Description Usage Arguments Details Value Author(s) References Examples
A method to pick out DCGs from microarray data based on 'Weighted Gene Coexpression Network Analysis' (WGCNA) (Mason, MJ. Et al. 2009; van Nas et al. 2009 ).
1 |
exprs.1 |
a data frame or matrix for condition A, with rows as variables (genes) and columns as samples. |
exprs.2 |
a data frame or matrix for condition B, with rows as variables (genes) and columns as samples. |
power |
the thresholding parameter, an integer >1. |
variant |
if the variant is 'WGCNA' the original version is evoked; if it is 'DCp', the length-normalized Euclidean distance is adopted to replace the connectivity difference measure. |
The 'weighted gene coexpression network analysis' (WGCNA) weights links with correlation coefficients and compares the sums of the correlation coefficients of a gene (Mason, et al., 2009; van Nas, et al., 2009). Correlation coefficients are firstly softly thresholded by a 'power'.
WGCNA |
score of 'WGCNA' to identify DCGs |
Bao-Hong Liu, Hui Yu
Mason, M.J., et al. (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells, BMC Genomics, 10, 327.
van Nas, A., Guhathakurta, D., Wang, S.S., Yehya, N., Horvath, S., Zhang, B., Ingram-Drake, L., Chaudhuri, G., Schadt, E.E., Drake, T.A., Arnold, A.P. and Lusis, A.J. (2009) Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks, Endocrinology, 150, 1235-1249.
1 2 |
AACS FSTL1 ELMO2 CREB3L1 RPS11 PNMA1
0.032491573 0.340345290 0.003607475 0.106060297 0.487543899 0.688860893
MMP2 SAMD4A SMARCD3 A4GNT PKNOX2 RALYL
0.055532587 0.306929359 0.143748972 0.421625128 0.296036651 0.164518603
ZHX3 ERCC5 GPR98 RXFP3 APBB2 BBOX1
0.083832350 0.112679144 0.073334981 0.066650504 0.008716904 0.024313279
PRO0478 XDH EDN1 MTERF AEN CLK4
0.399647460 0.356304624 0.008536358 0.020476887 0.172706050 0.064379101
KCNG1 CXCR4 DECR1 SALL1 PTPRR CADM4
0.029348125 0.037726173 0.142339497 0.192265472 0.017252518 0.253869309
IRAK1 CFHR5 TMSB10 CXCL3 LMAN1 CHD8
0.310181364 0.129817868 0.435033351 0.072471863 0.327807434 0.786545837
SUMO1 GP1BA OR7A10 DDB1 CHRNA10 STYK1
0.535912883 0.076253872 0.057791157 0.047250211 0.266437916 0.025677555
MYO9B CCNI MMP7 EP300 CRNKL1 C9orf45
0.240185468 0.040275941 0.068641074 0.453033899 0.455865429 0.077939731
XAB2 RTN1 HIC2 TBX10 CENPQ UTY
0.700706213 0.083996659 0.075069922 0.013703652 0.144338051 0.025968217
OR2W1 KCNA6 ATP5G2 ZEB1 ERG FAT4
0.221261970 0.010344958 0.044776060 0.317287703 0.260191021 0.276681666
PARN SOD2 CYTH1 ADAM5P CHD9 STK16
0.685927044 0.137672950 0.037275193 0.233734384 0.178130469 0.144813112
PDE1C SEMA4D AGPAT1 TOB2 BANK1 MAP3K3
0.174172649 0.150983942 0.524095428 0.320666481 0.070302341 0.206953214
MAX GRM2 OSBPL8 PROSC NR4A2 RICS
0.064727567 0.142152824 0.619112319 0.011615626 0.130812813 0.030023983
PIR PPCS IPO9 LONP1 EVC CXCL13
0.068985938 0.545802591 0.600311463 0.148346756 0.031150027 0.746594291
FFAR3 SCYL3 KIAA1199 SORL1 NAT10 CHD1
0.287071471 0.009680740 0.001102983 0.194798923 0.461902193 0.375919224
SYN3 DMC1 SLC22A2 SERPINF1 C20orf27 OR7A17
0.170271991 0.146553939 0.006574458 0.065901303 0.095633301 0.289318753
RPS6KA5 HMX1 DHRS11 LHB
0.537222145 0.225287530 0.204918591 0.186244528
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