diff --git a/R/predict.R b/R/predict.R
index da200eb..3582495 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -134,6 +134,8 @@ project_poisson_nmf <- function (X, F, numiter, ...) {
   rownames(L0) <- rownames(X)
   colnames(L0) <- colnames(F)
   fit <- init_poisson_nmf(X,F = F,L = L0)
-  fit <- fit_poisson_nmf(X,fit0 = fit,numiter = numiter,...)
+
+  # Fix the factors and update the loadings.
+  fit <- fit_poisson_nmf(X,fit0 = fit,numiter = numiter, update.factors = NULL, ...)
   return(fit)
 }
diff --git a/tests/testthat/test_predict.R b/tests/testthat/test_predict.R
new file mode 100644
index 0000000..57dd217
--- /dev/null
+++ b/tests/testthat/test_predict.R
@@ -0,0 +1,20 @@
+context("predict")
+
+# Test that project_poisson_nmf returns F with the same structure as the input F
+test_that("project_poisson_nmf returns F with the same structure as the input F", {
+
+  set.seed(1)
+  dat   <- simulate_multinom_gene_data(175,1200,k = 3)
+  train <- dat$X[1:100,]
+  test  <- dat$X[101:175,]
+
+  fit    <- init_poisson_nmf(train,F = dat$F,init.method = "random")
+  fit    <- fit_poisson_nmf(train,fit0 = fit,verbose = "none")
+  fit    <- poisson2multinom(fit)
+  F_pois <- multinom2poisson(fit)$F
+
+  out <- project_poisson_nmf(test,F_pois,numiter = 20,verbose = "none", update.factors = NULL)
+
+  norm_col <- function(M) sweep(M,2,colSums(M),"/")
+  expect_equal(norm_col(out$F),norm_col(F_pois),tolerance = 1e-10)
+})