README.md

In vjcitn/msigTextM: text mining for msigdb sets construed as documents

title: "Text-mining utilities for molecular signature catalogues with Bioconductor" author: "Vincent J. Carey, stvjc at channing.harvard.edu" date: "r format(Sys.time(), '%B %d, %Y')" vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{MSigDb text mining -- elementary concerns} %\VignetteEncoding{UTF-8} output: BiocStyle::html_document: highlight: pygments number_sections: yes theme: united toc: yes abstract: > The purpose of this package is to mobilize text mining technologies to help users interrogate gene set information in signature catalogues (such as MSigDb) through verbalization of relevant scientific concepts.

```{r setup,echo=FALSE,results="hide"} library(ssrch) library(msigTextM) library(DT)

# Introduction

[MSigDb](http://software.broadinstitute.org/gsea/msigdb/index.jsp)
is a curated collection of gene sets, with
enumerations of gene symbols or ENTREZ identifiers
in sets that occupy higher level categories.

The main categories (and subcategories, indented) are

- H: hallmark
- C1: positional
- C2: curated 
    - CGP: chemical and genetic perturbations
    - CP: canonical pathways (also subclassed by source)
- C3: motif
    - MIR: microRNA target sets
    - TFT: transcription factor target sets
- C4: computational
    - CGN: cancer gene neighborhoods
    - CM: cancer modules
- C5: Gene Ontology
    - BP: (biological process) 
    - MF: (molecular function)
    - CC: (cellular component)
- C6: oncogenic
- C7: immunologic

Here we tabulate the counts of gene sets by category
and sub-category.  

         main category

subcategory ARCHIVED C1 C2 C3 C4 C5 C6 C7 H 0 326 0 0 0 0 189 4872 50 BP 0 0 0 0 0 4436 0 0 0 C5_BP 527 0 0 0 0 0 0 0 0 C5_CC 159 0 0 0 0 0 0 0 0 C5_MF 178 0 0 0 0 0 0 0 0 CC 0 0 0 0 0 580 0 0 0 CGN 0 0 0 0 427 0 0 0 0 CGP 0 0 3433 0 0 0 0 0 0 CM 0 0 0 0 431 0 0 0 0 CP 0 0 252 0 0 0 0 0 0 CP:BIOCARTA 0 0 217 0 0 0 0 0 0 CP:KEGG 0 0 186 0 0 0 0 0 0 CP:REACTOME 0 0 674 0 0 0 0 0 0 MF 0 0 0 0 0 901 0 0 0 MIR 0 0 0 221 0 0 0 0 0 TFT 0 0 0 615 0 0 0 0 0

The _vocabularies_ of the gene set collections are important for
many aspects of utilization.  
`STANDARD_NAME` attributes for
C7 gene sets look like

[1] "KAECH_NAIVE_VS_DAY8_EFF_CD8_TCELL_UP" [2] "KAECH_NAIVE_VS_DAY8_EFF_CD8_TCELL_DN" [3] "KAECH_NAIVE_VS_DAY15_EFF_CD8_TCELL_UP" [4] "KAECH_NAIVE_VS_DAY15_EFF_CD8_TCELL_DN" [5] "KAECH_NAIVE_VS_MEMORY_CD8_TCELL_UP" [6] "KAECH_NAIVE_VS_MEMORY_CD8_TCELL_DN" [7] "KAECH_DAY8_EFF_VS_DAY15_EFF_CD8_TCELL_UP" [8] "KAECH_DAY8_EFF_VS_DAY15_EFF_CD8_TCELL_DN"

This indicates that authorship, time
(or other experimental design factors), cell type, and
regulatory association may be encoded in standard gene
set names.  The authorship information can be retrieved
systematically from the `PMID` attribute, and its
role in the set name can probably
be ignored.

Can we parse the set names, using the fact that
underscore separates the key descriptive tokens?
Perhaps, but examples like
`GSE19888_ADENOSINE_A3R_INH_PRETREAT_AND_ACT_
  BY_A3R_VS_A3R_INH_AND_TCELL_
  MEMBRANES_ACT_MAST_CELL_UP`
are not encouraging.

The `DESCRIPTION_BRIEF` field is more human-readable.  Here
are examples associated with the first four standard
names listed above.

[1] "Genes up-regulated in naïve CD8 T cells compared to effector CD8 T cells at the peak expansion phase (day 8 after LCMV-Armstrong infection)." [2] "Genes down-regulated in naïve CD8 T cells compared to effector CD8 T cells at the peak expansion phase (day 8 after LCMV-Armstrong infection)." [3] "Genes up-regulated in naïve CD8 T cells compared to effector CD8 T cells at contraction phase (day 15 after LCMV-Armstrong infection)." [4] "Genes down-regulated in naïve CD8 T cells compared to effector CD8 T cells at contraction phase (day 15 after LCMV-Armstrong infection)."

Some readers may find it a little challenging to extract the
key distinctions among these sets -- up and down regulation
are distinguished, but so are phases (peak expansion vs contraction),
cell types, and times after LCMV-Armstrong infection.

The purpose of this package is to mobilize text mining technologies
to help users interrogate gene set information
in MSigDb through verbalization of relevant scientific concepts.

# Illustration with the C7 (immunologic) gene set collection

We have created a `DocSet` instance for 300 gene sets from
the immunologic set collection.

```{r lkc7}
immu300

This is essentially a collection of environments mapping from gene set names and descriptions to genes and back. We have a searchDocs method to query the DocSet.

```{r lkc7b} dsl = searchDocs("down.*systemic lupus", immu300) datatable(dsl)

It is possible to search for occurrence of a gene in the
gene sets catalogued in the DocSet instance.

```{r lkc7c}
brocc = searchDocs("BRCA2", immu300)
brocc

TO DO

The abstracts (DESCRIPTION_FULL fields) of the sets need to be introduced. This would entail enhancement to the ssrch package.

Need to do the full c7 and see how large the resulting DocSet is.

Do other classes.

vjcitn/msigTextM documentation built on May 13, 2019, 12:33 a.m.