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CI

Signac - A versatile package for Single-cell RNA-Seq analysis

We introduce Signac, a versatile R package to facilitate the analysis workflow for single-cell data. It helps to find marker genes faster and more accurate, search for cells with similar expression profiles, integrate multiple datasets in the BioTuring Browser database (know more about BioTuring Browser), etc. For users with a limited computational resource, we provide the helper functions to exercise all analyses for the large-scale datasets from disk. Because of its speed and flexibility, it can be adapted to any existing R analysis pipeline to help explore single-cell data more efficient.

This package can also be used as the reference for the computational methods used in BioTuring Browser software.

24/06/2019

Please visit this blog to read about Venice, a fast and accurate method for finding marker genes, which is incorporated into Signac. Venice function can be accessed via Signac::VeniceMarker

You can also read the manuscript about Venice here.

Below is the benchmark when finding 4 types of DE genes in a simulated dataset (using scDD R package). Total 15 methods included Venice are tested. The dataset has 2000 DE genes that are even divided into 4 groups (DE, DP, DB, DM), and 18000 non-DE genes (EP and EE).

Installation

devtools::install_github("bioturing/signac")

Usage

Find marker genes with Signac::VeniceMarker

### The pbmc object created by following the tutorial: https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html
> pbmc
An object of class Seurat
13714 features across 2638 samples within 1 assay
Active assay: RNA (13714 features, 2000 variable features)
 1 dimensional reduction calculated: pca
> head(Seurat::Idents(pbmc))
AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1
               1                3                1                2
AAACCGTGTATGCG-1 AAACGCACTGGTAC-1
               6                1
Levels: 0 1 2 3 4 5 6 7 8

> ### Find markers for NK cells (cluster 6)
> VeniceMarker(pbmc@assays$RNA@counts, cluster=Idents(pbmc) == 6) %>% head
  Gene.ID Gene.Name Dissimilarity Bin.count Log10.p.value Perm.p.value
1    1804      GNLY     0.8129016         3    -102.95623          NaN
2   13002      NKG7     0.8186809         4    -102.32274          NaN
3    9294      GZMB     0.8004017         3    -101.14819          NaN
4    7153      PRF1     0.7359742         3     -93.08173          NaN
5   11880      CST7     0.6679871         3     -84.52626          NaN
6    3202    FGFBP2     0.6670391         3     -84.37497          NaN
  Log10.adjusted.p.value Up.Down.score Log2.fold.change      pct1      pct2
1              -98.81906             1         4.016472  96.12903 13.129279
2              -98.48661             1         3.424045 100.00000 25.493355
3              -97.48815             1         3.174406  96.12903  6.806283
4              -89.54662             1         2.502070  94.83871 10.672573
5              -81.08807             1         2.038486  94.83871 14.941603
6              -81.01596             1         2.362991  87.74194  6.202175