---
title: "SeqPlots GUI manual"
author: "Przemyslaw Stempor"
date: "September 12, 2014"
output:
BiocStyle::html_document:
toc: true
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```
Welcome to **SeqPlots**
=======================
> SeqPlots - An interactive tool for visualizing NGS signals and sequence motif
> densities along genomic features using average plots and heatmaps.
Summary
-------
SeqPlots is a web browser tool for plotting average track signals (e.g.
read coverage) and sequence motif densities over user specified genomic
features. The data can be visualized in linear plots with error
estimates or as series of heatmaps that can be sorted and clustered. The
software can be run locally on a desktop or deployed on a server and
allows easy data sharing. SeqPlots pre-calculates and stores binary
result matrices, allowing rapid plot generation. Plots can also be run
in batch.
Key features
------------
- Easy to use web interface (R or shell expertise not required)
- Web server or desktop versions
- Generates publication quality plots out of the box
- Plots average signals or heatmaps
- Accepts Wiggle, BedGraph, BigWiggle, and GFF and BED formats
- Calculates motif density from reference genome packages
- Tracks and features are searchable and old calculations stored
- Converts tracks to binary BigWiggle format for rapid data extraction
and efficient storage
- Implemented using Shiny R framework providing internet browser
reactive GUI and session based connectivity (websocets)
Adding and managing files
=========================
Supported file formats
----------------------
Tracks:
- BigWig (.bw) - http://genome.ucsc.edu/FAQ/FAQformat.html\#format6.1
- Wiggle (.wig) - http://genome.ucsc.edu/goldenPath/help/wiggle.html
- BedGraph (.bdg) -
http://genome.ucsc.edu/goldenPath/help/bedgraph.html
Features:
- BED - http://genome.ucsc.edu/FAQ/FAQformat.html\#format1
- GFF - http://genome.ucsc.edu/FAQ/FAQformat.html\#format3
- GTF (with .gff extension) -
http://genome.ucsc.edu/FAQ/FAQformat.html\#format4
Files must be formatted according to UCSC guidelines. All widely used
chromosome names conventions are accepted, e.g. for human files either
'chr1' or '1' can be used, however these conventions should not be mixed
within single files.
Adding files
------------
Pressing the `Add files` button brings up the **file upload panel**.
You can drag and drop files here or press the `Add files...` button to
opens a file selection menu. Before starting the upload the following
mandatory information must be provided about each file:
- User ID
- Reference genome - drop-down menu containing reference genome
package currently installed in R
Comments are optional.
The contents of the a text field can be copied to all files by clicking
the icon at the left of the field. The default values can be set using
`Set defaults...` button. Default values are stored using the browser
cookies, and the settings will be remembered across different sessions
as long as the same web browser is used. File extensions that are not
supported will raise an error.
Individual files can be uploaded by pressing 'start' next to the file
name or all files can be uploaded at once by pressing the `Start upload`
button at the top of **file upload panel**.
During the upload process a progress bar is displayed. After upload
SeqPlots gives a message that upload was successful or or gives an error
message. Common errors are misformatted file formats or chromosome names
do not matched the reference genome. For more information please refer
to [errors documantation](Errors%20explained)
To dismiss the upload window, click on `X` or outside the window.
Downloading and removing files
------------------------------
Clicking the `New plot set` button brings up the **file collection
window**. The primary function of this window is to choose signal tracks
and feature files to use for calculating the plots. However, it also
provides basic file management capabilities. Information on files can be
reviewed and files can be downloaded or deleted. Fields can be searched,
filtered and sorted by any column. The red `x` button on the right site
of file table removes a single file from the collection, while
`Remove selected files` button will erase all selected files.
Running the plot-set jobs
=========================
The **file collection modal** allows choosing signal tracks and feature
files from the collection to calculate average plots and heat maps.
Press `New plot set` button to bring it up. If you wish tu upload more
files please refer to [adding new files
documantation](Adding%20and%20managing%20files).This window have three
tabs:
- `Tracks` gather signal files, that is Wiggle, BigWiggle and BedGraph
- `Features` gather genomic feature files, that is BED, GFF and GTF
- `Sequence features` allows to set up the sequence motif density
track
Selecting files
---------------
Both `Tracks` and `Features` tabs allow to review all the information
about files, filter them and sort by any column. The "Search:" dialog
allows to quickly filter the files by any field, while dropdowns below
the file grid allow for more advanced filtering on specific columns.
Files are selected by clicking on file name, or any other part of the
row beside `Show comment` and `Download` or `Remove` buttons. Chosen
files are highlighted in light blue. Clicking the file name again will
cancel the selection. At least one signal track or motif and one feature
file must be selected before starting the calculation.
Setting up plot options
-----------------------
The set of options controlling the plot settings is available below the
file grid/motif option:
1. **`Bin track @ [bp]:`** - this numeric input determines the
resolution of data acquisition; the default value 10 means that 10bp
intervals within the plotting range will be summarized by
calculating the mean. Higher values increases the speed of
calculation and produces smoother plots. See the
[explanations](Terms).
2. **`Choose the plot type`** - this radio box determines the mode of
plots
- *`Point Features`* - plot a range around feature start or end
depending on it's directionality, see [explanations](Terms)
- *`Midpoint Features`* - calculates the middle point of the
feature and plot a range around it
- *`Anchored Features`* - scale the features to given
pseudo-length and plots the range upstream of the beginning and
downstream of the end
3. **`Ignore strand`** - the directionality (strand) will be ignored,
that its `+`, `-` and `*` ranges will be centered on start and
plotted in the same direction
4. **`Ignore zeros`** - the signal values equal to 0 in the track will
be ignored, that is will be excluded from mean and errors
calculation
5. **`Calculate heatmap`** - this checkbox determines if heat map
matrix should be saved; uncheck it will speed up calculation
calculation, but only average plots will be feasible in this plot
set.
6. **`Plotting distances in [bp]`** - the distances in to be plotted:
- *`Upsteram`* - the plotting distance in base pairs upstream to
the feature
- *`Anchored`* - the pseudo-length, to which the features will be
extended or shrunk using linear approximation (only for anchored
plots)
- *`Downstream`* - the plotting distance in base pairs downstream
to the feature
Plotting sequence motif density
-------------------------------
`Sequence features` tab allows to calculate and plot the motif density
around genomic features using the reference sequence package. Motif
plots can be mixed with track files' signal plots. The following options
can be set here:
1. **`DNA motif`** - the DNA motif
2. **`Sliding window size in base pairs [bp]`** - the size of the
sliding window for motif calculation. The value (number of matching
motifs within the window) is reported in the middle of the window,
e.g. if window is set to 200bp, DNA motif is "GC" and there are 8
CpGs in first 200 bp of the chromosome the value 8 will be reported
at 100th bp.
3. **`Display name`** - The name of the motif that will be shown in key
and heatmap labels. Leave blank to use `DNA motif` value.
4. **`Plot heatmap or error estimates`** - this checkbox determines if
heatmap matrix and error estimates should be calculated. If
unchecked much faster algorithm will be used for motif density
calculation, but only the average plot without the error estimates
will be available.
5. **`Match reverse complement as well`** - determined if reverse
complement motif should be reported as well. For example the TATA
motif will report both TATA and ATAT with this option selected.
Clicking `Add` button adds the motif to plot set, while `Reset All`
clears the motif selection. On the right side from motif setting panel
is the list summary of included motifs.
Starting the plot set calculation
---------------------------------
The option are executed by pressing `Run calculation` button. This
dismisses the **file collection modal** and brings up the calculation
dialog, which shows the progress. On Linux and Mac OS X (systems
supporting fork based parallelization) the calculation can be stopped
using the `Cancel` button - this will bring back all settings in **file
collection modal**.
After the successful execution the **plot array** will appear. In case
of error the informative error pop-up will explain the problem. Please
reffer to error section for further information.
Plotting
========
> This section focuses on average (line) plots and options common
> between these and heatmaps. For heatmap options please refer to
> heatmps documentation.
Previewing plot
---------------
After calculating or loading plot set select the pairs of features and
tracks/motifs using **plot array** checkboxes. Clicking on the column
name (tracks/motifs) toggles the whole column selection, Similarly
clicking on row name (features) toggles the whole row selection.
Clicking on top-left most cell of **plot array** toggles the selection
of whole array.
If at least one pair on **plot array** is selected pressing `Line plot`
button produces average plot preview and `Heatmap` button the heatmap
preview. Finally, pressing `refresh` button or [RETURN] key from
keyboard applies the new selection and options. These operations are
done automatically in [reactive mode](AdvancedOptions).
Below the plotting buttons are seven panels. On application start the
first panel responsible for bringing file upload, management and plot
set calculation modals is active. The further three panels hold common
plot settings.
Titles and axis panel
---------------------
This panel groups settings influencing the plot main title, axis labels,
various font sizes plus vertical and horizontal plot limits.
- `Title` - The main title of the plot, shown in top-center part of
the figure; default empty
- `X-axis label` - Label shown below horizontal axis; default empty
- `Y-axis label` - Label shown below vertical axis; default empty
- `Title font size` - Font size of the title in points (point = \~1/72
an inch for standard A4 output); default 20 points
- `Labels font size` - Font size of axis labels in points; default 16
points
- `Axis font size` - Controls axis ticks font size, that is size of
the numbers indicating position in base pairs on X-axis and means
signal value on X-axis; default 14 points
- `Set X-axis limits` - Set hard plotting limits for X-axis; default
values are whole range chosen during plot set calculation
- `Set Y-axis limits` - Set hard plotting limits for Y-axis; default
values are a range between lowest and highest mean signal extended
by error estimate
Guide lines and data scaling
----------------------------
Controls in this panel controls the display of guide lines and error
estimates, and allows to log scale the signal prior to plotting.
- `Transform signal` - if set to *`Log2 transform`* performs log2
transformation of the signal prior to plotting; default setting is
*`Do not transform`*
- `Show vertical guide line` - show the vertical line at point 0 -
beginning of the feature or midpoint and end of the pseudo-length
scaled features (only for anchored plots); turn on by default
- `Show horizontal guide line` - show the horizontal line at user
determined height; turn off by default
- `Show error estimates` - show error standard error and 95%
confidence interval as fields, if turned off only the line
representing the mean signal is shown; turn on by default
Keys, labels and colors panel
-----------------------------
This panel groups two types of controls. `Colors`, `Label` and
`Priority/Order` are a checkboxes revealing further controls on **plot
set grid**, specific for a feature-track pair or sub-heatmap.
`Show plot key`, `Show error estimate key` and `Legend font size` re
global controls specific for average plots. Inputs on **plot set grid**
do not have specific labels, but the tooltip explaining their meaning is
shown on mouse cursor hover.
- `Colors` - checkboxes revealing a color picker on **plot set grid**.
This input allows to control the colors of specific feature-track
pair average plots or sub-heatmaps. In browser supporting the color
picker 'e.g Chrome' the system dialog will show up. In other
browsers (e.g. Firefox) the javaScript color picker will be
initialized.
- `Label` - checkboxes revealing a label text input **plot set grid**.
This controls the names shown on the **key** with average plots or
the heatmap top labels.
- `Priority/Order` - checkboxes revealing numeric input on **plot set
grid**. These number determine the order of average plots and
hetamaps. Feature-track pair with the highest priority will be
listed on the top of **key** for average plots and left-most for
heatmaps.
- `Show plot key` - shows the key giving the color to feature-track
pair label mapping. If turned on the additional drop-down allows to
choose the position on the plot, top-right by default
- `Show error estimate key` - shows the key gexplaining the meaning of
error fields. If turnedon the additional drop-down allows to choose
the position on the plot, top-left by default
- `Legend font size` - set the size of font used to plot the keys; 12
default
Plotting and adjusting heatmaps
===============================
Heatmaps can be more informative comparing to average plots. If the
variability in signal along given genomic feature comes from different
biological classes the average plot might not be sufficient for proper
examination of the signal or even misleading. SeqPlots implements
heatmap plotting in similar way to Galaxy, plotting track-feature pairs
as sub-heatmaps horizontally aligned on single figure. All sub-heatmaps
must have the same number of data rows, hence in single plot mode
simultaneous plotting is possible only on single feature or features
containing exact same number of ranges. The heatmaps can be sorted and
clustered by k-means, hierarchical clustering and super self organising
maps (SupreSOM).
Heatmap setup tab
-----------------
This tab groups heatmap specific options, that allows to manipulate
various data processing and graphical options.
- **`Preview heatmap`** - this checkbox indicates whether the preview
of average plot or heatmap will be produced, its state is linked to
`Line plot` and `Heatmap` buttons above the option tabs. It can be
toggled from keyboard by using Ctrl/Cmd+H key combination
- **`Sort heatmap rows by mean signal`** - sorts the heatmap rows
based on the mean value of each row across all sub-heatmps. The
highest values on top. Turned off by default.
- **`Clustering algorithm`** - determines which clustering algorithm
(k-means, hierarchical or SupreSOM) will be used to produce the
clusters or turns of the clustering while *`do not cluster`* is
selected. K-means by default.
- **`Choose individual heatmaps for sorting/clustering`** - similarly
to `Colors`, `Label` and `Priority/Order`, which also works for
heatmaps, this checkbox reveals new control on on **plot set grid**
that determines if given sub-heatmap should be included in plotting
and/or clustering. The excluded sub-plots will be plotted and
clustered/ordered along with other sub-heatmaps, but their values
would not influence the clustering/sorting. By default all
sub-heatmaps are included. Following example shows hierarchical
clustering on both heatmaps included (left) and second heatmap
excluded (right):
- **`Heatmaps have individual color keys`** - this option determines
if each sub-heatmap should have separate color key (plotted below
the heatmap) or single, common key should be calculated for all
sub-plots (plotted rightmost). By default all sub-heatmap have its
own color keys. The example below show the difference between
separate (left) and common (right) color keys:
- **`Set default color key limits`** - this option determines the
limits in mapping the numerical values to the colors. The range of
generated is dependent on these options. Values smaller and lower
than given limits will not produce further increase of heatmap color
range, but will be plotted in the same color as closest limit value.
If this checkbox is not selected, these values are auro-generated
using **`Color key scaling`** parameter. If it is of two numerical
fields are shown to hard set the limits.
- **`Color key scaling`** - this slider influence how color key
limits are generated. For example, 0.01 (default value)
calculates limits using data range from 1-99 percentile of
available data points. 0.1 uses data range from 10-90
percentile. The general formula for limit is: [quantile(data,
`Color key scaling`); quantile(data, 1-`Color key scaling`)]
- **`min`** and **`max`** numeric inputs - in opposite to auto
generating color key limits they can be directly given as
numeric values
- **`Set individual color key limits`** - this option is similar to
manual set up of color key limits, but allows to set up different
values for individual sub-heatmaps. When this checkbox is selected
**`min`** and **`max`** numeric inputs are revealed on **plot set
grid**
- **`Set default colorspace`** - When this option is selected three
color pickers are being shown. This allows to set up custom color
mappings for heatmaps. The following example below shows standard
jet colors (left), default blue color mapping after selecting the
checkbox (middle) and custom color selection (right):
Other options controlling heatmap appearance
--------------------------------------------
Many options from other tabs influence heatmap output. Here we provide
the list of these inputs, please refer to ["Viewing and manipulating
plots"](Viewing%20and%20manipulating%20plots) for further reference.
- **Titles and axis panel**
- `X-axis label` - Label shown below horizontal axis, drawn
separately for each sub-heatmap; default empty
- `Y-axis label` - Label shown next to vertical axis, drawn
separately for each sub-heatmap; default empty
- `Labels font size` - Font size for axis labels and main labels
of sub-heatmaps; default 16 points
- `Axis font size` - Controls axis ticks font size; default 14
points
- `Set X-axis limits` - Set hard plotting limits for X-axis;
default values are whole range chosen during plot set
calculation
- **Guide lines and data scaling panel**
- `Transform signal` - if set to *`Log2 transform`* performs log2
transformation of the signal prior to plotting; default setting
is *`Do not transform`*
- `Show vertical guide line` - show the vertical line at point 0 -
beginning of the feature or midpoint and end of the
pseudo-length scaled features (only for anchored plots); turn on
by default
- **Keys, labels and colors panel**
- `Colors` - for hetmaps this input allows to control the color
mapping of specific sub-heatmaps. The map allways start with
white (for low color key limit) and finishes with selected color
(for high color key limit).
- `Label` - allows to set up custom sub-heatmap top labels
- `Priority/Order` - The feature-track pairs with the highest
priority will be plotted as left-most sub-heatmaps.
- `Legend font size` - control the font size of common color key,
inactive if heatmaps have individual color keys; 12 default
Output files and batch operations
=================================
Plots can be downladed as portable document files (PDFs) by clicking
`Line plot` or `Heatmap` buttons in "Download:" section of **tool
panel** (above the plot preview).
Small buttons next to `Line plot` and `Heatmap` produce additional
output files:
- `i` button next to `Line plot` downloads the PDF containing average
plot keys
- `cluster diagram` button next to `Heatmap` downloads a cluster
report giving cluster assignments for each feature as a comma
separated value (CSV) spreadsheet.
The cluster report contains following columns:
- `chromosome` - the name of chromosome, contig or scaffold
- `start` - start of the feature (1 based chromosomal coordinate)
- `end` - end of the feature (1 based chromosomal coordinate)\
- `width` - width of the feature in base pairs
- `strand` - strand of the feature
- `metadata_...` - the annotation columns driven from original GFF/BED
e.g. gene name, score, group
- `originalOrder` - number of feature (row) in GFF/BED, can be used to
restore original order after sorting on cluster ID
- `ClusterID` - the numeric ID of the cluster, topmost cluster on
heatmap annotated with 1, and the bottom cluster with k, where k
equals to number of clusters selected, exported only if clustering
is enabled
- `SortingOrder` - the order imposed on heatmap by sorting by mean
row(s) values, exported only if sorting is enabled
- `FinalOrder` - the final order of heatmap's rows, this can be
influenced by sorting and clustering; 1 indicates topmost row
Sample report:
chromosome start end width strand metadata_group originalOrder ClusterID SortingOrder FinalOrder
chrI 9065087 9070286 5200 + g1 1 1 3 3
chrI 5171285 5175522 4238 - g1 2 3 50 43
chrI 9616508 9618109 1602 - g1 3 3 13 43
chrI 3608395 3611844 3450 + g1 4 3 11 12
Table view:
chromosome start end width strand metadata\_group originalOrder ClusterID SortingOrder FinalOrder
------------ --------- --------- ------- -------- ----------------- --------------- ----------- -------------- ------------
chrI 9065087 9070286 5200 + g1 1 1 3 3
chrI 5171285 5175522 4238 - g1 2 3 50 43
chrI 9616508 9618109 1602 - g1 3 3 13 43
chrI 3608395 3611844 3450 + g1 4 3 11 12
PDF output size
---------------
The last tab (`Batch operation and setup`) on the **tool panel**
includes batch operations and various other settings including PDF
output size. By default the output PDF will be A4 landscape. This can be
changed using the drop-down list to following settings:
- `user defined` - this option reveals two numeric inputs that allows
to set output PDF width and height. The values must be given in
inches.
- `Legal rotated` - US Legal landscape: 14" by 8.5"
- `A4` - A4 portrait: - 8.27" x 11.69"
- `Letter` - US Letter portrait: 8.5" x 11"
- `Legal` - US Legal portrait: 8.5" x 14"
- `Executive` - a.k.a Monarch paper: 7.25 x 10.5"
Batch operations
----------------
Controls to plot multiple plots at once are located on the
`Batch operation and setup` tab, just below PDF paper options. It is
possible to output the plots to multipage PDF, plot an array of plots on
a single page (for average plots) or mix these options together.
The first drop-down controls the type of the plot - either average or
heatmap. The second drop down determines the strategy to traverse the
**plot grid**. The options include:
- `single` - every single feature-track pair will be plotted on
separate plot
- `rows` - the **plot grid** will be traversed by rows, which means
one plot that contains all tracks per feature will be prepared
- `columns` - the **plot grid** will be traversed by columns, which
means one plot that contains all features per tracks will be
prepared
The `multi plot grid` option controls how many plots will be placed on
each page of the PDF output, e.g. 1x1 means one plot per one page, while
3x4 means 3 columns and 4 rows of plots. If number of plots exceeds the
number of slots on page the new page will be added to the PDF.
`Filter names` will apply a filter to plot titles, which are based on on
uploaded file names. For example, if you uploaded 100 files starting
with a prefix of "my\_experiment\_", you can remove this fragment from
each plot title and/or heatmap caption by putting this string in
`Filter names`.
Finally, pressing `Get PDF` produces the final output file. Please see
example below:
Saving and loading plot sets
============================
SeqPlots allows to save the plot sets as binary R files. This allows to
quickly load pre-calculated set for replotting. Furthermore, the saved
plot sets can be shared with other SePlots users.
Load or save plotset
--------------------
Following controls are available on "Load or save plotset" panel:
- **`Load saved plot set`** - this drop-down list allows to select a
plotset. Once the Rdata binary file is selected the **plot grid**
will be shown instantaneously. Selecting the file reveals two
additional buttons:
- **`Remove dataset`** - this button deletes the selected saved
plot set from user data.
- **`Download plotset`** - this button saves a copy of the plotset
in selected location.
- **`Save current plot set`** - this control allows to save the
current plot set. Once the desired name of the file is put to the
text input the `Save` button will appear. You can use it after
calculating the plot set. It is also possible to save a copy of
loaded plot sets. The plot set binary files can be renamed simply by
loading them, saving a copy and deleting original source file.
All saved dataset can be found in `data location`/publicFiles. Any
SeqPlots Rdata binaries put in the folder will become available for
loading in **`Load saved plot set`** control.
Plot set files structure
------------------------
The plot sets files can be also directly loaded in R. This allows
further processing and customization of the plots. Data structure is a
nested list, which elements be accessed by `[[` R operator. The nesting
goes as follow:
- **`feature`** - R list
- **`track`** - R list
- `means` - numeric vector giving mean signal value for each
(binned) genomic position
- `stderror` - numeric vector giving standard error for each
(binned) genomic position
- `conint` - numeric vector giving 95% confidence interval for
each (binned) genomic position
- `all_ind` - numeric vector giving the genomic position in
base pairs
- `e` - character string giveing numeric vector giving the
indicates of anchored distance, NULL for point features
plots
- `desc` - auto generated title of the plot
- `heatmap` - numeric matrix, (binned) signal values for each
genomic position (columns) and each feature (rows)
The example structure:
```
List of 2
$ HTZ1_Differential_genes_TOP100_v2.gff:List of 2
..$ HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged.bw :List of 7
.. ..$ means : num [1:501] 2.52 2.52 2.52 2.53 2.54 ...
.. ..$ stderror: num [1:501] 0.114 0.112 0.111 0.11 0.109 ...
.. ..$ conint : num [1:501] 0.226 0.223 0.221 0.218 0.217 ...
.. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ...
.. ..$ e : NULL
.. ..$ desc : chr "HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged\n@HTZ1_Differential_genes_TOP100_v2"
.. ..$ heatmap : num [1:100, 1:501] 2.36 5.25 2.2 3.48 4.32 ...
..$ HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged.bw:List of 7
.. ..$ means : num [1:501] 2.36 2.35 2.35 2.36 2.38 ...
.. ..$ stderror: num [1:501] 0.126 0.125 0.125 0.126 0.125 ...
.. ..$ conint : num [1:501] 0.249 0.249 0.247 0.251 0.249 ...
.. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ...
.. ..$ e : NULL
.. ..$ desc : chr "HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged\n@HTZ1_Differential_genes_TOP100_v2"
.. ..$ heatmap : num [1:100, 1:501] 2.61 3.17 1.42 2.46 4.26 ...
$ HTZ1_Differential_genes_BOTTOM100.gff:List of 2
..$ HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged.bw :List of 7
.. ..$ means : num [1:501] 1.57 1.57 1.58 1.6 1.62 ...
.. ..$ stderror: num [1:501] 0.0996 0.0985 0.1003 0.1022 0.1018 ...
.. ..$ conint : num [1:501] 0.198 0.195 0.199 0.203 0.202 ...
.. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ...
.. ..$ e : NULL
.. ..$ desc : chr "HTZ1_JA00001_IL1andIL2_F_N2_L3_NORM_linear_1bp_IL010andIL009_averaged\n@HTZ1_Differential_genes_BOTTOM100"
.. ..$ heatmap : num [1:100, 1:501] 1.64 1.37 1.61 1.77 1.86 ...
..$ HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged.bw:List of 7
.. ..$ means : num [1:501] 1.94 1.94 1.95 1.96 1.97 ...
.. ..$ stderror: num [1:501] 0.123 0.123 0.124 0.126 0.128 ...
.. ..$ conint : num [1:501] 0.244 0.245 0.246 0.251 0.253 ...
.. ..$ all_ind : num [1:501] -1000 -995 -990 -985 -980 -975 -970 -965 -960 -955 ...
.. ..$ e : NULL
.. ..$ desc : chr "HTZ1_JA00001_IL3andIIL5_F_lin35_L3_NORM_linear_1bp_IL008andIL011_averaged\n@HTZ1_Differential_genes_BOTTOM100"
.. ..$ heatmap : num [1:100, 1:501] 1.61 1.37 1.29 3.04 3.77 ...
```
Advanced options
================
Some additional SeqPlots options can be located at very bottom of
`Bach operation and setup` tab:
- `Keep 1:1 aspect ratio in batch mode` - This option guarantee that
the ratio between X- and Y-axis height will be 1, hence the produced
plots will be rectangular in batch mode. This prevent stretching the
plots while fitting the single row or column to one page. Turned on
by default.
- `Always keep 1:1 aspect ratio` - This checkbox extends previous
behaviours on single plots - the figures always will be rectangular,
no matter the paper size. Turned off by default.
- `Reactive plotting` - While this checkbox is selected, all plotting
operation ar executed on fly. That means changing the font size,
title caption, etc. will execute the plotting routine and changes
will be visible on preview. `Reactive plotting` might be useful for
exploratory data analysis using plots. However, it is not
recommended while working with big heatmap plots. Touleble from
keyboard by pressing [ctrl/cmd+R]. Turned off by default.
- `Use multithreading for calculations` - This option is available
only on desktop instances of SeqPlots under Mac OS X and Linux.
While turned off R will not fork the child processes for plotting
and plot set calculations. It is useful for debugging, since in
single process mode all warning/errors will be directly printed to R
console. Also might increase the performance for plotting small
average plots. Turned off by default.
Error messages
==============
Adding the files:
-----------------
Problem with line N: "line_text" [internal_error]
> The import of feature file (GFF or BED) was not successful due to
> mis-formatted file.
* * * * *
Chromosome names provided in the file does not match ones defined in
reference genome.
INPUT: [chr3R, chr2L, chr2R, chr3L]
GENOME: [chrI, chrII, chrIII, chrIV, chrV, ...]
> There are unexpected chromosome names in input file. Following
> genomes: *Arabidopsis thaliana, Caenorhabditis elegans,
> Cyanidioschyzon\_merolae, Drosophila melanogaster, Homo sapiens, Oryza
> sativa, Populus trichocarpa, Saccharomyces cerevisiae and Zea mays*
> support chromosome names remapping between different naming
> conventions, including: AGPv2, ASM9120v1, Ensembl, JGI2\_0, MSU6,
> NCBI, TAIR10 and UCSC. If you see above error in one of these genomes
> there are still unexpected names after the correction. The problematic
> chromosome names are given in the error message. Remove GFF/BED lines
> corresponding to them or upgrade the genome to one containing proper
> naming. Alternatively set genome to NA.
* * * * *
File already exists, change the name or remove old one.
> File named like this already exists in the database, it is impossible
> to have two files sharing same filename.
* * * * *
ERROR: solving row 300: negative widths are not allowed
> The the row 300 have end coordinate smaller than beginning, hence the
> width in negative. To fix it the start and stop indicates should be
> swapped. This error often happens when negative strand (-) ranges are
> misformatted.
* * * * *
Explanations
============
* * * * *
- "**feature**" - a genomic interval defined by **chromosome** name,
**start** and **end** positions and the **directionality** (strand).
The end must always be a bigger number than start, so the width of
the range is not negative. Start and end means here the numeric
start of the interval and should not be confused with TSS and TTS.
For example, in BED format this information is stored in following
text tab delimited format: `chr7 127471196 127472363 . . +`
* * * * *
- "**directionality**" - the strand of genomic feature, determining if
the plotting range should be anchored around the star or and, and
the direction in which signal is being processed to create the
average track or heatmap. Unknown directionality is marked by `*`
and treated as `+` for calculations.
* * * * *
- "**track**" - the file assigning the continuous signal (score) to
genomic locations across the chromosomes. The signal usually comes
from sequencing experiments, like ChIP-seq, RNA-seq, DNase-seq,
MNase-seq, or from computational tools, for example nucleosome
occupancy prediction, CpG density.
For example, in BedGraph format this information is stored in
following text tab delimited format: `chr19 49302300 49302600 -0.75`
* * * * *
- "**reference genome package**" - the R BSgemome package containing
the full reference sequence for given species. It is also used to
provide universal chromosome names and chromosome lengths taht are
used as plotting boundaries.
* * * * *
- "**reads coverage**" - The basic way to calculate the signal from
sequencing based assays. The numeric representation shows how much
reads was aligned to given genomic location. This can be a proxy to
protein-DNA binding (ChIP-seq) or the expression (RNA-seq). Can be
calculated using BedTools:
http://bedtools.readthedocs.org/en/latest/content/tools/genomecov.html
Also known as `pileups`.
* * * * *
Session Information
-------------------
```{r echo=FALSE}
sessionInfo()
```
References
==========
**R project and Bioconductor**
- R Core Team (2014). R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria.
URL http://www.R-project.org/.
- Bioconductor: Open software development for computational biology and
bioinformatics R. Gentleman, V. J. Carey, D. M. Bates, B.Bolstad,
M.Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, and others 2004,
Genome Biology, Vol. 5, R80. URL http://www.bioconductor.org/.
- RStudio and Inc. (2014). shiny: Web Application Framework for R. R package
version 0.10.1. http://shiny.rstudio.com/
- **Other CRAN packages:** digest, DBI,RSQLite, RJSONIO, plotrix, fields,
grid, kohonen, Cairo and parallel
- **Bioconductor packages:** IRanges, BSgenome, Rsamtools, rtracklayer,
GenomicRanges and Biostrings
**JavaScript and CSS**
- jQuery framework - http://jquery.com
- Bootstrap - http://getbootstrap.com
- DataTables, Table plug-in for jQuery - http://www.datatables.net
- jQuery File Upload Plugin - https://github.com/blueimp/jQuery-File-Upload
- jQuery throttle -
http://benalman.com/projects/jquery-throttle-debounce-plugin/
- jQuery Cookie Plugin - https://github.com/carhartl/jquery-cookie
- Modernizer JS library - http://modernizr.com
- JavaScript Templates - https://github.com/blueimp/JavaScript-Templates
- JavaScript Color Picker - http://jscolor.com
- md5-js - https://github.com/wbond/md5-js
- Font Awesome - http://fortawesome.github.io/Font-Awesome
- Google Fonts - https://www.google.com/fonts
- jQuery user interface - http://jqueryui.com (documentation)
- jquery.tocify.js: jQuery Table of Contents -
https://github.com/gfranko/jquery.tocify.js (documentation)
- Strapdown https://github.com/arturadib/strapdown (documentation)
- Bootswatch themes - http://bootswatch.com (documentation)
- google-code-prettify - https://code.google.com/p/google-code-prettify
(documentation)
- marked - https://github.com/chjj/marked (documentation)
**Important conceptual contribution to the project**
- Liu T, Ortiz J, Taing L, Meyer C, Lee B, Zhang Y, Shin H, Wong S, Ma J,
Lei Y, et al. 2011. [Cistrome: an integrative platform for transcriptional
regulation studies.](http://www.ncbi.nlm.nih.gov/pubmed/21859476) Genome
Biology 12: R83.
- Thomas Williams, Colin Kelley and others (2010). Gnuplot 4.4: an
interactive plotting program. URL http://www.R-project.org/.
- Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H.,
Zahler, A.M. and Haussler, a. D. (2002). [The Human Genome Browser at
UCSC](http://www.ncbi.nlm.nih.gov/pubmed/12045153). Genome Research.
12:996-1006.
- Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. (2010). [BigWig
and BigBed: enabling browsing of large distributed datasets.
](http://www.ncbi.nlm.nih.gov/pubmed/20639541) Bioinformatics.
1;26(17):2204-7
- Nicol, J.W., Helt, G.A., Blanchard, S.G., Raja, A. and Loraine, A.E.
(2009). [The Integrated Genome Browser: free software for distribution
and exploration of genome-scale datasets.
](http://www.ncbi.nlm.nih.gov/pubmed/19654113) Bioinformatics (Oxford,
England). 25:2730-1.
- Thorvaldsdottir, H., Robinson, J.T. and Mesirov, J.P. (2012). [Integrative
Genomics Viewer (IGV): high-performance genomics data visualization and
exploration.](http://www.ncbi.nlm.nih.gov/pubmed/22517427) Briefings
in bioinformatics. bbs017
**Server deployment**
- Shiny Server - https://github.com/rstudio/shiny-server
- ShinyApps - https://github.com/rstudio/shinyapps
**Publications containing figures made by SeqPlots**
- Chen RA, Stempor P, Down TA, Zeiser E, Feuer SK, Ahringer J. [Extreme HOT
regions are CpG-dense promoters in C. elegans and humans.
](http://www.ncbi.nlm.nih.gov/pubmed/24653213)
Genome Res 24(7):1138-1146 Jul 2014