A method for selecting a treatment option for an activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) subject or a germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL) subject comprising the steps of: (a) isolating a gene expression product from a biopsy sample from a lymphoma subject; (b) obtaining digital gene expression data from the isolated gene expression product, wherein the digital gene expression data comprises data for genes in a gene expression signature, and wherein the gene expression signature comprises at least one of the genes listed in Table 2; (c) optionally obtaining digital gene expression data from a first and second reference set of probe sets, the first and second reference set of probe sets being known for classifying ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL; (d) calculating a predictor score using the equation: y i = log 2 x i − ∑ j log 2 x j h j − log 2 r i + log 2 g i wherein y i is a predictor score for gene i, x i is the counts for probe set i for gene i on the sample being tested, x j is the counts for probe set j for gene j, probe set j is the set of normalization genes of the sample being tested, h j is the normalization weight for probe set j as listed in Table 4, r i is the counts for probe set i for gene i on the first reference set of probe sets, and g i is the counts for probe set i for gene i on the second reference set of probe sets, the mathematical terms with r i and g i being equal to zero if no digital gene expression data is obtained from a first and second reference set of probe sets; (e) calculating a Bayesian sub-model score for each of ABC/GCB DLBCL, BL non-myc, BL myc, PMBL, and MCL using the equation: B submodel = φ ∑ i y i w i; m 1, v 1 φ ∑ i y i w i; m 1, v 1 + φ ∑ i y i w i; m 2, v 2 wherein y i is the value assigned probe set i in (d); w i are the weights associated with that probe set for the particular model as listed in Table 4; m 1, v 1, m 2, and v 2 are values associated with the submodel as listed in Table 7, and Φ is the Gaussian density defined as: φ y m v = 1 v 2 π e − 1 2 y − m v 2; (f) combining the Bayesian sub-model scores for BL non-myc and BL myc using the formula: B BL = { B BLnon-mye if B BLmye > 0.1 min B BLnon-mye 0.5 if B BLmye ≤ 0.1; (g) calculating the confidence values of each subtype using: MCL confidence = B MCL BL confidence = B BL 1 − B MCL PMBL confidence = B PMBL 1 − B BL 1 − B MCL ABC confidence = B ABC/GCB 1 − B PMBL 1 − B BL 1 − B MCL GCB confidence = 1 − B ABC / GCB 1 − B PMBL 1 − B BL 1 − B MCL; (h) classifying the subject as belonging to one of the following groups: ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL; and (i) selecting a treatment option for the subject based on the subject's classification in (h) if the classification is ABC DLBCL or GCB DLBCL. (a) isolating a gene expression product from a biopsy sample from a lymphoma subject; (b) obtaining digital gene expression data from the isolated gene expression product, wherein the digital gene expression data comprises data for genes in a gene expression signature, and wherein the gene expression signature comprises at least one of the genes listed in Table 2; (c) optionally obtaining digital gene expression data from a first and second reference set of probe sets, the first and second reference set of probe sets being known for classifying ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL; (d) calculating a predictor score using the equation: y i = log 2 x i − ∑ j log 2 x j h j − log 2 r i + log 2 g i wherein y i is a predictor score for gene i, x i is the counts for probe set i for gene i on the sample being tested, x j is the counts for probe set j for gene j, probe set j is the set of normalization genes of the sample being tested, h j is the normalization weight for probe set j as listed in Table 4, r i is the counts for probe set i for gene i on the first reference set of probe sets, and g i is the counts for probe set i for gene i on the second reference set of probe sets, the mathematical terms with r i and g i being equal to zero if no digital gene expression data is obtained from a first and second reference set of probe sets; (e) calculating a Bayesian sub-model score for each of ABC/GCB DLBCL, BL non-myc, BL myc, PMBL, and MCL using the equation: B submodel = φ ∑ i y i w i; m 1, v 1 φ ∑ i y i w i; m 1, v 1 + φ ∑ i y i w i; m 2, v 2 wherein y i is the value assigned probe set i in (d); w i are the weights associated with that probe set for the particular model as listed in Table 4; m 1, v 1, m 2, and v 2 are values associated with the submodel as listed in Table 7, and Φ is the Gaussian density defined as: φ y m v = 1 v 2 π e − 1 2 y − m v 2; (f) combining the Bayesian sub-model scores for BL non-myc and BL myc using the formula: B BL = { B BLnon-mye if B BLmye > 0.1 min B BLnon-mye 0.5 if B BLmye ≤ 0.1; (g) calculating the confidence values of each subtype using: MCL confidence = B MCL BL confidence = B BL 1 − B MCL PMBL confidence = B PMBL 1 − B BL 1 − B MCL ABC confidence = B ABC/GCB 1 − B PMBL 1 − B BL 1 − B MCL GCB confidence = 1 − B ABC / GCB 1 − B PMBL 1 − B BL 1 − B MCL; (h) classifying the subject as belonging to one of the following groups: ABC DLBCL, GCB DLBCL, PMBL, BL, or MCL; and (i) selecting a treatment option for the subject based on the subject's classification in (h) if the classification is ABC DLBCL or GCB DLBCL.