tr_0 <- read.csv("AxoTC2_trinity/summarize_assemblies.output", header=TRUE) tr_1 <- read.csv("s_1_trinity/summarize_assemblies.output", header=TRUE) oa_1 <- read.csv("s_1_oases/summarize_assemblies.output", header=TRUE) cat(sprintf("%60s %24s %24s %24s\n", "statistic", "AxoTC2_trinity", "s_1_trinity", "s_1_oases")) for (n in names(tr_0)) { cat(sprintf("%60s %20.3f %20.3f %20.3f\n", n, tr_0[[n]], tr_1[[n]], oa_1[[n]])) } Note: Many of these statistics, notably the RSEM scores, are not really commensurable between ..._trinity and ..._s_1 columns, because the s_1 reads are only a subset of the full AxoTC2 reads. statistic AxoTC2_trinity s_1_trinity s_1_oases assembly.N25 1711.000 775.000 635.000 assembly.N50 819.000 448.000 340.000 assembly.N75 369.000 288.000 194.000 assembly.longest 11131.000 6275.000 5793.000 assembly.mean 576.742 415.735 221.692 assembly.median 340.000 311.000 158.000 assembly.shortest 201.000 201.000 0.000 assembly.num.contigs 98560.000 35161.000 74074.000 num.oracleset.in.assembly 12453.000 2219.000 1144.000 frac.oracleset.in.assembly 0.109 0.019 0.010 num.assembly.in.oracleset 11024.000 7539.000 11606.000 frac.assembly.in.oracleset 0.112 0.214 0.157 num.oracleset.in.assembly.without.check.insdel 12750.000 2320.000 1245.000 frac.oracleset.in.assembly.without.check.insdel 0.112 0.020 0.011 num.assembly.in.oracleset.without.check.insdel 11158.000 7627.000 11767.000 frac.assembly.in.oracleset.without.check.insdel 0.113 0.217 0.159 allmatches.num.oracleset.in.assembly 19795.000 3965.000 1331.000 allmatches.frac.oracleset.in.assembly 0.174 0.035 0.012 allmatches.num.assembly.in.oracleset 13747.000 9671.000 18364.000 allmatches.frac.assembly.in.oracleset 0.139 0.275 0.248 allmatches.num.oracleset.in.assembly.without.check.insdel 20901.000 4286.000 1468.000 allmatches.frac.oracleset.in.assembly.without.check.insdel 0.183 0.038 0.013 allmatches.num.assembly.in.oracleset.without.check.insdel 14000.000 9818.000 18648.000 allmatches.frac.assembly.in.oracleset.without.check.insdel 0.142 0.279 0.252 rsem.approx.approx -6317422787.384 -995780189.260 -1587461448.591 rsem.approx.bic -6317817353.864 -995916467.613 -1587728180.545 rsem.approx.loglikelihood -6316897176.022 -995624243.060 -1587116395.379 rsem.approx.loglikelihood.penalty 920177.842 292224.554 611785.166 rsem.eval.lognumer.minus.logdenom -6317390745.668 -995775904.064 -1587398259.693 rsem.eval.logprior 1034731.020 332899.699 751388.095 rsem.eval.loglikelihood -6316900402.975 -995625315.537 -1587140990.323 rsem.eval.logdenom 1525073.713 483488.226 1008657.465 rsem.prior.log.prob.M -1092.804 -37434.782 -8168.602 rsem.prior.log.prob.L.given.M -725351.161 -249679.407 -499988.717 rsem.prior.log.prob.Sequences.given.L.and.M -78802067.504 -20264357.450 -22765178.412 rsem.prior.log.prob.A -79528511.469 -20551471.639 -23273335.731 rsem.eval.loglikelihood.plus.rsem.prior.log.prob.A -6396428914.445 -1016176787.176 -1610414326.054 rsem.approx.loglikelihood.plus.rsem.prior.log.prob.A -6396425687.491 -1016175714.699 -1610389731.110 rsem.approx.approx.plus.rsem.prior.log.prob.A -6396951298.854 -1016331660.900 -1610734784.322 rsem.approx.bic.plus.rsem.prior.log.prob.A -6397345865.334 -1016467939.253 -1611001516.276 rsem.ss.mean.num.reads.per.transcript 1304.819 470.544 224.503 rsem.ss.median.num.reads.per.transcript 28.000 27.000 8.000 rsem.ss.num.transcripts.with.zero.reads 266.000 44.000 21407.000 rsem.ss.num.matching.bases 8100465754.805 923959489.152 381517798.721 rsem.ss.num.mismatching.bases 317283058.249 70213390.013 60326581.458