sc_DTU_analysis {FLAMES} | R Documentation |
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
.
sc_DTU_analysis(sce, path, min_count = 15)
sce |
The |
path |
The path to the output folder of
|
min_count |
The minimum UMI count threshold for filtering isoforms. |
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.
a data.frame
containing the following columns:
gene_name - differentially transcribed genes
X_value - the X value for the DTU gene
df - degrees of freedom of the approximate chi-squared distribution of the test statistic
DTU_tr - the transcript_id with the highest squared residuals
DTU_group - the cell group with the highest squared residuals
p_value - the p-value for the test
adj_p - the adjusted p-value (by Benjamini–Hochberg correction)
The table is sorted by decreasing P-values. It will also be saved as sc_DTU_analysis.csv
under the
output folder.