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A networks analysis pipeline for RNASeq time series data

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NetSeekR

A networks analysis pipeline for RNASeq time series data.

NetSeekR is a network analysis R package that includes the capacity to analyze time series of RNASeq data, perform correlation and regulatory network inferences and use network analysis methods to summarize the results of a comparative genomics study.

Authors: Himangi Srivastava, Drew Ferrell, and George V. Popescu.

The NetSeekR code requires specific versions for packages that are used.

Package Version
pacman 0.5.1
BiocManager 1.30.10
magrittr 1.5
readr 1.3.1
purrr 0.4.2
stringr 0.3.3
ggplot2 1.4.0
devtools 3.2.1
flashClust 2.2.1
tidyr 1.01-2
networkD3 1.0.0
igraph 0.4
limma 1.2.4.2
edgeR 3.42.0
topGO 2.37.0
WGCNA 1.68
biomaRt 2.42.0
Rgraphviz 2.30.0
dplyr 0.8.3

Below are the steps to run NetSeekR.

  1. Set the working directory to the NetSeekR path.

setwd(<<path/to/NetSeekR>>)

  1. Unzip the NetSeekR file.

unzip('NetSeekR.zip')

  1. Load packages and source functions for NetSeekR.

source('scripts/NetSeekR.R')

  1. Edit configuration file and sample comparison matrix.
  • note Below is a template configuration file which needs to be edited per usage.
analysis_type custom tag
design_matrix path to experimental design matrix
edger_adjustment_method edgeR: p-value adjustment method
edger_lfc limma: minimum log2-fold-change that is considered scientifically meaningful
edger_method NOT USED
feature_counts_path path to feature counts software
kallisto Boolean value for Kallisto execution decision
kallisto_bias sequence based bias correction
kallisto_bootstrap_samples bootstrap sample number
kallisto_chromosomes tab separated file with chromosome names and lengths
kallisto_fasta_files path to genome annotation file
kallisto_fastq_files reads to quantify
kallisto_fr_stranded strand specific reads, first read forward
kallisto_fragment_length estimated average fragment length
kallisto_fusion search for fusions for Pizzly
kallisto_genomebam project pseudoalignments to genome sorted BAM file
kallisto_gtf GTF file for transcriptome information (required for --genomebam)
kallisto_index location to write genome index from Kallisto (required for Kallisto alignment)
kallisto_kmer_size k-mer (odd) length (defaut: 31, max value: 31
kallisto_make_unique replace repeated target names with unique names
kallisto_output_dir directory to write quantification output to
kallisto_path path to Kallisto software
kallisto_plaintext output plaintext instead of HDF5
kallisto_pseudobam save pseudoalignments to transcriptome to BAM file
kallisto_rf_stranded strand specific reads, first read reverse
kallisto_sd estimated standard deviation of fragment length (default: -l, -s values are estimated from paired end data, but are required when using --single)
kallisto_seed seed for the bootstrap sampling (default: 42)
kallisto_single quantify single-end reads
kallisto_single_overhang include reads where unobserved rest of fragment is predicted to lie outside a transcript
kallisto_threads number of threads to use (default: 1)
sample_comparisons_file path to the sample comparison file for differential gene expression testing
sample_covariates experimental design matrix column names to be used as covariates with Sleuth
significance_cutoff a cutoff value for determining significance
sleuth_gene_mode Boolean value for Sleuth gene mode execution decision
sleuth_transcript_mode Boolean value for Sleuth transcript mode execution decision
star Boolean value for STAR execution decision
star_genomeDir path to the directory where the genome indices are stored
star_genomeFastaFiles path to a FASTA file with the genome reference sequences
star_path path to STAR software
star_readFilesIn path to the folder containing the sequences to be mapped
star_runThreadN number of threads to be used for genome generation, it has to be set to the number of available cores on the server node
star_sjdbGTFfile path to the file with annotated transcripts in the standard GTF format
star_sjdbOverhang length of the genomic sequence around the annotated junction to be used in constructing the splice junctions database>
  1. Align batches of reads.

alignment_results <- implement_alignment(arguments_file = <<path/to/configuration file>>)

  1. Test for differential gene expression.
  • note An edited sample comparison matrix needs to be supplied for differential gene expression testing. An example of an edited sample comparison matrix file is below. A sample comparison matrix does not need headers, only sample identifiers. Which samples to compare in differential testing should be written row-wise.
Example
SL209924 SL209925 SL209926 SL209921 SL209922 SL209923
SL209927 SL209928 SL209937 SL209921 SL209922 SL209923
SL209938 SL209939 SL209940 SL209921 SL209922 SL209923
SL209944 SL209945 SL209946 SL209941 SL209942 SL209943
SL209947 SL209948 SL209949 SL209941 SL209942 SL209943
SL209950 SL209951 SL209952 SL209941 SL209942 SL209943

implement_differential_gene_expression(alignment_results)

  1. Network analysis is then conducted assuming sets of differentially expressed genes are available.

implement_network_analysis(alignment_tool = 'star', alignment_results = alignment_results)

The network analysis function above will produce an image of the network. The image can be saved from the ‘Export’ tab at the top of the window.

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