BMC Bioinformatics

Table 1 Comparison of model fits to a single group of biological replicates

From: Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

				mean AIC			mean BIC
	N	tags	k	Poisson	Negbin	Mixture	Poisson	Negbin	Mixture
BRAIN
astrocytoma	14	1141	2.6	238.2	105.2	103.6	238.9	106.5	106.3
ependymoma	10	1205	2.3	152.4	80.9	75.0	152.7	81.5	76.1
glioblastoma	7	1197	2.3	139.5	57.6	53.0	139.4	57.5	52.8
medulloblastoma	18	1045	2.7	280.6	128.7	128.7	281.5	130.5	132.6
normal	8	1099	2.4	156.8	68.0	59.8	156.8	68.2	60.1
AML
inv(16)	5	900	1.7	68.9	39.3	37.6	68.5	38.5	36.7
t(8;21)	5	1037	1.3	52.3	34.1	33.5	51.9	33.3	32.9
t(15;17)	5	709	1.8	127.7	46.0	38.9	127.3	45.2	37.9
t(9;11) de novo	4	954	1.8	58.5	34.6	30.1	57.9	33.4	28.5
t(9;11) treatment	3	1061	1.5	42.9	32.0	20.9	42.0	30.2	19.1
BREAST
normal	6	1259	1.8	71.6	43.9	41.6	71.4	43.5	41.0
DCIS	4	598	1.3	25.5	24.0	21.0	24.9	22.8	20.1
invasive	3	1069	2.0	60.4	27.8	22.8	59.5	26.0	20.1
SKIN
normal	4	1015	1.6	33.8	24.6	22.2	33.2	23.4	20.9
melanoma	3	992	1.8	38.2	24.0	19.6	37.3	22.2	17.4

SAGE tag counts from fifteen sets of biological replicates were fit to log-linear (Poisson), negative binomial (overdispersed log-linear), and Poisson mixture models. The table contains the number of replicates (N), tags tested, and mean number of mixture components (k). For each model, mean goodness of fit scores calculated using Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) are shown. For both scores, a lower value indicates a better fit.

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