tfea 1.1.4

Transcription Factor Enrichment Analysis

Transcription Factor Enrichment Analysis (TFEA)

Table of Contents

1. Pipeline
2. Installation and Requirements
   • TFEA
   • DESeq
   • Bedtools
   • Samtools
   • MEME Suite
     • Image Magick
   • FIJI Modules
3. Basic Usage
   • Testing TFEA
   • Running TFEA
4. Advanced Usage
   • Configuration File
   • Batch Correction
   • Using SBATCH
   • Pre-Processed Inputs
   • Secondary Analysis (MD, MDD)
   • Measuring TF FPKM
   • Generating Simulated Data
   • Rerunning TFEA
   • Help Message
5. Example Output
6. Contact Information

TFEA Pipeline

Installation and Requirements

TFEA

To install, this package and all python3 dependencies:

python3 -m pip install tfea

This should take no longer than several minutes.

Once successfully installed, you should be able to run the tfea command from anywhere, try:

TFEA --help

Note: If you plan to run TFEA only on FIJI using the --sbatch flag, then you only need to install DESeq and DESeq2. Otherwise, follow the instructions below for installing all TFEA dependencies.

DESeq

TFEA uses DESeq or DESeq2 (depending on replicate number) to rank inputted bed files based on fold change significance. If on FIJI, make sure all gcc modules are unloaded before installing DESeq or DESeq2. This can be accomplished with:

module unload gcc

or

module purge

To install DESeq and DESeq2 type in your terminal:

R

> if (!requireNamespace("BiocManager", quietly = TRUE))
>   install.packages("BiocManager")

> BiocManager::install("DESeq")
> BiocManager::install("DESeq2")

Bedtools

TFEA uses Bedtools to do several genomic computations. Instructions for installing bedtools can be found here:

Bedtools Installation

If you are on FIJI compute cluster, bedtools is available as a module:

module load bedtools/2.25.0

Samtools

TFEA uses samtools to index bam files. Samtools download and install instructions can be found here: Samtools Download and Install

If you are on FIJI compute cluster, bedtools is available as a module:

module load samtools/1.8

MEME Suite

TFEA uses the MEME suite to scan sequences from inputted bed files for motif hits using the background atcg distribution form inputted bed file regions. TFEA also uses the MEME suite to generate motif logos for html display. Instructions for downloading and installing the MEME suite can be found here:

MEME Download and Installation

If you are on FIJI compute cluster, the meme suite is available as a module:

module load meme/5.0.3

Image Magick

TFEA uses the meme2images script within MEME to produce motif logo figures. This requires Image Magick, which is a common linux utility package sometimes pre-installed on machines. To check if you have Image Magick installed try:

identify -version

If you do not have Image Magick installed, follow these instructions:

Image Magick Download and Installation

FIJI Modules

Below is a summary of all FIJI modules needed to run TFEA.

module load python/3.6.3
module load python/3.6.3/matplotlib/1.5.1
module load python/3.6.3/scipy/0.17.1
module load python/3.6.3/numpy/1.14.1
module load python/3.6.3/htseq/0.9.1
module load python/3.6.3/pybedtools/0.7.10

module load samtools/1.8
module load bedtools/2.25.0
module load meme/5.0.3

BasicUsage

Testing TFEA

To make sure TFEA is installed properly, run the following tests:

Note: If you chose to skip installations because you were going to run TFEA using the --sbatch flag, make sure you load the appropriate modules on FIJI or these tests will fail.

TFEA --test-install
TFEA --test-full

These should each take no longer than several minutes to run

If on a compute cluster with slurm the --sbatch flag is compatible with --test-full and is recommended on FIJI. Execute like so:

TFEA --test-full --sbatch [e-mail]

Running TFEA

Once you've run the above tests successfully, you should be ready to run the full version of TFEA. Below are the minimum required inputs to run the full TFEA pipeline. Test files are provided in './TFEA/test/test_files' within this repository.

TFEA --output ./TFEA/test/test_files/test_output \
--bed1 ./TFEA/test/test_files/SRR1105736.tfit_bidirs.chr22.bed ./TFEA/test/test_files/SRR1105737.tfit_bidirs.chr22.bed \
--bed2 ./TFEA/test/test_files/SRR1105738.tfit_bidirs.chr22.bed ./TFEA/test/test_files/SRR1105739.tfit_bidirs.chr22.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 DMSO --label2 Nutlin \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme

Advanced Usage

Configuration File

TFEA can be run exclusively through the command line using flags. Alternatively, TFEA can be run using a configuration file (.ini) that takes in flags as variables. For example:

TFEA --config ./TFEA/test/test_files/test_config.ini

This can be helpful to keep track of different TFEA runs and because you can use variables within the config file to clean up your input. For documentation on config files and what you can do with them see Supported INI File Structure and Interpolation of values (ExtendedInterpolation)

Notes:

1. Section headers (ex: [OUTPUT]) don't matter but you need to have at least ONE section header to be a viable .ini file.
2. Capitalization of variables doesn't matter.
3. Feel free to specify any additional variables you like (variables are bash-like), TFEA will only parse variables that match a flag input.
4. If an input is provided both as a flag and in a configuration file, TFEA prioritizes the command line flag input.

Below is an example of a configuration file (./test_files/test_config.ini):

[OUTPUT]
OUTPUT='./TFEA/test/test_files/test_output/'
LABEL1='Condition 1'
LABEL2='Condition 2'

[DATA]
TEST_FILES='./TFEA/test/test_files/'
BED1=[${TEST_FILES}+'SRR1105736.tfit_bidirs.chr22.bed',${TEST_FILES}+'SRR1105737.tfit_bidirs.chr22.bed']
BED2=[${TEST_FILES}+'SRR1105738.tfit_bidirs.chr22.bed',${TEST_FILES}+'SRR1105739.tfit_bidirs.chr22.bed']
BAM1=[${TEST_FILES}+'SRR1105736.sorted.chr22.subsample.bam', ${TEST_FILES}+'SRR1105737.sorted.chr22.subsample.bam']
BAM2=[${TEST_FILES}+'SRR1105738.sorted.chr22.subsample.bam', ${TEST_FILES}+'SRR1105739.sorted.chr22.subsample.bam']

[MODULES]
TEST_FILES='./TFEA/test/test_files/' #You need to re-initialize variables within each [MODULE]
FIMO_MOTIFS=${TEST_FILES}+'test_database.meme'
GENOMEFASTA=${TEST_FILES}+'chr22.fa'

[OPTIONS]
OUTPUT_TYPE='html'
PLOTALL=True

Batch Correction

The presence of batch effects is common in sequencing data. TFEA can account for batch effects when performing ROI ranking using DE-Seq using built in functions. To correct for batch effects, specify a comma-separated list of batch labels to apply to your bam files in order of bam1 then bam2. For example:

--bam1 condition1_batch1 condition1_batch2 condition1_batch3
--bam2 condition2_batch1 condition2_batch2 condition2_batch3
--batch 1,2,3,1,2,3

Using SBATCH

Specifying the --sbatch flag will submit TFEA to a compute cluster assuming you are logged into one. If the --sbatch flag is specified, it MUST be followed by an e-mail address to send job information to. For example:

TFEA --config ./TFEA/test/test_files/test_config.ini --sbatch [e-mail]

Additionally, the following flags can be used to change some of the job parameters and specify a python virtual environment:

  --cpus CPUS           Number of processes to run in parallel. Warning:
                        Increasing this value will significantly increase
                        memory footprint. Default: 1
  --mem MEM             Amount of memory to request forsbatch script. Default:
                        50gb
  --venv VENV           Full path to virtual environment.

Note: --cpus also works without the --sbatch flag. The --venv flag takes the root venv directory, it then activates the venv by calling source <venv path>/bin/activate

Using Pre-processed Inputs

TFEA has several pipeline elements to it that a user may bypass by providing downstream pre-processed files. These files can be generated by TFEA if running the full pipeline and may also be used to speed up reruns of TFEA. Below are the three types of pre-processed inputs, short descriptions, an example of the file, and a usage example with TFEA (in some cases there are other inputs needed to go along with the pre-processed file). If multiple pre-processed inputs specified, TFEA will use the most downstream one.

combined_file

A sorted (by chrom, start, stop) bed file containing regions of interest

Example (./test_files/test_combined_file.bed)

#chrom   start stop
chr22 10683195	10683999
chr22	16609343	16609405
chr22	16901069	16902599
chr22	17036962	17037636
chr22	17158022	17160214
...

Usage with TFEA

TFEA --output ./TFEA/test/test_files/test_output \
--combined_file ./TFEA/test/test_files/test_combined_file.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme

ranked_file

A ranked bed file with regions of interest.

Note: Specifying a ranked_file turns off some plotting functionality

Example (./test_files/test_ranked_file.bed)

#chrom	start	stop
chr22	50794870	50797870
chr22	21554591	21557591
chr22	50304644	50307644
chr22	39096295	39099295
chr22	31176104	31179104
...

Usage with TFEA

TFEA --output ./TFEA/test/test_files/test_output \
--ranked_file ./TFEA/test/test_files/test_ranked_file.bed \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme

fasta_file

A ranked fasta file with regions of interest (sequences must have unique names but these names aren't used for anything).

Note: Specifying a fasta_file turns off some plotting functionality

Example (./test_files/test_fasta_file.bed)

>chr22:50794870-50797870
ccgccccacactgacgcagt...ccgcctcagcctcctaaa
>chr22:21554591-21557591
cttggggagagcagaagcca...gtgcagtggtgcaatctt
>chr22:50304644-50307644
CTGAGCACCCCCCACCAGCCA...GGAGACGGGGCCTTTGT
...

Usage with TFEA

TFEA --output ./TFEA/test/test_files/test_output \
--fasta_file ./TFEA/test/test_files/test_fasta_file.fa \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme

Secondary Analysis

TFEA can also perform MD-Score analysis and differential MD-Score analysis. This can be switched on easily if running the full pipeline:

TFEA --output ./TFEA/test/test_files/test_output \
--combined_file ./TFEA/test/test_files/test_combined_file.bed \
--bam1 ./TFEA/test/test_files/SRR1105736.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105737.sorted.chr22.subsample.bam \
--bam2 ./TFEA/test/test_files/SRR1105738.sorted.chr22.subsample.bam ./TFEA/test/test_files/SRR1105739.sorted.chr22.subsample.bam \
--label1 condition1 --label2 condition2 \
--genomefasta ./TFEA/test/test_files/chr22.fa \
--fimo_motifs ./TFEA/test/test_files/test_database.meme \
--md --mdd

These secondary analyses can also take pre-processed input similar to TFEA. See the 'Secondary Analysis Inputs' section in the help message for more information.

Measuring TF FPKM

TFEA will also measure the FPKM of TF genes within your data if desired. This requires input into the `--motif_annotations` flag which is a bed file with motif names as the 4th column. Example:

chr1	3698045	3733079	P73_HUMAN.H11MO.0.A	0	+
chr1	6579990	6589212	ZBT48_HUMAN.H11MO.0.C	0	+
chr1	15941868	15948495	ZBT17_HUMAN.H11MO.0.A	0	-
chr1	23359447	23368005	ZN436_HUMAN.H11MO.0.C	0	-

This special bed file can be generated from a .meme database file using a tab-separated 2-column file containing motif names to gene names and a gene annotation file:

Example of a motif_to_gene.tsv (this was generated on the HOCOMOCO v11 website):

Model	Transcription factor
ANDR_HUMAN.H11MO.0.A	AR
AP2A_HUMAN.H11MO.0.A	TFAP2A
AP2C_HUMAN.H11MO.0.A	TFAP2C
ASCL1_HUMAN.H11MO.0.A	ASCL1

Example of gene annotations:

chr1	11873	14409	DDX11L1;NR_046018;chr1:11873-14409	0	+
chr1	14361	29370	WASH7P;NR_024540;chr1:14361-29370	0	-
chr1	17368	17436	MIR6859-1;NR_106918;chr1:17368-17436	0	-
chr1	17368	17436	MIR6859-4;NR_128720;chr1:17368-17436	0	-
chr1	17368	17436	MIR6859-3;NR_107063;chr1:17368-17436	0	-

The script works by looking for gene names that correspond to each motif within the 4th column of the gene annotation file. It expects the 4th column to be ';' delimited.

Already generated motif_annotation.bed files (and also intermediate .tsv files) are located within './motif_files/'

Generating Simulated Data

TFEA has a subpackage that is capable of generating simulated data for testing. If you have installed TFEA, it can be invoked with:

TFEA-simulate --help

The purpose of this subpackage is to embed motif instances into fasta sequences that can be generated randomly or be from an experimental dataset (e.g. untreated control sample). There are several key flags that control this process (each of these may be a comma-delimited list of values that would indicate multiple instances of motif adding):

--distance_mu : This flag controls where the center of the distribution is located (note: only normal distributions are supported at this point)

--distance_sigma : Controls the standard deviation of the normal distribution

--rank_range : Controls the range of sequences in which to add a motif

--motif_number : Controls the number of motifs to add to your range of sequences

Rerunning TFEA

TFEA can be easily rerun given one or multiple TFEA output folders. This works simply by rerunning the rerun.sh script which contains all command-line flag inputs. TFEA also automatically creates a copy of your configuration file (if used) within the output folder which is then also used when rerunning (so no need to worry about editing the original configuration file). To rerun a single TFEA output folder:

TFEA --rerun ./TFEA/test/test_files/test_output

The --rerun flag also supports patterns containing wildcards to rerun all TFEA output folders that match. For example:

TFEA --rerun ./TFEA/test/test_files/test*

This works by looking recursively into all folders and subfolders for rerun.sh scripts and then executing sh rerun.sh, so use with caution!

Help Message

Below are all the possible flags that can be provided to TFEA with a short description and default values.

usage: TFEA [-h] [--output DIR] [--bed1 [BED1 [BED1 ...]]]
            [--bed2 [BED2 [BED2 ...]]] [--bam1 [BAM1 [BAM1 ...]]]
            [--bam2 [BAM2 [BAM2 ...]]] [--bg1 [BG1 [BG1 ...]]]
            [--bg2 [BG2 [BG2 ...]]] [--label1 LABEL1] [--label2 LABEL2]
            [--genomefasta GENOMEFASTA] [--fimo_motifs FIMO_MOTIFS]
            [--config CONFIG] [--sbatch SBATCH] [--test-install] [--test-full]
            [--combined_file COMBINED_FILE] [--ranked_file RANKED_FILE]
            [--fasta_file FASTA_FILE] [--md] [--mdd]
            [--md_bedfile1 MD_BEDFILE1] [--md_bedfile2 MD_BEDFILE2]
            [--mdd_bedfile1 MDD_BEDFILE1] [--mdd_bedfile2 MDD_BEDFILE2]
            [--md_fasta1 MD_FASTA1] [--md_fasta2 MD_FASTA2]
            [--mdd_fasta1 MDD_FASTA1] [--mdd_fasta2 MDD_FASTA2]
            [--mdd_pval MDD_PVAL] [--mdd_percent MDD_PERCENT]
            [--combine {mumerge,intersect/merge,mergeall,tfitclean,tfitremovesmall}]
            [--rank {deseq,fc,False}] [--scanner {fimo,genome hits}]
            [--enrichment {auc,auc_bgcorrect}] [--fimo_thresh FIMO_THRESH]
            [--fimo_background FIMO_BACKGROUND] [--genomehits GENOMEHITS]
            [--singlemotif SINGLEMOTIF] [--permutations PERMUTATIONS]
            [--largewindow LARGEWINDOW] [--smallwindow SMALLWINDOW]
            [--padjcutoff PADJCUTOFF] [--plot_format {png,svg,pdf}]
            [--dpi DPI] [--plotall] [--metaprofile] [--output_type {txt,html}]
            [--batch BATCH] [--cpus CPUS] [--mem MEM]
            [--motif_annotations MOTIF_ANNOTATIONS] [--bootstrap BOOTSTRAP]
            [--basemean_cut BASEMEAN_CUT] [--rerun [RERUN [RERUN ...]]]
            [--gc GC] [--venv VENV] [--debug]

Transcription Factor Enrichment Analysis (TFEA) v1.1.3

optional arguments:
  -h, --help            show this help message and exit

Main Inputs:
  Inputs required for full pipeline

  --output DIR, -o DIR  Full path to output directory. If it exists, overwrite
                        its contents.
  --bed1 [BED1 [BED1 ...]]
                        Bed files associated with condition 1
  --bed2 [BED2 [BED2 ...]]
                        Bed files associated with condition 2
  --bam1 [BAM1 [BAM1 ...]]
                        Sorted bam files associated with condition 1. Must be
                        indexed.
  --bam2 [BAM2 [BAM2 ...]]
                        Sorted bam files associated with condition 2. Must be
                        indexed.
  --bg1 [BG1 [BG1 ...]]
                        Sorted bedGraph files associated with condition 1.
                        Must be indexed.
  --bg2 [BG2 [BG2 ...]]
                        Sorted bedGraph files associated with condition 2.
                        Must be indexed.
  --label1 LABEL1       An informative label for condition 1
  --label2 LABEL2       An informative label for condition 2
  --genomefasta GENOMEFASTA
                        Genomic fasta file
  --fimo_motifs FIMO_MOTIFS
                        Full path to a .meme formatted motif databse file.
                        Some databases included in motif_files folder.
  --config CONFIG, -c CONFIG
                        A configuration file that a user may use instead of
                        specifying flags. Command line flags will overwrite
                        options within the config file. See examples in the
                        config_files folder.
  --sbatch SBATCH, -s SBATCH
                        Submits an sbatch (slurm) job. If specified, input an
                        e-mail address.
  --test-install, -ti   Checks whether all requirements are installed and
                        command-line runnable.
  --test-full, -t       Performs unit testing on full TFEA pipeline.

Processed Inputs:
  Input options for pre-processed data

  --combined_file COMBINED_FILE
                        A single bed file combining regions of interest.
  --ranked_file RANKED_FILE
                        A bed file containing each regions rank as the 4th
                        column.
  --fasta_file FASTA_FILE
                        A fasta file containing sequences to be analyzed,
                        ranked by the user.

Secondary Analysis Inputs:
  Input options for performing MD-Score and Differential MD-Score analysis

  --md                  Switch that controls whether to perform MD analysis.
  --mdd                 Switch that controls whether to perform differential
                        MD analysis.
  --md_bedfile1 MD_BEDFILE1
                        A bed file for MD-Score analysis associated with
                        condition 1.
  --md_bedfile2 MD_BEDFILE2
                        A bed file for MD-Score analysis associated with
                        condition 2.
  --mdd_bedfile1 MDD_BEDFILE1
                        A bed file for Differential MD-Score analysis
                        associated with condition 1.
  --mdd_bedfile2 MDD_BEDFILE2
                        A bed file for Differential MD-Score analysis
                        associated with condition 2.
  --md_fasta1 MD_FASTA1
                        A fasta file for MD-Score analysis associated with
                        condition 1.
  --md_fasta2 MD_FASTA2
                        A fasta file for MD-Score analysis associated with
                        condition 2.
  --mdd_fasta1 MDD_FASTA1
                        A fasta file for Differential MD-Score analysis
                        associated with condition 1.
  --mdd_fasta2 MDD_FASTA2
                        A fasta file for Differential MD-Score analysis
                        associated with condition 2.
  --mdd_pval MDD_PVAL   P-value cutoff for retaining differential regions.
                        Default: 0.2
  --mdd_percent MDD_PERCENT
                        Percentage cutoff for retaining differential regions.
                        Default: False

Modules:
  Options for different modules

  --combine {mumerge,intersect/merge,mergeall,tfitclean,tfitremovesmall}
                        Method for combining input bed files. Default: mumerge
  --rank {deseq,fc,False}
                        Method for ranking combined bed file
  --scanner {fimo,genome hits}
                        Method for scanning fasta files for motifs. Default:
                        fimo
  --enrichment {auc,auc_bgcorrect}
                        Method for calculating enrichment. Default: auc

Scanner Options:
  Options for performing motif scanning

  --fimo_thresh FIMO_THRESH
                        P-value threshold for calling FIMO motif hits.
                        Default: 1e-6
  --fimo_background FIMO_BACKGROUND
                        Options for choosing mononucleotide background
                        distribution to use with FIMO. Default:
                        largewindow{'largewindow', 'smallwindow', int, file}
  --genomehits GENOMEHITS
                        A folder containing bed files with pre-calculated
                        motif hits to a genome. For use with 'genome hits'
                        scanner option.
  --singlemotif SINGLEMOTIF
                        Option to run analysis on a subset of motifs within
                        specified motif database or genome hits. Can be a
                        single motif or a comma-separated list of motifs.

Enrichment Options:
  Options for performing enrichment analysis

  --permutations PERMUTATIONS
                        Number of permutations to perfrom for calculating
                        p-value. Default: 1000
  --largewindow LARGEWINDOW
                        The size (bp) of a large window around input regions
                        that captures background. Default: 1500
  --smallwindow SMALLWINDOW
                        The size (bp) of a small window arount input regions
                        that captures signal. Default: 150

Output Options:
  Options for the output.

  --padjcutoff PADJCUTOFF
                        A p-adjusted cutoff value that determines some
                        plotting output.
  --plot_format {png,svg,pdf}
                        Format of saved figures. Default: png
  --dpi DPI             Resolution of saved figures. Applies to png. Default:
                        100
  --plotall             Plot graphs for all motifs.Warning: This will make
                        TFEA run much slower andwill result in a large output
                        folder.
  --metaprofile         Create meta profile plots per quartile. Warning: This
                        will make TFEA run much slower and consume a lot more
                        memory.
  --output_type {txt,html}
                        Specify output type. Selecting html will increase
                        processing time and memory usage. Default: txt

Miscellaneous Options:
  Other options.

  --batch BATCH         Comma-separated list of batches to assign to bam files
                        in order of bam1 files then bam2 files. For use only
                        when ranking with DE-Seq.
  --cpus CPUS           Number of processes to run in parallel. Warning:
                        Increasing this value will significantly increase
                        memory footprint. Default: 1
  --mem MEM             Amount of memory to request for sbatch script.
                        Default: 20gb
  --motif_annotations MOTIF_ANNOTATIONS
                        A bed file specifying genomic coordinates for genes
                        corresponding to motifs. Motif name must be in the 4th
                        column and match what is in the database.
  --bootstrap BOOTSTRAP
                        Amount to subsample motifhits to. Set to False to turn
                        off. Default: False
  --basemean_cut BASEMEAN_CUT
                        Basemean cutoff value for inputted regions. Default: 0
  --rerun [RERUN [RERUN ...]]
                        Rerun TFEA in all folders of aspecified directory.
                        Used as a standalone flag.Default: False
  --gc GC               Perform GC-correction. Default: True
  --venv VENV           Full path to virtual environment.
  --debug               Print memory and CPU usage to stderr. Also retain
                        temporary files.

Example Output

TFEA will output all files and folders into the directory specified by the `--output` flag. The output directory structure is as follows:

./TFEA/test/test_output
│   rerun.sh
│   test_config.ini
│   inputs.txt
│   results.txt
│   md_results.txt
│   mdd_results.txt
│   results.html
│
└───e_and_o
│      TFEA_test_output.err
│      TFEA_test_output.out
│  
└───plots
│      logo_rcMOTIF.eps
│      logo_rcMOTIF.png
│      logoMOTIF.eps
│      logoMOTIF.png
│      MOTIF_enrichment_plot.png
│      MOTIF_simulation_plot.png
│      MOTIF.results.html
│      
└───temp_files
       combined_file.mergeall.bed
       count_file.bed
       count_file.header.bed
       DESeq.R
       DESeq.Rout
       DESeq.res.txt
       markov_background.txt
       ranked_file.bed
       ranked_file.fa

A brief description of the files contained within this output directory are below:

rerun.sh

This bash script can be used at any time to regenerate a TFEA output folder in its entirety, run it using:

sh ./TFEA/test/test_output/rerun.sh

test_config.ini

TFEA copies the config file you are using into the output directoy. This file is then referenced by rerun.sh.

inputs.txt

A .txt file that contains all user-provided inputs into TFEA

results.txt

Contains TFEA results tab-delimited in .txt format. For example:

#TF     E-Score Corrected E-Score       Events  GC      FPKM    P-adj   Corrected P-adj
ZN121_HUMAN.H11MO.0.C   0.03850103634523616     0.10374224445904234     230     0.5825102880658438      nan     0.637   1e-1
SP2_HUMAN.H11MO.0.A     -0.14690991996820513    -0.09232220913971645    119     0.7937748120168564      nan     1e0     0.494

Column Descriptions:

1. TF - The name of the motif analyzed as it appears in the .meme database or filename within the genome hits directory without the '.bed' extension.
2. E-Score - The enrichment score of the given motif. Ranges from -1 to 1.
3. Corrected E-Score - The GC-corrected E-Score. Calculated by fitting a linear regression to the E-Score vs. motif GC content of results and correcting to obtain a flat line.
4. Events - Number of motif hits within the analyzed regions of interest.
5. GC - GC content of the analyzed motif
6. FPKM - FPKM of the gene associated with the analyzed motif, see --motif_annotations flag.
7. P-adj - The adjusted p-value of the motif using the Bonferroni correction (to get the original p-value, simply divide this by the total number of motifs analyzed).
8. Corrected P-adj - The adjusted p-value after GC-correction.

md_results.txt and mdd_results.txt

Contains tab-delimited results for secondary MD-Score (MDS) and Differential MD-Score (MDD) analysis if these flags were specified

results.html

The main results html (if --output_type 'html' specified). For example:

Figure 1: Main Results Page. An example main results page (i.e. results.html). (a) TFEA GC-Plot. A scatter plot showing the raw calculated E-Score as a function of GC-content. A linear regression is fit (red line) to these data to determine if there is a GC-bias. E-Scores are then corrected to flatten the line by subtracting the y-offset from each motif to yield the corrected E-Score. TFs are also colored on the subsequent correction to be applied. (b) TFEA MA-Plot. A scatter plot showing E-Score vs. Log10(Events). Analogous to an MA-Plot produced from DE-Seq, these are a good way to judge believable motifs. The further you go to the right, the more confidence you have in smaller absolute E-Score values. (c) DE-Seq MA-Plot. A scatter plot showing all input ROIs as an MA-Plot showing fold change in reads vs. average reads across conditions. Colored based on the subsequent rank of each ROI. (d) Links to supplementary infromation, secondary analyses performed, and runtime information. (e) Lists of all motifs analyzed separated on positive and negative E-Scores. Significant motifs (or any if --plotall is specified) may be clicked to bring up individual motif plots

MOTIF.results.html

Each signficant TF motif (or all motifs if --plotall specified) will produce its own MOTIF.results.html file contained within the plots/ directory in the specified output directory. All images are also self-contained within the plots/ folder. For example:

Figure 2: Individual Motif Results Page. An example individual motif results page. (a) The numerical results for this specific motif. (b) A representation of the running sum statistic which increases from 0 as it travels right based on the distance of an observed motif to the center of each ROI. (c) Representation of the amount added to the running sum at each given location. Similar to GSEA enrichment plots. (d) Scatter plot showing the raw motif hits as a function of distance to ROI center (y-axis) and rank (x-axis). (e) Representation of the ranking metric used to rank ROI. Specifically this is the -log10 of the DE-Seq p-value with an added sign (+/-) based on whether the ROI fold change was positive or negative. (f) Meta plots of all regions that contain a motif hit separated by quartiles (n=number of ROI that go into the plot). (g) Heatmaps that represent motif hit distribution across the n ROI, separated again by quartiles. (h) Forward and reverse motif logos. (i) Simulation plot showing the background simulated distribution in blue, the observed non-corrected E-Score in red, and the GC-corrected E-Score in green.

Contact Information

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