-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.Rmd
96 lines (65 loc) · 2.16 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# express
<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/express)](https://CRAN.R-project.org/package=express)
<!-- badges: end -->
Warning: this repo is in very early development and not yet ready for use yet.
The goal of express is to simplify exploration of tabular, gene-level expression datasets.
To analyse your own data, you need a data.frame with 3 columns
1. *sample*: a sample identifier
2. *gene*: a gene identifier
3. *expression*: a quantitative value representing expression of that has been normalised so is comparable across samples (e.g. TPM).
## Installation
You can install the development version of express like so:
``` r
# install.packages('remotes')
remotes::install_github('selkamand/express')
```
## Quick Start
```{r, eval=FALSE}
library(express)
path_example <- system.file("example.tsv" ,package = "express")
df_expression <- read_expression_table(path_example)
express_gene_distribution(
data = df_expression,
genes = "TP53",
sample_metadata = df_sample_metadata,
colour_by = "Disease"
)
express_pathway_distribution(
data = df_expression,
pathways = list("pathway_name" = c("gene1", "gene2", "gene3"))
sample_metadata = df_sample_metadata,
colour_by = "Disease"
)
express_sample_relatedness(
data = df_expression,
method = "TSNE",
gene_selector = "most_variable",
sample_metadata = df_sample_metadata,
colour_by = "Disease"
)
```
## Accessing Precomputed Public Analyses
express can also visualise the results from precomputed analyses hosted on GitHub.
For example to see a t-SNE of TCGA BRCA
```{r, fig.height=5, fig.width=7}
library(express)
express_precomputed("BRCA", datatype = "expression")
```
To see all precomputed datasets available
```{r}
express_available_datasets()
```