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title: "TRENA: computational prediction of gene regulation"
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# Introduction

The regulation of target genes by their transcription factors is complex and incompletely
understood.  Multiple signals involving core promoters, distal enhancers, epigenetic controls
on chromatin accessibility, stochastic and cooperative binding on different times scales are
all involved.

Many hundreds of human TF/target gene relationships *are* known, however.  These relationships
are primarily from  well-studied systems and from research into human diseases.  Hundreds of
thousands of TF/target gene relationships exist in human cells.  There is an urgent need
for laboratory and computational methods which predict and validate the many that remain unknown.

*trena* is one such computational method.  It's predictive success depends upon the judicious
combination of two predictors, each of which in isolation has little predictive value:

   1) correlated gene expression of TF and target gene
   2) actual or predicted DNA binding of the TF in regulatory DNA regions associated with
      the target gene

We will show that these predictors, when applied in tandem to high quality gene expression, genomic
and epigenomic data, can 1) recapitulate known TF/target gene relationships, and 2) predict new ones
sufficiently plausible to warrant small-scale laboratory valdidation, and 3) specify useful
genome-scale tissue-specific regulatory networks.  

# Methodological Overview

There are two stages in every trena analysis:

  1. Select a relevant set of transcription factors
  2. Score the gene expression association of each TF with the target gene using an ensemble of
     the independent feature selectors trena provides: lasso, ridge, xgboost, randomForest
     pearson & spearman correlation.

### Step One: select candidate transcription factors

Step one can be accomplished in many ways.  A naive approach uses all 1663 human genes currently
annotated by the GeneOntology project to the molecular function, "DNA-binding transcription factor activity".
A more conservative choice is to use the 558 human genes for which Jaspar2018 and HOCOMOCO provide
binding motifs.  We will demonstrate these two annotation-based strategies below and then proceed
to use more discriminating approaches:

  1.  Assume a traditional cis-regulatory promoter region for the target gene (say, +/-
      5kb from the TSS).
  2.  Obtain cis-regulatory promoters and enhancers from public databaes or - best of all -
      by cell type specific experimental data for the system under study
  3.  Select transcription factors with motifs which match DNA sequence in these regulatory regions.
      Each match can be quantified in (at least) two ways:  by motif/sequence match, and by
      the evolutionary conservation of that matching sequence.

Each of these strategies has both strengths weaknesses.  The naive 1663 GO annotation gene strategy
will identify TFs (or co-factors) which may be active and functional but for which a binding motif
is not neccesarily known.  The 558 motif TF strategy similarly makes no assumptions about regulatory regions, but
probably is enriched for better studied TFs. In the absence of good data about the target genes's
actual regulatory regions, and binding, either of these inclusive approaches can be useful.

However, there is an emerging consensus (Cusanovich 2014 & etc) that a great deal of actually bound
TFs, as detected by ChIP-seq, in known regulatory regions, are not functional.  Their
knockdown or silencing has no effect on the expression of the target gene.  Thus the primary challenge
for the first stage of trena - TF selection - is how to avoid considering too many extraneous TF candidates.

We adopt these strategies for TF selection:

  1) select only TFs with motifs with very high DNA matching scores
  2) occuring in non-coding DNA sequence that is highly conserved over 100 million years
  3) when avaiable, as with the increasingly available single-cell ATAC-seq, further
     filter the TFs by requring that condition-specific chromatin is open

The stringency of strategies 1 and 2 will, in any given study, exclude functional and relevant
binding sites, and their cognate TFs from consideration in the trena regulatory model.  This loss is
more than offset, as we will show, by the credibility of the models which emerge from that
stringency, based on the assumption that (again, following Cusanovich 2014) that since each human
gene has a median TF count of 35, that high fidelity motif match in highly conserved regions of open
chromatin identifies at least some TFs of interest - and, we speculate, that these will often include the
oldest and most central TFs for each target gene.

### Step Two: Run the ensemble of feature selectors

After appropriate normalization, the expression of the TFs selected in step one are used to predict
the expression of the target gene, using a variety of algorithms:

   - lasso
   - lassoPValue
   - ridge regression
   - pearson correlation
   - spearman correlation
   - random forest
   - xgboost

The resulting "trena model" is an R data.frame containing the score using each algorithm of each TF
as a predictor of the gene expression of the target gene.   We as yet have no single settled heuristic method
to aggregate these scores.   Individucal analysts evolve their own informal aggregation scheme. For
example:  giving weight and preference to the random forest score while also heeding spearman correlation.
Quite commonly, as will be seen in the case studies, the various methods produce compatible predictions.

The first criterion of value for a computational tool such as trena is its ability to reliably
reproduce existing knowledge.  Here we accomplish that with four case studies, first by demonstrating failure
with bulk GTEx blood RNA-seq, then success with three applications of stage-specific erythropoiesis
RNA-seq and ATAC-seq data.

### Case Studies

- [Case Study 1](caseStudyOne.html) An informative failure: trena with GTEx bulk blood expression
- [Case Study 2](caseStudyTwo.html)  Successful recapitulation:  trena + Corces et al, 2016, RNA-seq on FACS-separated erythropoiesis stages, GSE74246
- [Case Study 3](caseStudyThree.html) Successful recapituation: more recent Brand lab (similar to Corces et al), GSE118537
- [Case Study 4](caseStudyFour.html)  Discover a novel regulator of GATA2?: trena + Brand lab + scATAC-seq

<!--
This image summarizes the NFE2 genome neighborhood and binding sites for putative regulators identified
by trena:

  1. phast7: conserved DNA sequence, in red, on a scale of 0-1, across 7 species: opossum, dog, rat, mouse, rhesus
     monkey, chimpanzee and human.  Highly conserved sequence in non-coding regions is likely to be functional,
     that is, to play a role in regulating a nearby gene, and possibly a TF binding site.

  2. atac combined: the union of 12 scATAC-seq datasets, collected in an erythropoiesis time course
     Gillespie et al, https://www.biorxiv.org/content/10.1101/812123v1

  3. TAL1, KLF1, GATA1,  etc:  high-scoring motif matches for top regulating TFs predicted by trena
     using RNA-seq expression data from Gillespie et al.

Here is the high-res model as scored by trena, followed by the genome view.


```{r load brand model, echo=FALSE}
tbl.model <- get(load("brand.tbl.model.trimmed"))
knitr::kable(tbl.model)
```


```{r, eval=TRUE, echo=FALSE}
knitr::include_graphics("igvR-nfe2-regulation-noGeneHancer.png")
```


## Correlated gene expression of all known TFs to the target gene

We do this with three (four) sources of gene expression data

 1.  GTEx Whole Blood

### GTEx Blood


### Corces et al; Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.
in 13 human proimary blood cell types that span the hamatopoietic hierarchy"

see al
    https://www.ncbi.nlm.nih.gov/pubmed/27526324

see also: Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation
Jason D.Buenrostro et al

### Brand erythropoiesis time-course

## Further restrict TFs to those which fall in highly conserved (phast7 >= 0.5) regions

##  as above, but with RNA collected from multiple stages of erythropoiesis
  5. use RNA-seq and scATAC-seq from Gillespie et al.  The ATAC-seq track is the union of
    for 20 samples across 12 erythropoiesis development timepoints.


# Journal Articles Reporting Regulators of NFE2  

  - Bose, Francesca, et al. "Functional interaction of CP2 with GATA-1 in the regulation of erythroid promoters."
    Molecular and cellular biology 26.10 (2006): 3942-3954.

  - Ding, Ya‐li, et al. "Over‐expression of EDAG in the myeloid cell line 32D: Induction of GATA‐1 expression
    and erythroid/megakaryocytic phenotype." Journal of cellular biochemistry 110.4 (2010): 866-874.

  - many more to be added...




# Session Info

```{r sessionInfo}
sessionInfo()
````


![knitr logo](http://yihui.name/knitr/images/knit-logo.png)
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