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Code and Data Availability for Single cell transcriptomics reveals a signaling roadmap coordinating endoderm and mesoderm lineage diversification during foregut organogenesis
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ZornLab/Single-cell-transcriptomics-reveals-a-signaling-roadmap-coordinating-endoderm-and-mesoderm-lineage
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README for GitHub Repo "Single-cell-transcriptomics-reveals-a-signaling-roadmap-coordinating-endoderm-and-mesoderm-lineage" from ZornLab License : GPLv3.0 Contact : Please report Bugs, Issues and Improvements on Github Requirements: R version[>=3.4], PERL version[>=5.16] and PYTHON version[2.7] *** Please see that all the source codes have extensively tested on MacOS and Linux [Redhat]. Scripts have not been tested on Windows OS. *** *** Each Script provides information on Inputs, processing steps and output files ***** ######################################################################################## ############################################ Cellranger_processing_Scripts: #################### Install Cellranger [v1.3.0] #################### Install bcl2fastq [v2.18.0] #################### Install samtools [v1.8.0] #################### cellranger.csv is required to run cellranger #################### PATH to reference transcriptome files is required to run cellranger #################### PATH-FASTQ path to fastq files in required to run cellranger #################### For issues running cellranger, please contact 10x genomics #################### ./cellrangerv1.3.0_Process.sh ############################################# ############################################# MetaGene_Profile_Calculation: Two steps: 1) Run GenerateMetaprofile_ForGeneSets.pl 2) Run Seuratv3.0_MetaProfile_Dotplot.r GenerateMetaprofile_ForGeneSets.pl ################## Script Requires two Inputs : 1) Directory with counts files [For example: Expression_Ligands_BMP.txt [this file has all BMP ligands and their counts] and similarly Expression_Receptors_Hedgehog.txt and so on] 2) OutputDirectory ################## How to run : perl GenerateMetaprofile_ForGeneSets.pl CountsDIR MetaProfileDIR ################## Please see that script requires counts files to be named in the following manner : Expression_Ligands_BMP.txt, Expression_Ligands_RA.txt, Expression_Response_FGF.txt etc. ################## Run time 15-20 mins [For 30 counts files where each file has 5-10 genes and counts across ~14k cells] Run Seuratv3.0_MetaProfile_Dotplot.r ############ This script processes MetaProfiles of GeneSets to create DotPlots using Seurat [v3.0] ############ Please Refer to Seurat [v3.0] manual for details on parameters and functions ############ Inputs: working dir, Output of GenerateMetaprofile_ForGeneSets.pl, metafile and geneinfo [See example files on Github] ############################################ ########################################### Monocle3_Pseudotime_Analysis: ############ Monoclev3_TrajectoryAnalysis.r processes Pseudotime Analysis using Monocle3 [v3.0 alpha]############### ############ Please note only Markers obtained from Seurat were used to drive Pseudotime analysis ############ Please Refer to Monocle [v3.0 alpha] manual for details on parameters and functions ############ Inputs : Counts Matrix, Metafile [infomration on cells and their classification[if available]], GeneInfo ########################################### ############################################ PseudoSpaceOrdering_Analysis: ############ PseudotimeDistribution_Using_URD.r processes Pseudospace Ordering of cells using URD [v1.0]############### ############ Please Refer to URD [v1.0] manual for details on parameters and functions ############ Please note markers obtained from Seurat were used as variable genes to drive pesudospace analysis ############ Inputs : Counts matrix, Metafile, GeneInfo ############################################ ############################################ Seurat_Analysis_Scripts: ############ R scripts processes Cells and their transcriptome using Seurat [v2.3.4]############### ############ Script carries out basic filtering, global scaling based normalization and scaling using Seurat Functions ############ Script regresses out Cellcycle difference between G2m and S phase using ScaleData Function ############ Please Refer to Seurat [v2.3.4] manual for details on parameters and functions ############ for Analysis of endoderm and mesoderm blood, mitochondrial, ribosomal and certain ncRNA were regressed out ############ Inputs : Counts Matrix, Metafile, GeneInfo ############################################# ############################################# SingleCellVoting_UsingKNN: Two steps: 1) Run KNN_Classification.r 2) Run Generate_Consensus_NormalizedVoteProbabilityMatrix.pl Run KNN_Classification.r: #################### Single Cell Voting using KNN classification algorithm #################### Please see Github for sample files #################### Inputs : TrainSet.txt and TestSet.txt #################### Please use >= R/3.4.4 #################### Some Issues that can occur : problems with installation of KODAMA and knnflex packages and if using > R/3.5.0 then please follow Part2 of the script #################### Please see Generate_Consensus_NormalizedVoteProbabilityMatrix.pl is not required when using Part2 analysis method in KNN_Classification.r Generate_Consensus_NormalizedVoteProbabilityMatrix.pl: ############## Perl Script to generate Consensus Normalized vote probability matrix ############## Inputs : 1) ProbabilityMatrix.txt [from KNN_Classification.r] 2) MetaFile.txt [Training Set Cells and their cluster annotation] ############## how to run : perl Script_Generate_Consensus_Matrix.pl ProbabilityMatrix.txt MetaFile.txt ############################################### ############################################### SPRING_Analysis_Scripts: ################################# Please Download SPRING [v1.0] from (https://github.com/AllonKleinLab/SPRING) ################################# This script is a modified version of the regular SPRING processing script ################################# Script is designed to learn Principle Component Space from most complex dataset [in terms of lineage diversification] and then transform the whole dataset using learnt PC space to enhance lineage diversification through time ################################# Please see SPRING github page on information on file formats and functions ################################# Please use python v2.7 ################################# Once SPRING directory is generated please follow the steps below to visualize the SPRING analysis ################################# 1) python -m SimpleHTTPServer 8000 & 2) http://localhost:8000/springViewer.html?datasets/SPRINGDIR_DATASET ############################################### ############################################### # TranscriptionFactor_EnrichmentAnalysis: ############ Seuratv3.0_TFEnrichment_Analysis.r carries out Transcription Factor Enrichment Analysis using Seurat [v3.0] ############ Inputs: TF counts matrix, Metafile, GeneInfo ############ Please Refer to Seurat [v3.0] manual for details on parameters and functions ############ Animal Transcription Factors are provided along with the scripts ############################################### ############################################## RShiny App Please access Shiny App using: (https://research.cchmc.org/ZornLab-singlecell) ### Requirements: R version[>=3.6]and PYTHON version[2.7] R packages required: shiny reticulate servr Seurat ggplot2 dplyr DT ggcorrplot ############################################
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