 0.188 * 0.15719000 0.21824375
## 679 list2 align1_rep2 79 0.148 * 0.12708500 0.16998750
## 680 list2 align1_rep2 80 0.350 * 0.31886500 0.38394375
## 681 list2 align1_rep2 81 0.290 * 0.26467250 0.32272125
## 682 list2 align1_rep2 82 0.131 * 0.11399125 0.15045750
## 683 list2 align1_rep2 83 0.080 * 0.06668000 0.10510375
## 684 list2 align1_rep2 84 0.094 * 0.08171250 0.11236125
## 685 list2 align1_rep2 85 0.133 * 0.10574750 0.14929125
## 686 list2 align1_rep2 86 0.128 * 0.10578000 0.14876625
## 687 list2 align1_rep2 87 0.166 * 0.13594625 0.19523000
## 688 list2 align1_rep2 88 0.199 * 0.17244875 0.22317875
## 689 list2 align1_rep2 89 0.148 * 0.11914250 0.17884875
## 690 list2 align1_rep2 90 0.108 * 0.09068250 0.12324750
## 691 list2 align1_rep2 91 0.168 * 0.14811500 0.19080000
## 692 list2 align1_rep2 92 0.220 * 0.19115375 0.24900500
## 693 list2 align1_rep2 93 0.260 * 0.22874500 0.28854875
## 694 list2 align1_rep2 94 0.311 * 0.27649250 0.36061875
## 695 list2 align1_rep2 95 0.221 * 0.19741750 0.27121875
## 696 list2 align1_rep2 96 0.179 * 0.15636125 0.20617875
## 697 list2 align1_rep2 97 0.296 * 0.25792375 0.33226000
## 698 list2 align1_rep2 98 0.323 * 0.27853625 0.37970625
## 699 list2 align1_rep2 99 0.251 * 0.20667875 0.30744125
## 700 list2 align1_rep2 100 0.332 * 0.28875750 0.37338750
## 701 list2 align2_rep1 1 0.055 * 0.04223875 0.06607500
## 702 list2 align2_rep1 2 0.052 * 0.03784625 0.06749625
## 703 list2 align2_rep1 3 0.068 * 0.05329000 0.08624000
## 704 list2 align2_rep1 4 0.063 * 0.05163625 0.07546625
## 705 list2 align2_rep1 5 0.082 * 0.06615625 0.09304750
## 706 list2 align2_rep1 6 0.111 * 0.09590375 0.12798000
## 707 list2 align2_rep1 7 0.152 * 0.13636000 0.16458625
## 708 list2 align2_rep1 8 0.153 * 0.13433000 0.17728375
## 709 list2 align2_rep1 9 0.156 * 0.13276250 0.17859125
## 710 list2 align2_rep1 10 0.105 * 0.09231125 0.12171750
## 711 list2 align2_rep1 11 0.063 * 0.05133125 0.07739625
## 712 list2 align2_rep1 12 0.123 * 0.10764750 0.14044750
## 713 list2 align2_rep1 13 0.144 * 0.12113000 0.16295750
## 714 list2 align2_rep1 14 0.140 * 0.12169625 0.15907125
## 715 list2 align2_rep1 15 0.167 * 0.14606875 0.19288750
## 716 list2 align2_rep1 16 0.159 * 0.14271000 0.17252750
## 717 list2 align2_rep1 17 0.150 * 0.12992125 0.16666375
## 718 list2 align2_rep1 18 0.137 * 0.11751750 0.15401625
## 719 list2 align2_rep1 19 0.080 * 0.06421250 0.10052250
## 720 list2 align2_rep1 20 0.072 * 0.06044750 0.08596000
## 721 list2 align2_rep1 21 0.131 * 0.10422500 0.16135250
## 722 list2 align2_rep1 22 0.160 * 0.13821625 0.19130750
## 723 list2 align2_rep1 23 0.084 * 0.06867875 0.10341625
## 724 list2 align2_rep1 24 0.063 * 0.05271875 0.07994500
## 725 list2 align2_rep1 25 0.093 * 0.08064250 0.10981750
## 726 list2 align2_rep1 26 0.071 * 0.05486375 0.08625250
## 727 list2 align2_rep1 27 0.075 * 0.06356625 0.08388125
## 728 list2 align2_rep1 28 0.121 * 0.10894625 0.14284625
## 729 list2 align2_rep1 29 0.106 * 0.08684375 0.12202625
## 730 list2 align2_rep1 30 0.112 * 0.09624000 0.13311375
## 731 list2 align2_rep1 31 0.127 * 0.10458375 0.15343625
## 732 list2 align2_rep1 32 0.054 * 0.04615000 0.06395750
## 733 list2 align2_rep1 33 0.020 * 0.01431250 0.02676625
## 734 list2 align2_rep1 34 0.057 * 0.04723750 0.06638375
## 735 list2 align2_rep1 35 0.114 * 0.09531000 0.13134625
## 736 list2 align2_rep1 36 0.168 * 0.14092250 0.18857875
## 737 list2 align2_rep1 37 0.175 * 0.15298500 0.20029750
## 738 list2 align2_rep1 38 0.105 * 0.09214000 0.12103750
## 739 list2 align2_rep1 39 0.123 * 0.10521750 0.13896500
## 740 list2 align2_rep1 40 0.130 * 0.11569250 0.14016625
## 741 list2 align2_rep1 41 0.099 * 0.08064625 0.11405125
## 742 list2 align2_rep1 42 0.125 * 0.10198125 0.15365250
## 743 list2 align2_rep1 43 0.106 * 0.08421750 0.12083250
## 744 list2 align2_rep1 44 0.173 * 0.15237250 0.19699750
## 745 list2 align2_rep1 45 0.226 * 0.20715000 0.24313125
## 746 list2 align2_rep1 46 0.169 * 0.15213125 0.18864500
## 747 list2 align2_rep1 47 0.251 * 0.22455875 0.27834250
## 748 list2 align2_rep1 48 0.241 * 0.20700500 0.26548125
## 749 list2 align2_rep1 49 0.240 * 0.20533625 0.27367750
## 750 list2 align2_rep1 50 0.183 * 0.16713750 0.21198375
## 751 list2 align2_rep1 51 0.181 * 0.16035250 0.20188625
## 752 list2 align2_rep1 52 0.240 * 0.21036000 0.26417250
## 753 list2 align2_rep1 53 0.149 * 0.12868125 0.16088625
## 754 list2 align2_rep1 54 0.148 * 0.13410625 0.16810250
## 755 list2 align2_rep1 55 0.223 * 0.19744375 0.24808125
## 756 list2 align2_rep1 56 0.170 * 0.15599000 0.19296750
## 757 list2 align2_rep1 57 0.127 * 0.11470625 0.14732000
## 758 list2 align2_rep1 58 0.107 * 0.08980875 0.12486750
## 759 list2 align2_rep1 59 0.113 * 0.09152875 0.13394375
## 760 list2 align2_rep1 60 0.133 * 0.10981750 0.16130250
## 761 list2 align2_rep1 61 0.102 * 0.08546250 0.11211375
## 762 list2 align2_rep1 62 0.186 * 0.16452250 0.21446625
## 763 list2 align2_rep1 63 0.205 * 0.18268000 0.23593250
## 764 list2 align2_rep1 64 0.162 * 0.14015125 0.18487875
## 765 list2 align2_rep1 65 0.134 * 0.11598250 0.14926500
## 766 list2 align2_rep1 66 0.102 * 0.08847125 0.11768875
## 767 list2 align2_rep1 67 0.032 * 0.02515625 0.03822500
## 768 list2 align2_rep1 68 0.081 * 0.06766500 0.09642750
## 769 list2 align2_rep1 69 0.158 * 0.13985875 0.17309875
## 770 list2 align2_rep1 70 0.151 * 0.13513375 0.17523250
## 771 list2 align2_rep1 71 0.097 * 0.08105625 0.11568875
## 772 list2 align2_rep1 72 0.080 * 0.06765000 0.09025500
## 773 list2 align2_rep1 73 0.132 * 0.11490500 0.15316000
## 774 list2 align2_rep1 74 0.121 * 0.10225625 0.14198750
## 775 list2 align2_rep1 75 0.121 * 0.10793750 0.13822250
## 776 list2 align2_rep1 76 0.181 * 0.16570125 0.20240750
## 777 list2 align2_rep1 77 0.144 * 0.12794250 0.17317500
## 778 list2 align2_rep1 78 0.105 * 0.08804875 0.12471250
## 779 list2 align2_rep1 79 0.104 * 0.08621125 0.12174750
## 780 list2 align2_rep1 80 0.190 * 0.16262500 0.20725500
## 781 list2 align2_rep1 81 0.237 * 0.21136625 0.25881625
## 782 list2 align2_rep1 82 0.188 * 0.16847250 0.20718375
## 783 list2 align2_rep1 83 0.129 * 0.11481000 0.14963250
## 784 list2 align2_rep1 84 0.099 * 0.08548375 0.11526750
## 785 list2 align2_rep1 85 0.136 * 0.11822875 0.16173500
## 786 list2 align2_rep1 86 0.130 * 0.10884500 0.15074000
## 787 list2 align2_rep1 87 0.093 * 0.07717375 0.10922750
## 788 list2 align2_rep1 88 0.098 * 0.08017250 0.12365000
## 789 list2 align2_rep1 89 0.073 * 0.05098125 0.08482125
## 790 list2 align2_rep1 90 0.046 * 0.03187000 0.05446500
## 791 list2 align2_rep1 91 0.049 * 0.04075000 0.05673125
## 792 list2 align2_rep1 92 0.046 * 0.03645750 0.05443250
## 793 list2 align2_rep1 93 0.052 * 0.03881375 0.06302125
## 794 list2 align2_rep1 94 0.081 * 0.06797375 0.09332500
## 795 list2 align2_rep1 95 0.070 * 0.05943500 0.08383750
## 796 list2 align2_rep1 96 0.074 * 0.06154500 0.09290625
## 797 list2 align2_rep1 97 0.097 * 0.07560000 0.11763750
## 798 list2 align2_rep1 98 0.078 * 0.06136125 0.10032375
## 799 list2 align2_rep1 99 0.063 * 0.04656750 0.08030625
## 800 list2 align2_rep1 100 0.049 * 0.03912875 0.05734375
## 801 list2 align2_rep2 1 0.170 * 0.12591250 0.20387000
## 802 list2 align2_rep2 2 0.202 * 0.16121250 0.25220750
## 803 list2 align2_rep2 3 0.173 * 0.12555750 0.20455125
## 804 list2 align2_rep2 4 0.187 * 0.14935500 0.22236250
## 805 list2 align2_rep2 5 0.366 * 0.28222875 0.42767125
## 806 list2 align2_rep2 6 0.241 * 0.17584000 0.30607625
## 807 list2 align2_rep2 7 0.146 * 0.09629750 0.17887000
## 808 list2 align2_rep2 8 0.207 * 0.15379750 0.24872375
## 809 list2 align2_rep2 9 0.172 * 0.13287000 0.21337375
## 810 list2 align2_rep2 10 0.279 * 0.19715000 0.35174500
## 811 list2 align2_rep2 11 0.266 * 0.16857500 0.35158750
## 812 list2 align2_rep2 12 0.169 * 0.11252250 0.23544000
## 813 list2 align2_rep2 13 0.180 * 0.13002500 0.22620000
## 814 list2 align2_rep2 14 0.031 * 0.02227250 0.04011625
## 815 list2 align2_rep2 15 0.040 * 0.02245000 0.05465000
## 816 list2 align2_rep2 16 0.014 * 0.00980000 0.01960000
## 817 list2 align2_rep2 17 0.000 * 0.00000000 0.00000000
## 818 list2 align2_rep2 18 0.000 * 0.00000000 0.00000000
## 819 list2 align2_rep2 19 0.000 * 0.00000000 0.00000000
## 820 list2 align2_rep2 20 0.000 * 0.00000000 0.00000000
## 821 list2 align2_rep2 21 0.051 * 0.03372375 0.07592625
## 822 list2 align2_rep2 22 0.142 * 0.10785000 0.17566875
## 823 list2 align2_rep2 23 0.095 * 0.06175000 0.14036250
## 824 list2 align2_rep2 24 0.015 * 0.00916875 0.02175000
## 825 list2 align2_rep2 25 0.060 * 0.04267500 0.08632500
## 826 list2 align2_rep2 26 0.139 * 0.10849750 0.16994875
## 827 list2 align2_rep2 27 0.172 * 0.13684500 0.22260000
## 828 list2 align2_rep2 28 0.025 * 0.01528125 0.03500000
## 829 list2 align2_rep2 29 0.000 * 0.00000000 0.00000000
## 830 list2 align2_rep2 30 0.038 * 0.02322750 0.05087250
## 831 list2 align2_rep2 31 0.082 * 0.05525250 0.11241000
## 832 list2 align2_rep2 32 0.250 * 0.18217500 0.31838750
## 833 list2 align2_rep2 33 0.283 * 0.22216125 0.35640750
## 834 list2 align2_rep2 34 0.235 * 0.18650250 0.28781750
## 835 list2 align2_rep2 35 0.216 * 0.14418000 0.30014000
## 836 list2 align2_rep2 36 0.050 * 0.02447000 0.07207750
## 837 list2 align2_rep2 37 0.099 * 0.07389625 0.13244000
## 838 list2 align2_rep2 38 0.294 * 0.24315750 0.36024000
## 839 list2 align2_rep2 39 0.161 * 0.11413000 0.19826125
## 840 list2 align2_rep2 40 0.227 * 0.16775500 0.28531500
## 841 list2 align2_rep2 41 0.128 * 0.10192500 0.15908500
## 842 list2 align2_rep2 42 0.073 * 0.05063625 0.09936625
## 843 list2 align2_rep2 43 0.179 * 0.13315500 0.24152750
## 844 list2 align2_rep2 44 0.115 * 0.08052500 0.13818375
## 845 list2 align2_rep2 45 0.313 * 0.24588875 0.39107875
## 846 list2 align2_rep2 46 0.186 * 0.13261250 0.27006125
## 847 list2 align2_rep2 47 0.110 * 0.07921500 0.13128750
## 848 list2 align2_rep2 48 0.391 * 0.31324375 0.46271375
## 849 list2 align2_rep2 49 0.303 * 0.25732875 0.36969750
## 850 list2 align2_rep2 50 0.190 * 0.14568125 0.24776875
## 851 list2 align2_rep2 51 0.344 * 0.30281500 0.40363750
## 852 list2 align2_rep2 52 0.372 * 0.29445625 0.45782625
## 853 list2 align2_rep2 53 0.188 * 0.12755875 0.25077500
## 854 list2 align2_rep2 54 0.229 * 0.17823625 0.27286875
## 855 list2 align2_rep2 55 0.290 * 0.21801500 0.37093000
## 856 list2 align2_rep2 56 0.189 * 0.14467750 0.23224250
## 857 list2 align2_rep2 57 0.196 * 0.14624500 0.25212500
## 858 list2 align2_rep2 58 0.323 * 0.18228375 0.45534000
## 859 list2 align2_rep2 59 0.205 * 0.13793125 0.28163750
## 860 list2 align2_rep2 60 0.137 * 0.10175875 0.17491625
## 861 list2 align2_rep2 61 0.275 * 0.19931625 0.34997875
## 862 list2 align2_rep2 62 0.384 * 0.30525750 0.48510250
## 863 list2 align2_rep2 63 0.273 * 0.22271500 0.33450250
## 864 list2 align2_rep2 64 0.159 * 0.11420750 0.20571500
## 865 list2 align2_rep2 65 0.312 * 0.24805750 0.36618000
## 866 list2 align2_rep2 66 0.418 * 0.34284000 0.47941250
## 867 list2 align2_rep2 67 0.248 * 0.19466750 0.30385750
## 868 list2 align2_rep2 68 0.083 * 0.06619375 0.10097250
## 869 list2 align2_rep2 69 0.156 * 0.11321750 0.19075500
## 870 list2 align2_rep2 70 0.151 * 0.12998875 0.17265250
## 871 list2 align2_rep2 71 0.169 * 0.14009750 0.23084375
## 872 list2 align2_rep2 72 0.152 * 0.10322500 0.21092000
## 873 list2 align2_rep2 73 0.025 * 0.01524500 0.03409125
## 874 list2 align2_rep2 74 0.032 * 0.02298000 0.04013000
## 875 list2 align2_rep2 75 0.128 * 0.09310000 0.16254500
## 876 list2 align2_rep2 76 0.296 * 0.21205500 0.35827625
## 877 list2 align2_rep2 77 0.259 * 0.19371625 0.33243875
## 878 list2 align2_rep2 78 0.175 * 0.13297375 0.20608000
## 879 list2 align2_rep2 79 0.394 * 0.30261500 0.46470750
## 880 list2 align2_rep2 80 0.385 * 0.28343250 0.44187125
## 881 list2 align2_rep2 81 0.295 * 0.20925750 0.40532625
## 882 list2 align2_rep2 82 0.478 * 0.38033250 0.57392125
## 883 list2 align2_rep2 83 0.252 * 0.18933250 0.30572500
## 884 list2 align2_rep2 84 0.018 * 0.01080000 0.02700000
## 885 list2 align2_rep2 85 0.130 * 0.09011250 0.15903750
## 886 list2 align2_rep2 86 0.243 * 0.18544000 0.28568750
## 887 list2 align2_rep2 87 0.214 * 0.16359500 0.26303000
## 888 list2 align2_rep2 88 0.030 * 0.02170000 0.04036500
## 889 list2 align2_rep2 89 0.147 * 0.10349500 0.19790750
## 890 list2 align2_rep2 90 0.281 * 0.22999500 0.34229750
## 891 list2 align2_rep2 91 0.251 * 0.18665625 0.31768250
## 892 list2 align2_rep2 92 0.145 * 0.10450375 0.17379875
## 893 list2 align2_rep2 93 0.080 * 0.05200000 0.11110000
## 894 list2 align2_rep2 94 0.024 * 0.01796000 0.03231000
## 895 list2 align2_rep2 95 0.088 * 0.05849000 0.11017000
## 896 list2 align2_rep2 96 0.361 * 0.27301125 0.44605500
## 897 list2 align2_rep2 97 0.586 * 0.50174500 0.72348250
## 898 list2 align2_rep2 98 0.313 * 0.22695875 0.41445375
## 899 list2 align2_rep2 99 0.466 * 0.35219875 0.58591625
## 900 list2 align2_rep2 100 0.415 * 0.29352375 0.52514375
## 901 list2 ctrl 1 0.365 * 0.32506375 0.40486375
## 902 list2 ctrl 2 0.296 * 0.26920875 0.31761625
## 903 list2 ctrl 3 0.311 * 0.26400375 0.36020625
## 904 list2 ctrl 4 0.416 * 0.37218625 0.49118000
## 905 list2 ctrl 5 0.536 * 0.47356125 0.58896750
## 906 list2 ctrl 6 0.357 * 0.31515000 0.39997125
## 907 list2 ctrl 7 0.278 * 0.24788625 0.31420500
## 908 list2 ctrl 8 0.340 * 0.28486750 0.39804375
## 909 list2 ctrl 9 0.403 * 0.31325125 0.47317000
## 910 list2 ctrl 10 0.584 * 0.49619375 0.64295875
## 911 list2 ctrl 11 0.500 * 0.44756375 0.53074875
## 912 list2 ctrl 12 0.448 * 0.39486625 0.49602875
## 913 list2 ctrl 13 0.461 * 0.43031375 0.50415875
## 914 list2 ctrl 14 0.493 * 0.45186875 0.53025625
## 915 list2 ctrl 15 0.430 * 0.39481875 0.47669500
## 916 list2 ctrl 16 0.388 * 0.35714750 0.41435000
## 917 list2 ctrl 17 0.466 * 0.41861500 0.50843750
## 918 list2 ctrl 18 0.387 * 0.32786125 0.42820125
## 919 list2 ctrl 19 0.341 * 0.29406250 0.38671750
## 920 list2 ctrl 20 0.379 * 0.33315125 0.42141625
## 921 list2 ctrl 21 0.340 * 0.30495000 0.36531875
## 922 list2 ctrl 22 0.265 * 0.23909750 0.30082375
## 923 list2 ctrl 23 0.323 * 0.28825125 0.37004250
## 924 list2 ctrl 24 0.492 * 0.43995875 0.52120125
## 925 list2 ctrl 25 0.459 * 0.41493500 0.51104375
## 926 list2 ctrl 26 0.288 * 0.26135500 0.32384375
## 927 list2 ctrl 27 0.358 * 0.32230750 0.39763250
## 928 list2 ctrl 28 0.389 * 0.35026000 0.42990375
## 929 list2 ctrl 29 0.373 * 0.34875250 0.40184375
## 930 list2 ctrl 30 0.374 * 0.33331750 0.42636000
## 931 list2 ctrl 31 0.444 * 0.39964125 0.48663875
## 932 list2 ctrl 32 0.381 * 0.32795875 0.42346500
## 933 list2 ctrl 33 0.481 * 0.40827875 0.53086250
## 934 list2 ctrl 34 0.519 * 0.46547375 0.57822000
## 935 list2 ctrl 35 0.576 * 0.52312500 0.64885000
## 936 list2 ctrl 36 0.589 * 0.50473875 0.66259375
## 937 list2 ctrl 37 0.744 * 0.64896375 0.84394500
## 938 list2 ctrl 38 0.742 * 0.65984250 0.83059875
## 939 list2 ctrl 39 0.729 * 0.64231000 0.81249875
## 940 list2 ctrl 40 0.870 * 0.77524750 0.97757500
## 941 list2 ctrl 41 0.719 * 0.65067500 0.79954875
## 942 list2 ctrl 42 0.616 * 0.55089125 0.71289625
## 943 list2 ctrl 43 0.791 * 0.68083625 0.93238125
## 944 list2 ctrl 44 0.907 * 0.82865875 0.99674750
## 945 list2 ctrl 45 0.984 * 0.89030375 1.06293875
## 946 list2 ctrl 46 1.127 * 1.03921750 1.24031250
## 947 list2 ctrl 47 1.295 * 1.20075125 1.43937000
## 948 list2 ctrl 48 1.275 * 1.16793000 1.38386250
## 949 list2 ctrl 49 1.160 * 1.04347250 1.27789750
## 950 list2 ctrl 50 1.024 * 0.96071500 1.12255625
## 951 list2 ctrl 51 0.964 * 0.88521000 1.04828125
## 952 list2 ctrl 52 0.858 * 0.79381125 0.94362500
## 953 list2 ctrl 53 0.808 * 0.75637875 0.86808000
## 954 list2 ctrl 54 0.888 * 0.81733000 0.96288125
## 955 list2 ctrl 55 0.897 * 0.83779875 0.94583875
## 956 list2 ctrl 56 0.697 * 0.64636250 0.76454250
## 957 list2 ctrl 57 0.753 * 0.69022750 0.82591125
## 958 list2 ctrl 58 0.737 * 0.66948875 0.80893625
## 959 list2 ctrl 59 0.552 * 0.50605125 0.62040375
## 960 list2 ctrl 60 0.520 * 0.46012625 0.56857000
## 961 list2 ctrl 61 0.798 * 0.72477875 0.84428750
## 962 list2 ctrl 62 1.014 * 0.93928500 1.09799875
## 963 list2 ctrl 63 0.752 * 0.68265500 0.81049500
## 964 list2 ctrl 64 0.589 * 0.52741125 0.63103500
## 965 list2 ctrl 65 0.544 * 0.49780750 0.59321625
## 966 list2 ctrl 66 0.483 * 0.43840875 0.53273375
## 967 list2 ctrl 67 0.566 * 0.52320000 0.61226625
## 968 list2 ctrl 68 0.663 * 0.60382500 0.71610000
## 969 list2 ctrl 69 0.521 * 0.48366750 0.55788375
## 970 list2 ctrl 70 0.484 * 0.41213375 0.54936125
## 971 list2 ctrl 71 0.412 * 0.36215750 0.46396000
## 972 list2 ctrl 72 0.385 * 0.34674000 0.42152125
## 973 list2 ctrl 73 0.407 * 0.36732750 0.45334500
## 974 list2 ctrl 74 0.540 * 0.50783500 0.58068375
## 975 list2 ctrl 75 0.574 * 0.51978125 0.62089625
## 976 list2 ctrl 76 0.625 * 0.55030750 0.69595250
## 977 list2 ctrl 77 0.655 * 0.56046500 0.73733250
## 978 list2 ctrl 78 0.558 * 0.51296375 0.61173500
## 979 list2 ctrl 79 0.557 * 0.49726125 0.60034125
## 980 list2 ctrl 80 0.529 * 0.47422250 0.58780625
## 981 list2 ctrl 81 0.586 * 0.52021750 0.63771000
## 982 list2 ctrl 82 0.664 * 0.60254000 0.75156000
## 983 list2 ctrl 83 0.529 * 0.45631500 0.61070125
## 984 list2 ctrl 84 0.324 * 0.29215750 0.35561750
## 985 list2 ctrl 85 0.433 * 0.40385875 0.46867500
## 986 list2 ctrl 86 0.504 * 0.47112875 0.54041875
## 987 list2 ctrl 87 0.347 * 0.32157875 0.37605250
## 988 list2 ctrl 88 0.383 * 0.35634250 0.41283250
## 989 list2 ctrl 89 0.415 * 0.37924000 0.47217500
## 990 list2 ctrl 90 0.349 * 0.32495000 0.38058125
## 991 list2 ctrl 91 0.478 * 0.43440375 0.52096625
## 992 list2 ctrl 92 0.593 * 0.54406750 0.67260375
## 993 list2 ctrl 93 0.384 * 0.34569125 0.42649625
## 994 list2 ctrl 94 0.390 * 0.35336125 0.43204500
## 995 list2 ctrl 95 0.544 * 0.47047250 0.62623375
## 996 list2 ctrl 96 0.513 * 0.42180125 0.63849000
## 997 list2 ctrl 97 0.615 * 0.52243125 0.74858375
## 998 list2 ctrl 98 0.705 * 0.53881625 0.83782750
## 999 list2 ctrl 99 0.708 * 0.58962375 0.88644750
## 1000 list2 ctrl 100 0.652 * 0.54948625 0.73770250
## group
## 1 align1_rep1_list1
## 2 align1_rep1_list1
## 3 align1_rep1_list1
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## 708 align2_rep1_list2
## 709 align2_rep1_list2
## 710 align2_rep1_list2
## 711 align2_rep1_list2
## 712 align2_rep1_list2
## 713 align2_rep1_list2
## 714 align2_rep1_list2
## 715 align2_rep1_list2
## 716 align2_rep1_list2
## 717 align2_rep1_list2
## 718 align2_rep1_list2
## 719 align2_rep1_list2
## 720 align2_rep1_list2
## 721 align2_rep1_list2
## 722 align2_rep1_list2
## 723 align2_rep1_list2
## 724 align2_rep1_list2
## 725 align2_rep1_list2
## 726 align2_rep1_list2
## 727 align2_rep1_list2
## 728 align2_rep1_list2
## 729 align2_rep1_list2
## 730 align2_rep1_list2
## 731 align2_rep1_list2
## 732 align2_rep1_list2
## 733 align2_rep1_list2
## 734 align2_rep1_list2
## 735 align2_rep1_list2
## 736 align2_rep1_list2
## 737 align2_rep1_list2
## 738 align2_rep1_list2
## 739 align2_rep1_list2
## 740 align2_rep1_list2
## 741 align2_rep1_list2
## 742 align2_rep1_list2
## 743 align2_rep1_list2
## 744 align2_rep1_list2
## 745 align2_rep1_list2
## 746 align2_rep1_list2
## 747 align2_rep1_list2
## 748 align2_rep1_list2
## 749 align2_rep1_list2
## 750 align2_rep1_list2
## 751 align2_rep1_list2
## 752 align2_rep1_list2
## 753 align2_rep1_list2
## 754 align2_rep1_list2
## 755 align2_rep1_list2
## 756 align2_rep1_list2
## 757 align2_rep1_list2
## 758 align2_rep1_list2
## 759 align2_rep1_list2
## 760 align2_rep1_list2
## 761 align2_rep1_list2
## 762 align2_rep1_list2
## 763 align2_rep1_list2
## 764 align2_rep1_list2
## 765 align2_rep1_list2
## 766 align2_rep1_list2
## 767 align2_rep1_list2
## 768 align2_rep1_list2
## 769 align2_rep1_list2
## 770 align2_rep1_list2
## 771 align2_rep1_list2
## 772 align2_rep1_list2
## 773 align2_rep1_list2
## 774 align2_rep1_list2
## 775 align2_rep1_list2
## 776 align2_rep1_list2
## 777 align2_rep1_list2
## 778 align2_rep1_list2
## 779 align2_rep1_list2
## 780 align2_rep1_list2
## 781 align2_rep1_list2
## 782 align2_rep1_list2
## 783 align2_rep1_list2
## 784 align2_rep1_list2
## 785 align2_rep1_list2
## 786 align2_rep1_list2
## 787 align2_rep1_list2
## 788 align2_rep1_list2
## 789 align2_rep1_list2
## 790 align2_rep1_list2
## 791 align2_rep1_list2
## 792 align2_rep1_list2
## 793 align2_rep1_list2
## 794 align2_rep1_list2
## 795 align2_rep1_list2
## 796 align2_rep1_list2
## 797 align2_rep1_list2
## 798 align2_rep1_list2
## 799 align2_rep1_list2
## 800 align2_rep1_list2
## 801 align2_rep2_list2
## 802 align2_rep2_list2
## 803 align2_rep2_list2
## 804 align2_rep2_list2
## 805 align2_rep2_list2
## 806 align2_rep2_list2
## 807 align2_rep2_list2
## 808 align2_rep2_list2
## 809 align2_rep2_list2
## 810 align2_rep2_list2
## 811 align2_rep2_list2
## 812 align2_rep2_list2
## 813 align2_rep2_list2
## 814 align2_rep2_list2
## 815 align2_rep2_list2
## 816 align2_rep2_list2
## 817 align2_rep2_list2
## 818 align2_rep2_list2
## 819 align2_rep2_list2
## 820 align2_rep2_list2
## 821 align2_rep2_list2
## 822 align2_rep2_list2
## 823 align2_rep2_list2
## 824 align2_rep2_list2
## 825 align2_rep2_list2
## 826 align2_rep2_list2
## 827 align2_rep2_list2
## 828 align2_rep2_list2
## 829 align2_rep2_list2
## 830 align2_rep2_list2
## 831 align2_rep2_list2
## 832 align2_rep2_list2
## 833 align2_rep2_list2
## 834 align2_rep2_list2
## 835 align2_rep2_list2
## 836 align2_rep2_list2
## 837 align2_rep2_list2
## 838 align2_rep2_list2
## 839 align2_rep2_list2
## 840 align2_rep2_list2
## 841 align2_rep2_list2
## 842 align2_rep2_list2
## 843 align2_rep2_list2
## 844 align2_rep2_list2
## 845 align2_rep2_list2
## 846 align2_rep2_list2
## 847 align2_rep2_list2
## 848 align2_rep2_list2
## 849 align2_rep2_list2
## 850 align2_rep2_list2
## 851 align2_rep2_list2
## 852 align2_rep2_list2
## 853 align2_rep2_list2
## 854 align2_rep2_list2
## 855 align2_rep2_list2
## 856 align2_rep2_list2
## 857 align2_rep2_list2
## 858 align2_rep2_list2
## 859 align2_rep2_list2
## 860 align2_rep2_list2
## 861 align2_rep2_list2
## 862 align2_rep2_list2
## 863 align2_rep2_list2
## 864 align2_rep2_list2
## 865 align2_rep2_list2
## 866 align2_rep2_list2
## 867 align2_rep2_list2
## 868 align2_rep2_list2
## 869 align2_rep2_list2
## 870 align2_rep2_list2
## 871 align2_rep2_list2
## 872 align2_rep2_list2
## 873 align2_rep2_list2
## 874 align2_rep2_list2
## 875 align2_rep2_list2
## 876 align2_rep2_list2
## 877 align2_rep2_list2
## 878 align2_rep2_list2
## 879 align2_rep2_list2
## 880 align2_rep2_list2
## 881 align2_rep2_list2
## 882 align2_rep2_list2
## 883 align2_rep2_list2
## 884 align2_rep2_list2
## 885 align2_rep2_list2
## 886 align2_rep2_list2
## 887 align2_rep2_list2
## 888 align2_rep2_list2
## 889 align2_rep2_list2
## 890 align2_rep2_list2
## 891 align2_rep2_list2
## 892 align2_rep2_list2
## 893 align2_rep2_list2
## 894 align2_rep2_list2
## 895 align2_rep2_list2
## 896 align2_rep2_list2
## 897 align2_rep2_list2
## 898 align2_rep2_list2
## 899 align2_rep2_list2
## 900 align2_rep2_list2
## 901 ctrl_list2
## 902 ctrl_list2
## 903 ctrl_list2
## 904 ctrl_list2
## 905 ctrl_list2
## 906 ctrl_list2
## 907 ctrl_list2
## 908 ctrl_list2
## 909 ctrl_list2
## 910 ctrl_list2
## 911 ctrl_list2
## 912 ctrl_list2
## 913 ctrl_list2
## 914 ctrl_list2
## 915 ctrl_list2
## 916 ctrl_list2
## 917 ctrl_list2
## 918 ctrl_list2
## 919 ctrl_list2
## 920 ctrl_list2
## 921 ctrl_list2
## 922 ctrl_list2
## 923 ctrl_list2
## 924 ctrl_list2
## 925 ctrl_list2
## 926 ctrl_list2
## 927 ctrl_list2
## 928 ctrl_list2
## 929 ctrl_list2
## 930 ctrl_list2
## 931 ctrl_list2
## 932 ctrl_list2
## 933 ctrl_list2
## 934 ctrl_list2
## 935 ctrl_list2
## 936 ctrl_list2
## 937 ctrl_list2
## 938 ctrl_list2
## 939 ctrl_list2
## 940 ctrl_list2
## 941 ctrl_list2
## 942 ctrl_list2
## 943 ctrl_list2
## 944 ctrl_list2
## 945 ctrl_list2
## 946 ctrl_list2
## 947 ctrl_list2
## 948 ctrl_list2
## 949 ctrl_list2
## 950 ctrl_list2
## 951 ctrl_list2
## 952 ctrl_list2
## 953 ctrl_list2
## 954 ctrl_list2
## 955 ctrl_list2
## 956 ctrl_list2
## 957 ctrl_list2
## 958 ctrl_list2
## 959 ctrl_list2
## 960 ctrl_list2
## 961 ctrl_list2
## 962 ctrl_list2
## 963 ctrl_list2
## 964 ctrl_list2
## 965 ctrl_list2
## 966 ctrl_list2
## 967 ctrl_list2
## 968 ctrl_list2
## 969 ctrl_list2
## 970 ctrl_list2
## 971 ctrl_list2
## 972 ctrl_list2
## 973 ctrl_list2
## 974 ctrl_list2
## 975 ctrl_list2
## 976 ctrl_list2
## 977 ctrl_list2
## 978 ctrl_list2
## 979 ctrl_list2
## 980 ctrl_list2
## 981 ctrl_list2
## 982 ctrl_list2
## 983 ctrl_list2
## 984 ctrl_list2
## 985 ctrl_list2
## 986 ctrl_list2
## 987 ctrl_list2
## 988 ctrl_list2
## 989 ctrl_list2
## 990 ctrl_list2
## 991 ctrl_list2
## 992 ctrl_list2
## 993 ctrl_list2
## 994 ctrl_list2
## 995 ctrl_list2
## 996 ctrl_list2
## 997 ctrl_list2
## 998 ctrl_list2
## 999 ctrl_list2
## 1000 ctrl_list25.1.4 get_params
The various parameters used during the initialization of the metagene object,
the production of the table and the production of the plot are saved and can
be accessed with the get_params function:
mg <- get_demo_metagene()
mg$get_params()## $padding_size
## [1] 0
##
## $verbose
## [1] FALSE
##
## $bam_files
## align1_rep1
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep1.bam"
## align1_rep2
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align1_rep2.bam"
## align2_rep1
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep1.bam"
## align2_rep2
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/align2_rep2.bam"
## ctrl
## "/tmp/RtmppBAi4q/Rinst1436b219a69a61/metagene/extdata/ctrl.bam"
##
## $force_seqlevels
## [1] FALSE
##
## $flip_regions
## [1] FALSE
##
## $assay
## [1] "chipseq"
##
## $df_needs_update
## [1] TRUE
##
## $df_arguments
## [1] ""
##
## $table_needs_update
## [1] TRUE5.1.5 get_design
To get the design that was used to produce the last version of the table,
you can use the get_design function:
mg$produce_table(design = get_demo_design())## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## produce data table : ChIP-Seq## Alternatively, it is also possible to add a design without producing the
## table:
mg$add_design(get_demo_design())
mg$get_design()## Samples align1 align2
## 1 align1_rep1.bam 1 0
## 2 align1_rep2.bam 1 0
## 3 align2_rep1.bam 0 1
## 4 align2_rep2.bam 0 1
## 5 ctrl.bam 2 25.1.6 get_bam_count
To get the number of aligned read in a BAM file, you can use the
get_bam_count
function:
mg$get_bam_count(bam_files[1])## [1] 46355.1.7 get_regions
To get all the regions, you can use the get_regions function:
mg$get_regions()## GRangesList object of length 2:
## $list1
## GRanges object with 50 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 16103663-16105662 *
## [2] chr1 23921318-23923317 *
## [3] chr1 34848977-34850976 *
## [4] chr1 36368182-36370181 *
## [5] chr1 36690488-36692487 *
## ... ... ... ...
## [46] chr1 172081530-172083529 *
## [47] chr1 172081796-172083795 *
## [48] chr1 172147016-172149015 *
## [49] chr1 172205805-172207804 *
## [50] chr1 172260642-172262641 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
##
## $list2
## GRanges object with 50 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 3670499-3672498 *
## [2] chr1 5916399-5918398 *
## [3] chr1 9699210-9701209 *
## [4] chr1 9907639-9909638 *
## [5] chr1 10718946-10720945 *
## ... ... ... ...
## [46] chr1 81075951-81077950 *
## [47] chr1 85108854-85110853 *
## [48] chr1 85960056-85962055 *
## [49] chr1 86110971-86112970 *
## [50] chr1 87155522-87157521 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsIt is also possible to extract a subset of the regions with the get_regions
function:
mg$get_regions(region_names = c(regions[1]))## GRangesList object of length 1:
## $list1
## GRanges object with 50 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 16103663-16105662 *
## [2] chr1 23921318-23923317 *
## [3] chr1 34848977-34850976 *
## [4] chr1 36368182-36370181 *
## [5] chr1 36690488-36692487 *
## ... ... ... ...
## [46] chr1 172081530-172083529 *
## [47] chr1 172081796-172083795 *
## [48] chr1 172147016-172149015 *
## [49] chr1 172205805-172207804 *
## [50] chr1 172260642-172262641 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths5.1.8 get_raw_coverages
To get the coverages produced during the initialization of the metagene
object, you can use the get_raw_coverages function. Please note that to save
space, metagene will only extract the coverages in the regions provided.
coverages <- mg$get_raw_coverages()
coverages[[1]]## RleList of length 22
## $chr10
## integer-Rle of length 130694993 with 1 run
## Lengths: 130694993
## Values : 0
##
## $chr11
## integer-Rle of length 122082543 with 1 run
## Lengths: 122082543
## Values : 0
##
## $chr12
## integer-Rle of length 120129022 with 1 run
## Lengths: 120129022
## Values : 0
##
## $chr13
## integer-Rle of length 120421639 with 1 run
## Lengths: 120421639
## Values : 0
##
## $chr14
## integer-Rle of length 124902244 with 1 run
## Lengths: 124902244
## Values : 0
##
## ...
## <17 more elements>length(coverages)## [1] 5It is also possible to extract a subset of all the coverages by providing the filenames:
coverages <- mg$get_raw_coverages(filenames = bam_files[1:2])
length(coverages)## [1] 25.1.9 get_normalized_coverages
The get_normalized_coverages function works exactly like the
get_raw_coverages function except that it returns the coverages in read per
million aligned (RPM).
5.2 Chaining functions
Every function of metagene (except for the getters) invisibly return a pointer to itself. This means that the functions can be chained:
rg <- get_demo_regions()
bam <- get_demo_bam_files()
d <- get_demo_design()
title <- "Show chain"
mg <- metagene$new(rg, bam)$produce_table(design = d)$plot(title = title)## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="ctrl.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align1_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep1.bam": No such file or
## directory## Warning in normalizePath(bam_files): path[1]="align2_rep2.bam": No such file or
## directory## produce data table : ChIP-Seq## produce data frame : ChIP-Seq## Plot : ChIP-Seq
5.3 Copying a metagene object
To copy a metagene object, you have to use the clone function:
mg_copy <- mg$clone()6 Managing large datasets
While metagene try to reduce it’s memory usage, it’s possible to run into
memory limits when working with multiple large datasets (especially when there
is a lot of regions with a large width).
One way to avoid this is to analyse each dataset seperately and then merge just before producing the metagene plot:
mg1 <- metagene$new(bam_files = bam_files, regions = regions[1])
mg1$produce_data_frame()
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## produce data table : ChIP-Seq
## produce data frame : ChIP-Seq
mg2 <- metagene$new(bam_files = bam_files, regions = regions[2])
mg2$produce_data_frame()
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align1_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep1": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="align2_rep2": No such file or
## directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## Warning in normalizePath(bam_files): path[1]="ctrl": No such file or directory
## produce data table : ChIP-Seq
## produce data frame : ChIP-SeqThen you can extract the data.frames and combine them with rbind:
df1 <- mg1$get_data_frame()
df2 <- mg2$get_data_frame()
df <- rbind(df1, df2)Finally, you can use the plot_metagene function to produce the metagene plot:
p <- plot_metagene(df)## Plot : ChIP-Seqp + ggplot2::ggtitle("Managing large datasets")
7 Comparing profiles with permutations
It is possible to compare two metagene profiles using the permutation_test
function provided with the metagene package. Please note that the permutation
tests functionality is still in development and is expected to change in future
releases.
The first step is to decide which profiles we want to compare and extract the corresponding tables :
tab <- mg$get_table()
tab0 <- tab[which(tab$region == "list1"),]
tab1 <- tab0[which(tab0$design == "align1"),]
tab2 <- tab0[which(tab0$design == "align2"),]Then we defined to function to use to compare the two profiles. For this, a
companion package of metagene named similaRpeak provides
multiple metrics.
For this example, we will prepare a function to calculate the RATIO_NORMALIZED_INTERSECT between two profiles:
library(similaRpeak)
perm_fun <- function(profile1, profile2) {
sim <- similarity(profile1, profile2)
sim[["metrics"]][["RATIO_NORMALIZED_INTERSECT"]]
}We then compare our two profiles using this metric:
ratio_normalized_intersect <-
perm_fun(tab1[, .(moy=mean(value)), by=bin]$moy,
tab2[, .(moy=mean(value)), by=bin]$moy)
ratio_normalized_intersect## [1] 0.7387951To check if this value is significant, we can permute the two tables that were used to produce the profile and calculate their RATIO_NORMALIZED_INTERSECT:
permutation_results <- permutation_test(tab1, tab2, sample_size = 50,
sample_count = 1000, FUN = perm_fun)Finally, we check how often the calculated value is greater than the results of the permutations:
sum(ratio_normalized_intersect >= permutation_results) /
length(permutation_results)## [1] 0.026