Jul 06, 2023

Public workspaceClustering of differentially expressed genes

DOI
dx.doi.org/10.17504/protocols.io.rm7vzx82rgx1/v1
  • 1Institute of Biological Sciences, Faculty of Science, Universiti Malaya
Ahmad Husaini AHS Suhaimi
Icon indicating open access to content
QR code linking to this content
DOI: dx.doi.org/10.17504/protocols.io.rm7vzx82rgx1/v1
Protocol Citation: Ahmad Husaini AHS Suhaimi 2023. Clustering of differentially expressed genes. protocols.io https://dx.doi.org/10.17504/protocols.io.rm7vzx82rgx1/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: July 04, 2023
Last Modified: July 06, 2023
Protocol Integer ID: 84450
Abstract
This differentially expressed genes clustering pipeline utilizes coseq v3.17 package (Rau & Maugis-Rabusseau, 2018) in R.
Clustering of differentially expressed genes (DEG) using Coseq package in R
Clustering of differentially expressed genes (DEG) using Coseq package in R
Load the package (coseq).
Command
library(coseq)
library(matrixStats)



Run Coseq on transformed and normalized counts.
Example:
Performing clustering on bud data with expected clusters, K=5-16.
Clustering process is repeated for 10x.
Command
coseq_bud_logclr_1 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_2 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_3 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_4 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_5 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_6 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_7 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_8 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_9 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")
coseq_bud_logclr_10 = coseq(tcounts_logclr_exp_bud_ORF_scTMM[,1:15], K=5:16, normFactor = "none", transformation = "none")

Manually inspect the results and decide on the average number of clusters
Choose one clustering result to proceed with the subsequent steps
Command
summary(coseq_bud_logclr_1)
summary(coseq_bud_logclr_2)
summary(coseq_bud_logclr_3)
summary(coseq_bud_logclr_4)
summary(coseq_bud_logclr_5)
summary(coseq_bud_logclr_6)
summary(coseq_bud_logclr_7)
summary(coseq_bud_logclr_8)
summary(coseq_bud_logclr_9)
summary(coseq_bud_logclr_10)

Assigning clusters to transcripts
Assigning clusters to transcripts
Retrieve and tabulate the clustering information based on the chose clustering from the previous step.
Example:
coseq_bud_logclr_1 is chosen as the best clustering
results_coseq_bud_logclr: the new table/vector.
Command
results_coseq_bud_logclr = clusters(coseq_bud_logclr_1)

Convert the vector into a data frame.
Command
results_coseq_bud_logclr = data.frame(results_coseq_bud_logclr)

Create a column containing the assigned cluster number for each transcript in the read count data frame.
Example:
the new column: bud_logclr
the data frame with read counts: tcounts_logclr_exp_bud_ORF_scTMM
Command
tcounts_logclr_exp_bud_ORF_scTMM$bud_logclr = results_coseq_bud_logclr_1$results_coseq_bud_logclr

Protocol references
Rau A, Maugis-Rabusseau C (2018). “Transformation and model choice for co-expression analysis of RNA-seq data.” Briefings in Bioinformatics, 19(3), 425-436.