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 *****

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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
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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]
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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
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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
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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
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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
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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
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# 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
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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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