pseudotime

iCellR is an interactive R package designed to facilitate the analysis and visualization of high-throughput single-cell sequencing data. It supports a variety of single-cell technologies, including scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq, and Spatial Transcriptomics (ST).

Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

A deep learning architecture for robust inference and accurate prediction of cellular dynamics

a scalable python suite for tree inference and advanced pseudotime analysis from scRNAseq data.

Implementation of MaSigPro for scRNA-Seq Data

Characterize gene dynamics over trajectories using GLMs, GEEs, & GLMMs.

Pseudotime analysis for time-series single-cell sequencing and imaging data

A composite regression neural network for latent timing prediction of single-cell RNA-seq data

MiCV is a python dash-based web-application that enables researchers to upload raw scRNA-seq data and perform filtering, analysis, and manual annotation.

拟南芥根单细胞RNA-seq分析：细胞图谱、发育轨迹与细胞通讯

Modeling cell differentiation as continuous developmental flow through pseudotime instead of static clusters. Inspired by Waddington’s epigenetic landscape with interactive trajectory, branching, and uncertainty visualizations.

Inverse optimal transport for single-cell trajectory analysis in R: Anderson-accelerated soft Sinkhorn with exact implicit gradients, two-stage debiased fitting, transition matrices, random-walk pseudotime, ggplot2 visualisation, Seurat/SingleCellExperiment interfaces. Bundled demo data + real-data showcase.

Scripts for analysis of transcriptomic data of the developing cornea

my_RNA_seq_pipelines

Repository for benchmarking study of scRNA-Seq datasets for clustering and trajectory inference

Single-cell RNA-seq analysis — Pandey et al. 2022

TENET refined

Pseudotime analysis in newer versions of R using monocle2

Bioinfo scripts for the analyses described in EXPERIMENTAL PROCEDURES section of "Structured wound angiogenesis instructs mesenchymal barrier compartments in the regenerating nerve" manuscript

Trajectory analysis on scRNA-seq data from SARS-CoV-2-infected human bronchial epithelial cells (HBEC). Scanpy. Dataset Source: Ravindra et al. (2021). Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes. PLoS Biology, 19(3), e3001143.