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RBind specifications


Unique identifier OMICS_29083
Name RBind
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input data A structure information.
Input format PDB
Output format TXT,DAT
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes




No version available


  • person_outline Yunjie Zhao

Publication for RBind

RBind citations


Sulcal Polymorphisms of the IFC and ACC Contribute to Inhibitory Control Variability in Children and Adults

PMCID: 5844057
PMID: 29527565
DOI: 10.1523/ENEURO.0197-17.2018
call_split See protocol

[…] up (“children” vs “adults”), and the gender (“female” vs “male”). Specifically, we implemented the mixed-effect linear model using repeated-measures MANOVA () as detailed below (R syntax):CONDITION = rbind('incongruent','congruent')idata <- data.frame(CONDITION)lm.model <- lm(cbind(RT_Stroop_Incongruent, RT_Stroop_Congruent) ∼ (IFC_Asymetry + ACC_Asymetry) * AgeGroup + Sex)Anova(lm.model, idata=id […]


Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

PMCID: 5850119
PMID: 29568492
DOI: 10.5256/f1000research.14748.r30355

[…] gs <- merge(snps_hard_in_fantom, degs, by = "symbol", all = FALSE) We have identified 7 genes whose putative enhancers contain SLE GWAS SNPs. Let's add these to our list: prioritised_hits <- unique(rbind(prioritised_hits, data.frame( snp_id = snps_hard_in_fantom_in_degs$SNPS, snp_pvalue = snps_hard_in_fantom_in_degs$P.VALUE, snp_location = snps_hard_in_fantom_in_degs$LOCATION, gene_id = […]


lakemorpho: Calculating lake morphometry metrics in R

PMCID: 5698920
PMID: 29188019
DOI: 10.5256/f1000research.13548.r26223

[…] = 12 , expand = 1000 , src = "aws" ) * 3.281 lmorph <- lakeSurroundTopo ( inLake = ri_lakes[i,], inElev = dem) lmetric <- calcLakeMetrics (lmorph, bearing = 270 , pointDens = 100 ) output <- rbind (output, data.frame ( NAME = ri_lakes[i,] $ NAME, data.frame (lmetric))) } We can now merge the morphometry metrics back to the lake polygons. ri […]


Simple yet effective: Historical proximity variables improve the species distribution models for invasive giant hogweed (Heracleum mantegazzianum s.l.) in Poland

PLoS One
PMCID: 5604976
PMID: 28926580
DOI: 10.1371/journal.pone.0184677
call_split See protocol

[…] XENT).In order to assess the effect of an algorithm and HPV characteristics on the model quality, we melted modelling results using the function melt() from the reshape2 package, row-binded using the rbind() function, and then added the following HPV set and model attributes:model algorithm used,combination of information capacity variables (“P”, “C”, “D”, “CD” or control),minimal recentness of ti […]


CyTOF workflow: differential discovery in high throughput high dimensional cytometry datasets

PMCID: 5473464
PMID: 28663787
DOI: 10.5256/f1000research.12553.r23055

[…] (k){ contr_tmp <- glht (fit_tmp, linfct = matrix (k, 1 )) summ_tmp <- summary (contr_tmp) pval <- summ_tmp $ test $ pvalues return (pval) }) return (out) }) pvals <- do.call (rbind, fit_binomial) colnames (pvals) <- paste0( "pval_" , contrast_names) rownames (pvals) <- rownames (counts) ## Adjust the p-values adjp <- apply (pvals, 2 , p.adjust, me […]


TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages

PMCID: 5302158
PMID: 28232861
DOI: 10.5256/f1000research.9601.r14695

[…] clinical patient data for LGG samples 5 lgg _ clin <– GDCquery _ clinic(project = "TCGA–LGG" , type = "Clinical" ) 6 7 # Bind the results, as the columns might not be the same, 8 # we will will plyr rbind.fill, to have all columns from both files 9 clinical <– plyr:: rbind .fill(gbm _ clin,lgg _ clin) 10 11 # if barcode is not set, it will consider all samples. 12 # We only set it to make the exa […]


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RBind institution(s)
Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, China; Department of Physics, The George Washington University, Washington, DC, USA
RBind funding source(s)
Supported by National Natural Science Foundation of China 11704140, Natural Science Foundation of Hubei 2017CFB116, the Thousand Talents Plan 31103201603, and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE 20205170045.

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