Yipeng Gao, Wei Li 2020-08-22
2020-08-22:
- Version 0.0.1 is released!
scDaPars is a bioinformatics algorithm to accurately quantify Alternative Polyadenylation (APA) events at both single-cell and single-gene resolution using standard scRNA-seq data.
Step.1 scDaPars first takes scRNA-seq genome coverage data (bedgraph format) as input and forms a linear regression model to jointly infer the exact location of proximal poly(A) sites (Current Version of scDaPars do not support this function, raw PDUI values are calulated seperately using DaPars2). DaPars2 can be downloaded and the instructions to run DaPars2 can be found in DaPars2 Instruction.
Step.2 scDaPars constructs a nearest neighbor graph based on the sparse APA matrix generated in step.1 to identify a pool of candidate neighboring cells that have similar APA profiles.
Step.3 scDaPars uses a non-negative least square (NNLS) regression model to refine neighboring cells and impute PDUIs of dropout genes in each cell.
We welcome any suggestions on the package. For technical problems, please report to Issues. For suggestions and comments, please contact Yipeng ([email protected]) or Dr. Wei Li ([email protected]).
The package is not on CRAN yet. For installation please use the following codes in R
install.packages("devtools")
library(devtools)
install_github("YiPeng-Gao/scDaPars")
The imputation steps of scDaPars takes the APA matrix from step.1 as input and in the simplest case, the imputation can be done with one single function scDaPars:
scDaPars(raw_PDUI_file, # full path of the raw PDUI matrix generated by step1 of scDaPars
out_dir, # full path of the output directory
filter_gene_thre, # the percent of cells a gene's APA must be detected in step.1
filter_cell_thre) # the percent of gene APA a cell must be detected in step.1
The dataset used in this example is a time-course scRNA-seq dataset containing 758 cells sequenced at 0, 12, 24, 36, 72 and 96 h of differentiation during human definitive endoderm (DE) emergence from Chu et al. under GEO accession code GSE75748. After quality control, there is 739 cells remained for analysis.
- Genearting raw PDUI matrix
For generating raw PDUI matrix in step 1, we assume that the scRNA-seq data has been preprocessed, so that we have one wiggle file per cell. The raw PDUI files are then generated by DaPars2. The raw PDUI matrix for this example "Dapars_hESC_combined_all_chromosome.txt" is included in the example folder.
- install and load scDaPars R package
if(!require(devtools)) install.packages("devtools")
library(devtools)
## Loading required package: devtools
## Loading required package: usethis
devtools::install_github("YiPeng-Gao/scDaPars")
library(scDaPars)
## Loading required package: survival
## Welcome to penalized. For extended examples, see vignette("penalized").
## Loading required package: foreach
## Loading required package: RANN
## Loading required package: igraph
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
- Run scDaPars
scDaPars.res = scDaPars(raw_PDUI_file = "Dapars_hESC_combined_all_chromosome.txt",
out_dir = "./scDaPars_result",
filter_gene_thre = 0.2,
filter_cell_thre = 0.1)
## [1] "Reading in raw PDUI matrix ..."
## [1] "number of genes in raw count matrix 13029"
## [1] "number of cells in raw count matrix 743"
## [1] "Reading finished!"
## [1] "Start Processing raw PDUI matrix"
## [1] "Pre-Processing finished"
## [1] "Start Imputation Steps ..."
## [1] "Find potential Neighboring Cells ..."
## [1] "Number of neighbors(clusters) is 4"
## [1] "Outliers is/are "
## [1] "Imputation with neighboring cells ..."
## [1] 100
## [1] 100
## [1] 100
## [1] 200
## [1] 300
## [1] "Imputation Steps Finished!"
## [1] "Writing imputed PDUI matrix ..."
head(scDaPars.res)[,1:6]
## SRR2978558 SRR2978559 SRR2978560 SRR2978561 SRR2978562
## ENST00000000412.3 1.00000000 0.804251535 0.857067085 1.000000000 0.706309880
## ENST00000002165.10_1 0.01711468 0.002728381 0.001751477 0.001465125 0.001576918
## ENST00000003100.13_3 0.05000000 0.607145404 0.496367748 0.733390448 0.611959391
## ENST00000005082.13_1 0.75459201 1.000000000 0.788302984 0.110000000 0.672660621
## ENST00000005257.7_2 0.15491832 0.000000000 0.289365021 0.285070704 0.224915218
## ENST00000005259.8_2 0.05000000 0.046576290 0.036624434 0.050765637 0.070246239
## SRR2978564
## ENST00000000412.3 0.710688649
## ENST00000002165.10_1 0.002705341
## ENST00000003100.13_3 0.523058864
## ENST00000005082.13_1 0.655225597
## ENST00000005257.7_2 1.000000000
## ENST00000005259.8_2 0.071559391
- Visualize scDaPars' results
#Load cell type information for the example data
hESC_SRA = read.table("SraRunTable.txt", header = T, sep = ",", stringsAsFactors = F)
cell_type = hESC_SRA[which(hESC_SRA$Run %in% colnames(scDaPars.res)),]
cell_type = cell_type[match(colnames(scDaPars.res), cell_type$Run),]
head(cell_type)
## Run Assay.Type AvgSpotLen BioProject BioSample Center.Name
## 933 SRR2978558 RNA-Seq 51 PRJNA305280 SAMN04322527 GEO
## 934 SRR2978559 RNA-Seq 51 PRJNA305280 SAMN04322528 GEO
## 935 SRR2978560 RNA-Seq 51 PRJNA305280 SAMN04322529 GEO
## 936 SRR2978561 RNA-Seq 51 PRJNA305280 SAMN04322530 GEO
## 937 SRR2978562 RNA-Seq 51 PRJNA305280 SAMN04322531 GEO
## 939 SRR2978564 RNA-Seq 51 PRJNA305280 SAMN04322533 GEO
## Consent DATASTORE.filetype DATASTORE.provider
## 933 public sra gs,ncbi,s3
## 934 public sra gs,ncbi,s3
## 935 public sra gs,ncbi,s3
## 936 public sra gs,ncbi,s3
## 937 public sra gs,ncbi,s3
## 939 public sra gs,ncbi,s3
## DATASTORE.region Experiment facs_sorting GEO_Accession
## 933 gs.US,ncbi.public,s3.us-east-1 SRX1468145 not sorted GSM1965870
## 934 gs.US,ncbi.public,s3.us-east-1 SRX1468146 not sorted GSM1965871
## 935 gs.US,ncbi.public,s3.us-east-1 SRX1468147 not sorted GSM1965872
## 936 gs.US,ncbi.public,s3.us-east-1 SRX1468148 not sorted GSM1965873
## 937 gs.US,ncbi.public,s3.us-east-1 SRX1468149 not sorted GSM1965874
## 939 gs.US,ncbi.public,s3.us-east-1 SRX1468151 not sorted GSM1965876
## Instrument LibraryLayout LibrarySelection LibrarySource MBases
## 933 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 105
## 934 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 116
## 935 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 141
## 936 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 99
## 937 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 90
## 939 Illumina HiSeq 2500 SINGLE cDNA TRANSCRIPTOMIC 103
## MBytes Organism passage Platform ReleaseDate sample_acc
## 933 65 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194214
## 934 72 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194213
## 935 85 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194212
## 936 61 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194211
## 937 54 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194210
## 939 62 Homo sapiens passage 30-35 ILLUMINA 2016-07-22T00:00:00Z SRS1194208
## Sample.Name source_name SRA.Study
## 933 GSM1965870 H9 cells differentiated for 12 hours SRP067036
## 934 GSM1965871 H9 cells differentiated for 12 hours SRP067036
## 935 GSM1965872 H9 cells differentiated for 12 hours SRP067036
## 936 GSM1965873 H9 cells differentiated for 12 hours SRP067036
## 937 GSM1965874 H9 cells differentiated for 12 hours SRP067036
## 939 GSM1965876 H9 cells differentiated for 12 hours SRP067036
Perform UMAP analysis
scDaPars.res.umap = umap(t(scDaPars.res))
scDaPars.res.umap.data = data.frame(scDaPars.res.umap$layout)
colnames(scDaPars.res.umap.data) = c("Dim1", "Dim2")
scDaPars.res.umap.data$cellType = cell_type$source_name
Generate Scatter plot
ggplot(scDaPars.res.umap.data, aes(x=Dim1, y=Dim2, color = cellType)) +
scale_color_manual(values = c("#7DD2D9", "#FFA500", "#e55b54", "#ad6c58", "#989797", "#166FD5")) +
geom_point(size = 0.5) +
theme_bw(base_size = 14) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(legend.position = "right", legend.title = element_blank())
