sc_DTU_analysis {FLAMES}R Documentation

FLAMES Differential Transcript Usage Analysis

Description

Chi-square based differential transcription usage analysis. This variant is meant for single cell data. Takes the SingleCellExperiment object from sc_long_pipeline as input. Alternatively, the path to the output folder could be provided instead of the SCE object. A cluster annotation file cluster_annotation.csv is required, please provide this file under the output folder of sc_long_pipeline.

Usage

sc_DTU_analysis(sce, path, min_count = 15)

Arguments

sce

The SingleCellExperiment object from sc_long_pipeline, an additional cluster_annotation.csv file is required under the output folder of the SCE object.

path

The path to the output folder of sc_long_pipeline the folder needs to contain:

  • transcript_count.csv.gz - the transcript count matrix

  • isoform_FSM_annotation.csv - the full splice match annotation file

  • cluster_annotation.csv - cluster annotation file

min_count

The minimum UMI count threshold for filtering isoforms.

Details

This function will search for genes that have at least two isoforms, each with more than min_count UMI counts. For each gene, the per cell transcript counts were merged by group to generate pseudo bulk samples. Grouping is specified by the cluster_annotation.csv file. The top 2 highly expressed transcripts for each group were selected and a UMI count matrix where the rows are selected transcripts and columns are groups was used as input to a chi-square test of independence (chisq.test). Adjusted P-values were calculated by Benjamini–Hochberg correction.

Value

a data.frame containing the following columns:

The table is sorted by decreasing P-values. It will also be saved as sc_DTU_analysis.csv under the output folder.


[Package FLAMES version 1.0.2 Index]