From 9cb4b9d56eb89acd61467f384e0f631d52bb9977 Mon Sep 17 00:00:00 2001
From: AmandaBirmingham <lists@imladris.com>
Date: Wed, 24 Apr 2024 16:44:31 -0700
Subject: [PATCH] Switch to fits based on raw counts rather than CPMS--simpler
 but requires test rewrites

---
 docs/absolute_quant_example.xlsx         | Bin 35690 -> 66304 bytes
 pysyndna/src/calc_cell_counts.py         |  41 +-
 pysyndna/src/fit_syndna_models.py        |  43 +-
 pysyndna/tests/data/modelling_input.tsv  | 392 +----------------
 pysyndna/tests/data/modelling_output.tsv |  47 +--
 pysyndna/tests/data/models.yml           |  30 +-
 pysyndna/tests/test_calc_cell_counts.py  | 512 +++++++++++++----------
 pysyndna/tests/test_fit_syndna_models.py | 234 +++--------
 8 files changed, 424 insertions(+), 875 deletions(-)

diff --git a/docs/absolute_quant_example.xlsx b/docs/absolute_quant_example.xlsx
index 1b932aea51af6c4cb3fb1d3a3bb05c04be682037..07e61dd4353d1f9bbb68838c3380acd000176c45 100644
GIT binary patch
delta 62145
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diff --git a/pysyndna/src/calc_cell_counts.py b/pysyndna/src/calc_cell_counts.py
index 8604125..f294eb1 100644
--- a/pysyndna/src/calc_cell_counts.py
+++ b/pysyndna/src/calc_cell_counts.py
@@ -31,8 +31,7 @@
 SEQUENCED_SAMPLE_GDNA_MASS_NG_KEY = 'sequenced_sample_gdna_mass_ng'
 OGU_ID_KEY = 'ogu_id'
 OGU_READ_COUNT_KEY = 'ogu_read_count'
-OGU_CPM_KEY = 'ogu_CPM'
-LOG_10_OGU_CPM_KEY = 'log10_ogu_CPM'
+LOG_10_OGU_READ_COUNT_KEY = 'log10_ogu_read_count'
 OGU_PERCENT_COVERAGE_KEY = 'percent_coverage_of_ogu'
 TOTAL_OGU_READS_KEY = 'total_reads_per_ogu'
 LOG_10_OGU_GDNA_MASS_NG_KEY = 'log10_ogu_gdna_mass_ng'
@@ -365,16 +364,11 @@ def _calc_ogu_cell_counts_df_for_sample(
     sample_df = working_df[
         working_df[SAMPLE_ID_KEY] == sample_id].copy()
 
-    # get the total reads sequenced for this sample
-    sample_total_reads = per_sample_info_df.loc[
-        per_sample_info_df[SAMPLE_ID_KEY] == sample_id,
-        SAMPLE_TOTAL_READS_KEY].values[0]
-
     # predict mass of each OGU's gDNA in this sample from its counts
     # using the linear model
     ogu_gdna_masses = _calc_ogu_gdna_mass_ng_series_for_sample(
             sample_df, linregress_result[SLOPE_KEY],
-            linregress_result[INTERCEPT_KEY], sample_total_reads)
+            linregress_result[INTERCEPT_KEY])
     sample_df[OGU_GDNA_MASS_NG_KEY] = \
         sample_df[OGU_ID_KEY].map(ogu_gdna_masses)
 
@@ -413,8 +407,7 @@ def _calc_ogu_cell_counts_df_for_sample(
 def _calc_ogu_gdna_mass_ng_series_for_sample(
         sample_df: pd.DataFrame,
         sample_linregress_slope: float,
-        sample_linregress_intercept: float,
-        sample_total_reads: int) -> pd.Series:
+        sample_linregress_intercept: float) -> pd.Series:
 
     """Calculates mass of OGU gDNA in ng for each OGU in a sample.
 
@@ -427,9 +420,6 @@ def _calc_ogu_gdna_mass_ng_series_for_sample(
         Slope of the linear regression model for the sample.
     sample_linregress_intercept: float
         Intercept of the linear regression model for the sample.
-    sample_total_reads: int
-        Total number of reads for the sample (including all reads, not just
-        aligned ones).
 
     Returns
     -------
@@ -439,23 +429,26 @@ def _calc_ogu_gdna_mass_ng_series_for_sample(
     """
     working_df = sample_df.copy()
 
-    # add a column of counts per million (CPM) for each ogu by dividing
-    # each read_count by the total number of reads for this sample
-    # and then multiplying by a million (1,000,000)
-    # NB: dividing int/int in python gives float
-    working_df[OGU_CPM_KEY] = (working_df[OGU_READ_COUNT_KEY] /
-                               sample_total_reads) * 1000000
+    # NOTE that the linear regressions were originally done as described in
+    # the Zaramela et al notebooks, where the log10 of the CPM values were
+    # used as the independent variable.  Later scripts by Oriane Moyne
+    # showed that this is not necessary and that it is equivalent to simply
+    # use log10 of the read counts as the independent variable (as long as it
+    # is used for *both* the fit and the prediction, of course!).  Please see
+    # documentation on the fit_syndna_models.src._fit_linear_regression_models
+    # method for a full description of this change.
 
-    # add column of log10(ogu CPM) by taking log base 10 of the ogu CPM column
-    working_df[LOG_10_OGU_CPM_KEY] = np.log10(working_df[OGU_CPM_KEY])
+    # add column of log10(ogu read counts)
+    working_df[LOG_10_OGU_READ_COUNT_KEY] = \
+        np.log10(working_df[OGU_READ_COUNT_KEY])
 
     # calculate log10(ogu gdna mass) of each OGU's gDNA in this sample
-    # by multiplying each OGU's log10(ogu CPM) by the slope of this sample's
-    # regression model and adding the model's intercept.
+    # by multiplying each OGU's log10(ogu read count) by the slope of this
+    # sample's regression model and adding the model's intercept.
     # NB: this requires that the linear regression models were derived
     # using synDNA masses *in ng* and not in some other unit.
     working_df[LOG_10_OGU_GDNA_MASS_NG_KEY] = (
-            working_df[LOG_10_OGU_CPM_KEY] *
+            working_df[LOG_10_OGU_READ_COUNT_KEY] *
             sample_linregress_slope +
             sample_linregress_intercept)
 
diff --git a/pysyndna/src/fit_syndna_models.py b/pysyndna/src/fit_syndna_models.py
index 22aab11..22bc290 100644
--- a/pysyndna/src/fit_syndna_models.py
+++ b/pysyndna/src/fit_syndna_models.py
@@ -22,8 +22,7 @@
 SYNDNA_POOL_MASS_NG_KEY = 'mass_syndna_input_ng'
 SAMPLE_TOTAL_READS_KEY = 'raw_reads_r1r2'
 SYNDNA_COUNTS_KEY = 'read_count'
-COUNTS_PER_MIL_KEY = 'CPM'
-LOG10_COUNTS_PER_MIL_KEY = 'log10_CPM'
+LOG10_SYNDNA_COUNTS_KEY = 'log10_read_count'
 SYNDNA_INDIV_NG_KEY = 'syndna_ng'
 LOG10_SYNDNA_INDIV_NG_KEY = 'log10_syndna_ng'
 LIN_REGRESS_RESULT_KEY = 'lin_regress_by_sample_id'
@@ -206,15 +205,32 @@ def _fit_linear_regression_models(working_df: pd.DataFrame) -> \
 
     This function fits a linear regression model for each sample,
     predicting log10(mass of instances of a sequence) within a sample
-    from log10(counts per million for that sequence) within the sample,
+    from log10(read counts for that sequence) within the sample,
     using spike-in data from synDNAs.
 
+    Note that this function originally followed the R notebooks for Zaramela
+    et al. and fit the log10 of the mass of the syndna in the sample to the
+    log10 of the counts per million of the read for that syndna in the sample.
+    However, later work by Oriane Moyne left out the CPMs and demonstrated
+    that one can achieve the same end result (of OGU mass predictions) by
+    fitting the log10 mass of the syndna in the sample to log10 of the read
+    counts themselves.  (The slope of the fits are the same in both cases,
+    while the intercepts differ by a constant factor because the CPM
+    calculation modifies the read count by a constant factor--dividing by
+    total reads in the sample and multiplying by a million.)  When one uses
+    the resulting fits on the raw read counts for OGUs, one gets the same
+    mass predictions as when using the fits on the CPMs on the CPMs for the
+    OGUs. (HOWEVER, since I know it will come up: note that this is not true if
+    one looks at the mass predictions from the original Zaramela R notebooks,
+    which unintentionally used a different total counts value for the CPM
+    calculation in the fit than they used for the CPM calculation in the
+    OGU mass prediction, leading to inconsistent results.)
+
     Parameters
     ----------
     working_df: pd.DataFrame
         Long-form dataframe containing at least SAMPLE_ID_KEY,
-        SYNDNA_COUNTS_KEY, SAMPLE_TOTAL_READS_KEY, and
-        SYNDNA_INDIV_NG_KEY columns.
+        SYNDNA_COUNTS_KEY, and SYNDNA_INDIV_NG_KEY columns.
 
     Returns
     -------
@@ -230,23 +246,16 @@ def _fit_linear_regression_models(working_df: pd.DataFrame) -> \
     # drop any rows where the count value is 0--can't take log of 0
     working_df = working_df[working_df[SYNDNA_COUNTS_KEY] > 0].copy()
 
-    # add a column of counts per million (CPM) by dividing the count value
-    # in each read_count by the total number of reads for its sample_id and
-    # then multiplying by a million (1,000,000)
-    working_df.loc[:, COUNTS_PER_MIL_KEY] = \
-        (working_df[SYNDNA_COUNTS_KEY] /
-         working_df[SAMPLE_TOTAL_READS_KEY]) * 1000000
-
-    # add a column of log10(CMP) by taking the log base 10 of the CPM column
-    working_df.loc[:, LOG10_COUNTS_PER_MIL_KEY] = \
-        np.log10(working_df[COUNTS_PER_MIL_KEY])
+    # add a column for the log10 of the syndna read count column
+    working_df.loc[:, LOG10_SYNDNA_COUNTS_KEY] = \
+        np.log10(working_df[SYNDNA_COUNTS_KEY])
 
     # add a column for the log10 of the syndna ng column
     working_df.loc[:, LOG10_SYNDNA_INDIV_NG_KEY] = \
         np.log10(working_df[SYNDNA_INDIV_NG_KEY])
 
     # loop over each sample id and fit a linear regression model predicting
-    # log10(dna ng) from log10(counts per million)
+    # log10(dna ng) from log10(syndna read counts)
     linregress_by_sample_id = {}
     log_msgs_list = []
     for curr_sample_id in working_df[SAMPLE_ID_KEY].unique():
@@ -255,7 +264,7 @@ def _fit_linear_regression_models(working_df: pd.DataFrame) -> \
 
         try:
             curr_linregress_result = scipy.stats.linregress(
-                curr_sample_df[LOG10_COUNTS_PER_MIL_KEY],
+                curr_sample_df[LOG10_SYNDNA_COUNTS_KEY],
                 curr_sample_df[LOG10_SYNDNA_INDIV_NG_KEY])
         except Exception:
             # TODO: I need to know what kind of errors this can throw;
diff --git a/pysyndna/tests/data/modelling_input.tsv b/pysyndna/tests/data/modelling_input.tsv
index 96b5318..a683644 100644
--- a/pysyndna/tests/data/modelling_input.tsv
+++ b/pysyndna/tests/data/modelling_input.tsv
@@ -1,376 +1,16 @@
-# The data in this file is based on the contents of the filled synDNAMetaAgM object
-# in its state immediately before cell [37] in the notebook at 
-# https://github.com/lzaramela/SynDNA/blob/main/SynDNA_saliva_linear_models.ipynb .
-# The following columns have been renamed to match the column name expectations of
-# the code under test:
-# ID -> sample_name
-# Counts -> read_count
-# TotalReads -> raw_reads_r1r2
-# SynDNA -> syndna_id
-# Additionally, the syndna_ng has been added, with values inferred from the Dilution2 
-# column.  The regression models in the notebook are trained to predict Dilution2, and 
-# in https://github.com/lzaramela/SynDNA/blob/main/SynDNA_saliva_samples_analysis.ipynb
-# it is stated that read weight = 10^(-predicted_value) 
-# so the syndna_ng = 10^(-1*Dilution2)
-# All other columns were deleted.
-sample_name	read_count	raw_reads_r1r2	syndna_id	syndna_ng
-A1_pool1_Fwd	93135	3216923	p126	0.1
-A1_pool1_Rev	90897	3216923	p126	0.1
-C1_pool1_Fwd	56878	1723417	p126	0.1
-C1_pool1_Rev	56064	1723417	p126	0.1
-D1_pool1_Fwd	49065	2606004	p126	0.1
-D1_pool1_Rev	47258	2606004	p126	0.1
-E1_pool1_Fwd	25180	4755108	p126	0.1
-E1_pool1_Rev	44711	4755108	p126	0.1
-F1_pool1_Fwd	29056	4399124	p126	0.1
-F1_pool1_Rev	89709	4399124	p126	0.1
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-H1_pool2_Rev	12	2342284	p266	0.00001
-A1_pool3_Fwd	1117	2586532	p266	0.001
-A1_pool3_Rev	1093	2586532	p266	0.001
-C1_pool3_Fwd	1450	2782849	p266	0.001
-C1_pool3_Rev	1432	2782849	p266	0.001
-D1_pool3_Fwd	1063	4195427	p266	0.001
-D1_pool3_Rev	1140	4195427	p266	0.001
-E1_pool3_Fwd	884	5384906	p266	0.001
-E1_pool3_Rev	963	5384906	p266	0.001
-F1_pool3_Fwd	762	2202357	p266	0.001
-F1_pool3_Rev	807	2202357	p266	0.001
-H1_pool3_Fwd	927	2261101	p266	0.001
-H1_pool3_Rev	921	2261101	p266	0.001
\ No newline at end of file
+# The data in this file is based on the full data in the
+# "zaramela linear reg CPM&counts" sheet of the "absolute_quant_example.xlsx"
+# file, after removing columns not directly needed for the linear regression.
+# Note that the data in that spreadsheet itself comes from the Zaramela et al.
+# R notebooks; see that file for precise details.
+sample_name	read_count	syndna_id	syndna_ng
+A	93135	p126	0.099999995
+A	15190	p136	0.01
+A	2447	p146	0.001
+A	308	p156	1E-04
+A	77	p166	1E-05
+A	149	p226	1E-05
+A	1075	p236	1E-04
+A	3189	p246	0.001
+A	25347	p256	0.01
+A	237329	p266	0.099999995
\ No newline at end of file
diff --git a/pysyndna/tests/data/modelling_output.tsv b/pysyndna/tests/data/modelling_output.tsv
index 8687675..37d7391 100644
--- a/pysyndna/tests/data/modelling_output.tsv
+++ b/pysyndna/tests/data/modelling_output.tsv
@@ -1,44 +1,5 @@
-# This file is based on 
-# https://github.com/lzaramela/SynDNA/blob/main/data/saliva_linear_models.tsv
-# with the values of the a_intercept and b_intercept columns negated 
-# (because the Zaramela code generates regression models that predict the
-# *negative* log10 of the read weight while the code under test predicts just
-# log10 of the read weight.)
-# All other columns were deleted.
+# The fit in this file is the
+# "sample A regression of log10_syndna_ng from log10_raw_counts" fit in the
+# "zaramela linear reg CPM&counts" sheet of the "absolute_quant_example.xlsx".
 ID	a_intercept	b_slope
-A1_pool1_Fwd	-6.775395054	1.244876524
-A1_pool1_Rev	-6.771631397	1.246759136
-C1_pool1_Fwd	-6.878100856	1.241529167
-C1_pool1_Rev	-6.848393273	1.236655876
-D1_pool1_Fwd	-6.642112139	1.250771588
-D1_pool1_Rev	-6.575257456	1.236368669
-E1_pool1_Fwd	-6.209075909	1.267741008
-E1_pool1_Rev	-6.34471994	1.274278208
-F1_pool1_Fwd	-6.38086082	1.244062976
-F1_pool1_Rev	-6.605655262	1.239447269
-H1_pool1_Fwd	-6.440192081	1.205881471
-H1_pool1_Rev	-6.588296111	1.212090029
-A1_pool2_Fwd	-6.041983871	1.061187469
-A1_pool2_Rev	-6.02539348	1.058027999
-C1_pool2_Fwd	-6.056199167	1.060877447
-C1_pool2_Rev	-6.069492391	1.065961909
-D1_pool2_Fwd	-5.938894948	1.083900408
-D1_pool2_Rev	-5.944595969	1.085696514
-E1_pool2_Fwd	-5.303220104	1.027856635
-E1_pool2_Rev	-5.530968001	1.070901249
-F1_pool2_Fwd	-5.727181515	1.034888555
-F1_pool2_Rev	-5.901821005	1.058843954
-H1_pool2_Fwd	-5.674505663	0.998484653
-H1_pool2_Rev	-5.648429195	0.993954412
-A1_pool3_Fwd	-5.76337605	1.00578283
-A1_pool3_Rev	-5.793669519	1.015138091
-C1_pool3_Fwd	-5.133881506	0.828831285
-C1_pool3_Rev	-5.117345873	0.828171831
-D1_pool3_Fwd	-5.343334407	1.024461683
-D1_pool3_Rev	-5.545901564	1.052359855
-E1_pool3_Fwd	-5.316389176	1.081948957
-E1_pool3_Rev	-5.440241897	1.080885471
-F1_pool3_Fwd	-5.665952742	1.067055184
-F1_pool3_Rev	-5.740173228	1.063047339
-H1_pool3_Fwd	-4.930454427	0.787220436
-H1_pool3_Rev	-4.900925447	0.781458157
\ No newline at end of file
+A	-7.40709604550579	1.244876524
\ No newline at end of file
diff --git a/pysyndna/tests/data/models.yml b/pysyndna/tests/data/models.yml
index 97929b1..b2cafe6 100644
--- a/pysyndna/tests/data/models.yml
+++ b/pysyndna/tests/data/models.yml
@@ -1,22 +1,22 @@
 "example1":
   "slope": 1.24487652379132
-  "intercept": -6.77539505390338
-  "rvalue": 0.9865030975156575
-  "pvalue": 1.428443560659758e-07
-  "stderr": 0.07305408550335003
-  "intercept_stderr": 0.2361976278251443
+  "intercept": -7.35593916054843
+  "rvalue": 0.986503097515657
+  "pvalue": 1.42844356065977E-07
+  "stderr": 0.0730540855033502
+  "intercept_stderr": 0.271274537363401
 "example2":
   "slope": 1.24675913604407
-  "intercept": -7.155318973708384
-  "rvalue": 0.9863241797356326
-  "pvalue": 1.505381146809759e-07
-  "stderr": 0.07365795255302438
-  "intercept_stderr": 0.2563956755844754
+  "intercept": -7.45004083037736
+  "rvalue": 0.986324179735633
+  "pvalue": 1.5053811468097E-07
+  "stderr": 0.073657952553024
+  "intercept_stderr": 0.2729411999326
 # example4 is a copy of example2
 "example4":
   "slope": 1.24675913604407
-  "intercept": -7.155318973708384
-  "rvalue": 0.9863241797356326
-  "pvalue": 1.505381146809759e-07
-  "stderr": 0.07365795255302438
-  "intercept_stderr": 0.2563956755844754
\ No newline at end of file
+  "intercept": -7.45004083037736
+  "rvalue": 0.986324179735633
+  "pvalue": 1.5053811468097E-07
+  "stderr": 0.073657952553024
+  "intercept_stderr": 0.2729411999326
\ No newline at end of file
diff --git a/pysyndna/tests/test_calc_cell_counts.py b/pysyndna/tests/test_calc_cell_counts.py
index 48a3864..6aaeb44 100644
--- a/pysyndna/tests/test_calc_cell_counts.py
+++ b/pysyndna/tests/test_calc_cell_counts.py
@@ -36,26 +36,28 @@ class TestCalcCellCountsData:
     # and a lower gdna density value (which leadds to a smaller amount of
     # syndna pool and thus a bunch of interesting calculation knock-ons).
 
-    # The example1 linear model is what we get from running Zaramela's
-    # "A1_pool1_Fwd" sample data through the linear modelling code (see
-    # modelling_input.tsv and test_fit_syndna_models.py.) The slope and
-    # intercept (all that is provided by Zaramela) match those from that
-    # work (see modelling_output.tsv for details).
-    # The example2 linear model is what we get from running the linear
-    # modelling code on the "Sample B" data from test_fit_syndna_models.py;
-    # it does not match any Zaramela results because Sample B is made up.
-    # See FitSyndnaModelsTest.lingress_results comments for details.
+    # The example1 (Sample A) and example2 (Sample B) values are taken from
+    # FitSyndnaModelsTest.lingress_results, which gets them from those
+    # calculated in Excel (see results for full data
+    # on "linear regressions counts" sheet of "absolute_quant_example.xlsx").
+    # Note that these do not and *should* NOT be expected to match any results
+    # in Zaramela's linear models; see FitSyndnaModelsTest.lingress_results
+    # comments for details.
     linregresses_dict = {
         'example1': {
-            "slope": 1.24487652379132, "intercept": -6.77539505390338,
-            "rvalue": 0.9865030975156575, "pvalue": 1.428443560659758e-07,
-            "stderr": 0.07305408550335003,
-            "intercept_stderr": 0.2361976278251443},
+            "slope": 1.24487652379132,
+            "intercept": -7.35593916054843,
+            "rvalue": 0.986503097515657,
+            "pvalue": 1.42844356065977E-07,
+            "stderr": 0.0730540855033502,
+            "intercept_stderr": 0.271274537363401},
         'example2': {
-            "slope": 1.24675913604407, "intercept": -7.155318973708384,
-            "rvalue": 0.9863241797356326, "pvalue": 1.505381146809759e-07,
-            "stderr": 0.07365795255302438,
-            "intercept_stderr": 0.2563956755844754}
+            "slope": 1.24675913604407,
+            "intercept": -7.45004083037736,
+            "rvalue": 0.986324179735633,
+            "pvalue": 1.5053811468097E-07,
+            "stderr": 0.073657952553024,
+            "intercept_stderr": 0.2729411999326}
     }
 
     # Values from "absolute_quant_example.xlsx"
@@ -73,7 +75,7 @@ class TestCalcCellCountsData:
     # the OGU_READ_COUNT_KEY values for each sample
     mass_and_totals_dict = {
         SAMPLE_ID_KEY: ["example1", "example2"],
-        SAMPLE_TOTAL_READS_KEY: [472140, 611913],
+        SAMPLE_TOTAL_READS_KEY: [3216923, 611913],
         SEQUENCED_SAMPLE_GDNA_MASS_NG_KEY: [5, 4.76],
         GDNA_MASS_TO_SAMPLE_MASS_RATIO_KEY: [7.1867431342E-06,
                                              4.7470988923E-06]
@@ -120,8 +122,8 @@ class TestCalcCellCountsData:
         # 26130 counts at position 8 (for N. subflava, bc example 2 has
         # zero counts here), by a factor of 10 at position 10
         # (for F. periodonticum, bc example 2 has 10x the counts of example 1
-        # at this position), and by 12133 at position 12 (for H. influenzae,
-        # bc example 2 has 12 counts here instead of 12145).
+        # at this position), and by 12045 at position 12 (for H. influenzae,
+        # bc example 2 has 100 counts here instead of 12145).
         # All other positions are identical.
         OGU_READ_COUNT_KEY: [79951, 93024, 86188, 45441, 31185, 24929,
                              1975, 0, 22303, 197830, 14478, 100, 14609]
@@ -154,8 +156,9 @@ class TestCalcCellCountsData:
                                0.06528070535624650000],
         # for the arrays below, the 1st, 2nd, and 7th values (those for
         # L. gasseri, R. albus, and L. valderiana) match those worked out in
-        # detail for example1 in the "absolute_quant_example.xlsx" spreadsheet
-        # (in the section using the truncated Avogadro's #, as Zaramela did).
+        # detail for example 1 on the "full_calcs on correct masses" sheet in
+        # the "absolute_quant_example.xlsx" spreadsheet (in the section using
+        # the truncated Avogadro's #, as Zaramela did).
         # The remainder were not worked out individually and come from the code
         # calculations.
         OGU_GENOMES_PER_G_OF_GDNA_KEY: [5.26939603e+13, 2.77101748e+13,
@@ -187,11 +190,29 @@ class TestCalcCellCountsData:
                                         41071667.042116195]
     })
 
-    # This dict contains the results of calculations done on example1
-    # using the full Avogadro's number (6.022e23) instead of the truncated one.
+    # This dict contains the gdna mass from the
+    # "PredictedOguMass" column of the
+    # "Mass results for sample A regression for full data on log10_read_count"
+    # table on the "linear regressions counts" sheet of
+    # "absolute_quant_example.xlsx", and
+    # the results of calculations done on example 1
+    # using the full Avogadro's number instead of the truncated one.
     example1_ogu_full_outputs_full_avogadro_dict = (
         example1_ogu_full_inputs_dict.copy())
     example1_ogu_full_outputs_full_avogadro_dict.update({
+        OGU_GDNA_MASS_NG_KEY: [0.055906776,
+                               0.067506894,
+                               0.061387889,
+                               0.027669949,
+                               0.01731683,
+                               0.01310435,
+                               0.000558001,
+                               0.013894854,
+                               0.011408701,
+                               0.009826844,
+                               0.006662378,
+                               0.005353421,
+                               0.006737505],
         # Note that this is assuming that examples 1 and 2 were run
         TOTAL_OGU_READS_KEY: [x + y for x, y in zip(
             example1_ogu_full_inputs_dict[OGU_READ_COUNT_KEY],
@@ -212,35 +233,64 @@ class TestCalcCellCountsData:
                                    103.533221485547],
         # for the arrays below, the 1st, 2nd, and 7th values (those for
         # L. gasseri, R. albus, and L. valderiana) match those worked out in
-        # detail for example1 in the "absolute_quant_example.xlsx" spreadsheet
-        # (in the section using the FULL Avogadro's #, not matching Zaramela).
+        # detail for example1 in the "full_calcs on correct masses" sheet of
+        # the "absolute_quant_example.xlsx" spreadsheet
+        # (in the section using the FULL Avogadro's #, NOT matching Zaramela).
         # The remainder were not worked out individually and come from the code
         # calculations.
-        OGU_GENOMES_PER_G_OF_GDNA_KEY: [
-            52695192015949.67, 27710822536547.69, 21897704979729.094,
-            12866488251594.062, 12674159207435.06, 11582576292095.531,
-            11223075218306.252, 10879422748260.775, 9289882608698.639,
-            7100063146106.998, 5809957491032.718, 5718752608946.0205,
-            5715054273735.247],
-        OGU_CELLS_PER_G_OF_GDNA_KEY: [
-            52695192015949.67, 27710822536547.69, 21897704979729.094,
-            12866488251594.062, 12674159207435.06, 11582576292095.531,
-            11223075218306.252, 10879422748260.775, 9289882608698.639,
-            7100063146106.998, 5809957491032.718, 5718752608946.0205,
-            5715054273735.247],
-        OGU_CELLS_PER_G_OF_SAMPLE_KEY: [
-            378706809.42597693, 199150563.60756874, 157373180.91780522,
-            92468146.10340859, 91085926.66579163, 83241000.64356525,
-            80657358.76977262, 78187616.74012242, 66764000.05558892,
-            51026330.06767092, 41754672.108673245, 41099206.04853115,
-            41072627.06334715]
+        OGU_GENOMES_PER_G_OF_GDNA_KEY: [5438576851832.26,
+                                        2859984606316.35,
+                                        2260023103719.98,
+                                        1327927321117.10,
+                                        1308077383248.35,
+                                        1195417056032.46,
+                                        1158313590928.33,
+                                        1122845831970.39,
+                                        958792227127.95,
+                                        732784863204.92,
+                                        599635357838.64,
+                                        590222264508.60,
+                                        589840565922.86],
+        OGU_CELLS_PER_G_OF_GDNA_KEY: [5438576851832.26,
+                                      2859984606316.35,
+                                      2260023103719.98,
+                                      1327927321117.10,
+                                      1308077383248.35,
+                                      1195417056032.46,
+                                      1158313590928.33,
+                                      1122845831970.39,
+                                      958792227127.95,
+                                      732784863204.92,
+                                      599635357838.64,
+                                      590222264508.60,
+                                      589840565922.86],
+        OGU_CELLS_PER_G_OF_SAMPLE_KEY: [39085654.85,
+                                        20553974.73,
+                                        16242205.52,
+                                        9543472.56,
+                                        9400816.15,
+                                        8591155.32,
+                                        8324502.25,
+                                        8069604.57,
+                                        6890593.46,
+                                        5266336.58,
+                                        4309425.29,
+                                        4241775.81,
+                                        4239032.64]
     })
 
+    # NB: the reason there is no "example1_ogu_filtered_<etc>" is that the
+    # filtering threshold used in the test does not drop any samples from
+    # example1, so the filtered example1 data is the same as the full.
+
+    # NB: the reason there is no "example2_ogu_full_<etc>" is that
+    # example 2 isn't being used to test generating an unfiltered dataset;
+    # all those tests are being done with example 1.
+
     # This dict contains the results of calculations done on *filtered*
-    # example2 data using the full Avogadro's number (6.022e23) instead of
-    # the truncated one.
-    # The ogu id, ogu length, and ogu read counts are exactly the same as in
-    # the example 2 full data *except* that Neisseria subflava and
+    # example2 data using the full Avogadro's number instead of the truncated
+    # one. The ogu id, ogu length, and ogu read counts are exactly the same as
+    # in the example 2 full data *except* that Neisseria subflava and
     # Haemophilus influenzae have been removed (for falling below the
     # min_coverage = 1 threshold, which is the default min_coverage value in
     # our analysis system).
@@ -289,21 +339,46 @@ class TestCalcCellCountsData:
                                    151.731341482939, 1194.203338,
                                    105.484891342717,
                                    103.533221485547],
-        OGU_GENOMES_PER_G_OF_GDNA_KEY: [
-            17086455403978.045, 8987677125515.266, 7101240813289.261,
-            4167468287030.2075, 4102264162505.8833, 3747369928484.789,
-            3613767901730.258, 3004973286163.8184, 40527863244164.32,
-            1877803149989.8623, 1847161346896.6194],
-        OGU_CELLS_PER_G_OF_GDNA_KEY: [
-            17086455403978.045, 8987677125515.266, 7101240813289.261,
-            4167468287030.2075, 4102264162505.8833, 3747369928484.789,
-            3613767901730.258, 3004973286163.8184, 40527863244164.32,
-            1877803149989.8623, 1847161346896.6194],
-        OGU_CELLS_PER_G_OF_SAMPLE_KEY: [
-            81111093.52155752, 42665392.12688357, 33710292.398721,
-            19783384.089056477, 19473853.661753666, 17789135.636548474,
-            17154913.603333004, 14264905.358139353, 192389774.71365833,
-            8914117.253274327, 8768657.583752317]
+        # for the arrays below, the 1st, 2nd, and 7th values (those for
+        # L. gasseri, R. albus, and L. valderiana) match those worked out in
+        # detail for example 2 in the "full_calcs on correct masses" sheet of
+        # the "absolute_quant_example.xlsx" spreadsheet
+        # (in the section using the FULL Avogadro's #, NOT matching Zaramela).
+        # The remainder were not worked out individually and come from the code
+        # calculations.
+        OGU_GENOMES_PER_G_OF_GDNA_KEY: [4.698763e+12,
+                                        2.471605e+12,
+                                        1.952836e+12,
+                                        1.146051e+12,
+                                        1.128120e+12,
+                                        1.030524e+12,
+                                        9.937836e+11,
+                                        8.263655e+11,
+                                        1.114513e+13,
+                                        5.163945e+11,
+                                        5.079680e+11],
+        OGU_CELLS_PER_G_OF_GDNA_KEY: [4.698763e+12,
+                                      2.471605e+12,
+                                      1.952836e+12,
+                                      1.146051e+12,
+                                      1.128120e+12,
+                                      1.030524e+12,
+                                      9.937836e+11,
+                                      8.263655e+11,
+                                      1.114513e+13,
+                                      5.163945e+11,
+                                      5.079680e+11],
+        OGU_CELLS_PER_G_OF_SAMPLE_KEY: [2.230549e+07,
+                                        1.173295e+07,
+                                        9.270306e+06,
+                                        5.440416e+06,
+                                        5.355296e+06,
+                                        4.891999e+06,
+                                        4.717589e+06,
+                                        3.922839e+06,
+                                        5.290705e+07,
+                                        2.451376e+06,
+                                        2.411375e+06]
     }
 
     # This dict contains the results of calculations done on example1 and
@@ -330,21 +405,51 @@ class TestCalcCellCountsData:
         # The two 0 values are for N. subflava and H. influenzae, which were
         # removed from example2 data due to low coverage.
         OGU_CELLS_PER_G_OF_GDNA_KEY: [
-            [21897704979729.094, 7101240813289.261],
-            [7100063146106.998, 40527863244164.32],
-            [5718752608946.0205, 0],
-            [52695192015949.67, 17086455403978.045],
-            [11223075218306.252, 3613767901730.258],
-            [9289882608698.639, 3004973286163.8184],
-            [10879422748260.775, 0],
-            [12674159207435.06, 4102264162505.8833],
-            [27710822536547.69, 8987677125515.266],
-            [11582576292095.531, 3747369928484.789],
-            [5809957491032.718, 1877803149989.8623],
-            [12866488251594.062, 4167468287030.2075],
-            [5715054273735.247, 1847161346896.6194]],
+            [2260023103719.98, 1952836093054.1],
+            [732784863204.92, 11145133111026.7],
+            [590222264508.6, 0],
+            [5438576851832.26, 4698762891240.72],
+            [1158313590928.33, 993783562051.94],
+            [958792227127.95, 826365482621.33],
+            [1122845831970.39, 0],
+            [1308077383248.35, 1128119680829.86],
+            [2859984606316.35, 2471604715978.24],
+            [1195417056032.46, 1030524022882.84],
+            [599635357838.64, 516394509546.61],
+            [1327927321117.1, 1146050767964.49],
+            [589840565922.86, 507968035834.47]
+        ]
     }
 
+    # NB: The test values for example1 here are *slightly* different than
+    # those in self.example1_ogu_full_outputs_full_avogadro_dict because
+    # the gdna-to-sample mass ratio calculated internally during this
+    # soup-to-nuts function has more digits past the decimal than does the
+    # example1 entry in the manually-populated self.mass_and_totals_dict.
+    # Since we are multiplying/dividing by large numbers like e.g., 10^9
+    # (to change ng to g), this ends up making a slight difference in the
+    # end product: for example, for L.gasseri,
+    # 3908565*5.46* cells instead of 3908565*4.85* cells,
+    # 8324502.*38* instead of 8324502.*25* for L. valderiana,
+    # and 2055397*5.06* instead of 2055397*4.73* for R. albus.
+    # Remember, with reordering, the 4th sub-array is for L. gasseri,
+    # the 5th is for L. valderiana, and the 9th is for R. albus.
+    example1_example4_cells_per_g_sample = [
+            [16242205.78, 6489214.14],
+            [5266336.67, 37034933.76],
+            [4241775.87, 0],
+            [39085655.46, 15613844.24],
+            [8324502.38, 3302312.14],
+            [6890593.56, 2745987.02],
+            [8069604.7, 0],
+            [9400816.3, 3748706.92],
+            [20553975.06, 8213066.28],
+            [8591155.45, 3424399.56],
+            [4309425.36, 1715963.04],
+            [9543472.71, 3808291.37],
+            [4239032.7, 1687962.12]
+    ]
+
     @classmethod
     def combine_inputs(cls):
         sample_names = cls.generate_sample_names_list(use_filtered_ex2=False)
@@ -362,6 +467,8 @@ def combine_inputs(cls):
 
     @classmethod
     def combine_filtered_out(cls, col_name):
+        # NB: it *is* correct to use the "full" example1 and the "filtered"
+        # example2; see comments above in the property definitions for details.
         example1_copy = (
             TestCalcCellCountsData.example1_ogu_full_outputs_full_avogadro_dict[col_name].copy())
         example2_copy = (
@@ -427,39 +534,8 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita(self):
         # example4 has the same counts as example2
         counts_vals = TestCalcCellCountsData.make_combined_counts_np_array()
 
-        # NB: The test values for example1 here are *slightly* different than
-        # those in self.example1_ogu_full_outputs_full_avogadro_dict because
-        # the gdna-to-sample mass ratio calculated internally during this
-        # soup-to-nuts function has more digits past the decimal than does the
-        # example1 entry in the manually-populated self.mass_and_totals_dict.
-        # Since we are multiplying/dividing by large numbers like e.g., 10^9
-        # (to change ng to g), this ends up making a slight difference in the
-        # end product: for example, for L.gasseri,
-        # 3787068*15* cells instead of 3787068*09* cells,
-        # 80657360 instead of 80657358 for L. valderiana,
-        # and 199150566 instead of 199150563 for R. albus.
-        # The values for example 4 are about an order of magnitude smaller
-        # than those for example 1 and thus match those from
-        # "absolute_quant_example.xlsx" more closely:
-        # both 56777765 for L. gasseri (with rounding),
-        # 12008439 instead of 12008440 for L. valderiana,
-        # and both 29865774 for R. albus.
-        # Remember, with reordering, the 4th sub-array is for L. gasseri,
-        # the 5th is for L. valderiana, and the 9th is for R. albus.
-        ogu_cell_counts_per_g_sample = np.array([
-            [157373183.3914873, 23597204.3149076],
-            [51026330.8697321, 134672840.2210325],
-            [41099206.6945521, 0],
-            [378706815.3787082, 56777764.5887874],
-            [80657360.0375914, 12008439.3369959],
-            [66764001.1050239, 9985433.5965833],
-            [78187617.9691203, 0],
-            [91085928.0975326, 13631697.3528372],
-            [199150566.7379318, 29865774.0278729],
-            [83241001.9519951, 12452394.7533948],
-            [41754672.7649972, 6239881.9809863],
-            [92468147.5568761, 13848368.6486051],
-            [41072627.7089503, 6138060.2138924]])
+        ogu_cell_counts_per_g_sample = np.array(
+            TestCalcCellCountsData.example1_example4_cells_per_g_sample)
 
         sample_info_df = pd.DataFrame(sample_info_dict)
         prep_info_df = pd.DataFrame(prep_info_dict)
@@ -490,7 +566,8 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita(self):
 
         a_tester = Testers()
         a_tester.assert_biom_tables_equal(
-            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY])
+            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY],
+            decimal_precision=1)
         self.assertEqual(
             "The following items have % coverage lower than the minimum of "
             "1.0: ['example4;Neisseria subflava', "
@@ -524,39 +601,8 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita_w_casts(self):
         # example4 has the same counts as example2
         counts_vals = TestCalcCellCountsData.make_combined_counts_np_array()
 
-        # NB: The test values for example1 here are *slightly* different than
-        # those in self.example1_ogu_full_outputs_full_avogadro_dict because
-        # the gdna-to-sample mass ratio calculated internally during this
-        # soup-to-nuts function has more digits past the decimal than does the
-        # example1 entry in the manually-populated self.mass_and_totals_dict.
-        # Since we are multiplying/dividing by large numbers like e.g., 10^9
-        # (to change ng to g), this ends up making a slight difference in the
-        # end product: for example, for L.gasseri,
-        # 3787068*15* cells instead of 3787068*09* cells,
-        # 80657360 instead of 80657358 for L. valderiana,
-        # and 199150566 instead of 199150563 for R. albus.
-        # The values for example 4 are about an order of magnitude smaller
-        # than those for example 1 and thus match those from
-        # "absolute_quant_example.xlsx" more closely:
-        # both 56777765 for L. gasseri (with rounding),
-        # 12008439 instead of 12008440 for L. valderiana,
-        # and both 29865774 for R. albus.
-        # Remember, with reordering, the 4th sub-array is for L. gasseri,
-        # the 5th is for L. valderiana, and the 9th is for R. albus.
-        ogu_cell_counts_per_g_sample = np.array([
-            [157373183.3914873, 23597204.3149076],
-            [51026330.8697321, 134672840.2210325],
-            [41099206.6945521, 0],
-            [378706815.3787082, 56777764.5887874],
-            [80657360.0375914, 12008439.3369959],
-            [66764001.1050239, 9985433.5965833],
-            [78187617.9691203, 0],
-            [91085928.0975326, 13631697.3528372],
-            [199150566.7379318, 29865774.0278729],
-            [83241001.9519951, 12452394.7533948],
-            [41754672.7649972, 6239881.9809863],
-            [92468147.5568761, 13848368.6486051],
-            [41072627.7089503, 6138060.2138924]])
+        ogu_cell_counts_per_g_sample = np.array(
+            TestCalcCellCountsData.example1_example4_cells_per_g_sample)
 
         sample_info_df = pd.DataFrame(sample_info_dict)
         prep_info_df = pd.DataFrame(prep_info_dict)
@@ -588,7 +634,8 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita_w_casts(self):
 
         a_tester = Testers()
         a_tester.assert_biom_tables_equal(
-            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY])
+            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY],
+            decimal_precision=1)
         self.assertEqual(
             "The following items have % coverage lower than the minimum of "
             "1.0: ['example4;Neisseria subflava', "
@@ -624,39 +671,12 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita_w_negs(self):
         # example4 has the same counts as example2
         counts_vals = TestCalcCellCountsData.make_combined_counts_np_array()
 
-        # NB: The test values for example1 here are *slightly* different than
-        # those in self.example1_ogu_full_outputs_full_avogadro_dict because
-        # the gdna-to-sample mass ratio calculated internally during this
-        # soup-to-nuts function has more digits past the decimal than does the
-        # example1 entry in the manually-populated self.mass_and_totals_dict.
-        # Since we are multiplying/dividing by large numbers like e.g., 10^9
-        # (to change ng to g), this ends up making a slight difference in the
-        # end product: for example, for L.gasseri,
-        # 3787068*15* cells instead of 3787068*09* cells,
-        # 80657360 instead of 80657358 for L. valderiana,
-        # and 199150566 instead of 199150563 for R. albus.
-        # The values for example 4 are about an order of magnitude smaller
-        # than those for example 1 and thus match those from
-        # "absolute_quant_example.xlsx" more closely:
-        # both 56777765 for L. gasseri (with rounding),
-        # 12008439 instead of 12008440 for L. valderiana,
-        # and both 29865774 for R. albus.
-        # Remember, with reordering, the 4th sub-array is for L. gasseri,
-        # the 5th is for L. valderiana, and the 9th is for R. albus.
-        ogu_cell_counts_per_g_sample = np.array([
-            [157373183.3914873],
-            [51026330.8697321],
-            [41099206.6945521],
-            [378706815.3787082],
-            [80657360.0375914],
-            [66764001.1050239],
-            [78187617.9691203],
-            [91085928.0975326],
-            [199150566.7379318],
-            [83241001.9519951],
-            [41754672.7649972],
-            [92468147.5568761],
-            [41072627.7089503]])
+        # Results are returned only for example 1 because example 4 has a
+        # negative aliquot mass
+        ogu_cell_counts_per_g_sample = np.array(
+            [[x[0]] for x in
+             TestCalcCellCountsData.example1_example4_cells_per_g_sample]
+        )
 
         sample_info_df = pd.DataFrame(sample_info_dict)
         prep_info_df = pd.DataFrame(prep_info_dict)
@@ -687,7 +707,8 @@ def test_calc_ogu_cell_counts_per_g_of_sample_for_qiita_w_negs(self):
 
         a_tester = Testers()
         a_tester.assert_biom_tables_equal(
-            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY])
+            expected_out_biom, output_dict[CELL_COUNT_RESULT_KEY],
+            decimal_precision=1)
         self.assertEqual(
             "Dropping samples with negative values in necessary "
             "prep/sample column(s): example4",
@@ -830,12 +851,12 @@ def test_calc_ogu_cell_counts_biom(self):
             params_df, TestCalcCellCountsData.linregresses_dict, counts_biom, lengths_df,
             read_len, min_coverage, min_rsquared, output_metric)
 
-        # NB: only checking results to 2 decimals because Ubuntu and Mac
+        # NB: only checking results to 1 decimal because Ubuntu and Mac
         # differ past that point. Not that it matters much since the decimal
         # portion of values this huge is not very important.
         a_tester = Testers()
         a_tester.assert_biom_tables_equal(expected_out_biom, output_biom,
-                                          decimal_precision=2)
+                                          decimal_precision=1)
         self.assertListEqual(
             ["The following items have % coverage lower than the minimum of "
              "1.0: ['example2;Neisseria subflava', "
@@ -947,12 +968,12 @@ def test_calc_ogu_cell_counts_biom_w_cast(self):
             params_df, TestCalcCellCountsData.linregresses_dict, counts_biom, lengths_df,
             read_len, min_coverage, min_rsquared, output_metric)
 
-        # NB: only checking results to 2 decimals because Ubuntu and Mac
+        # NB: only checking results to 1 decimal because Ubuntu and Mac
         # differ past that point. Not that it matters much since the decimal
         # portion of values this huge is not very important.
         a_tester = Testers()
         a_tester.assert_biom_tables_equal(expected_out_biom, output_biom,
-                                          decimal_precision=2)
+                                          decimal_precision=1)
         self.assertListEqual(
             ["The following items have % coverage lower than the minimum of "
              "1.0: ['example2;Neisseria subflava', "
@@ -969,16 +990,26 @@ def test__calc_long_format_ogu_cell_counts_df(self):
         }
 
         ogu_masses = \
-            TestCalcCellCountsData.example1_ogu_full_outputs_short_avogadro_dict[
+            TestCalcCellCountsData.example1_ogu_full_outputs_full_avogadro_dict[
                 OGU_GDNA_MASS_NG_KEY].copy()
-        # NB: The example2 test values were NOT verified manually.  They
-        # are what the code that produces correct results for all the
-        # values downstream of these values produces for these values.
-        ogu_masses.extend([
-            0.16721225613234225, 0.20196161687112832, 0.1836288899469535,
-            0.08266911949481899, 0.051700594315481685, 0.03910745610301345,
-            0.0016573186101851826, 0.03403997782058165, 0.517402166874799,
-            0.01986227733996378, 0.02008659206780049])
+        # NB: The example 2 gdna mass values come from the "PredictedOguMass"
+        # column of the "Mass results for sample B regression for full data on log10_read_count"
+        # table on the "linear regressions counts" sheet of
+        # "absolute_quant_example.xlsx", but with the 8th and 12th (1-based)
+        # values removed since this is for *filtered* example 2 data.
+        ogu_masses.extend([0.04598325,
+                           0.055539299,
+                           0.050497812,
+                           0.022733948,
+                           0.014217626,
+                           0.010754522,
+                           0.000455761,
+                           # NUM!
+                           0.009360969,
+                           0.142285222,
+                           0.005462112,
+                           # 1.10529E-05,
+                           0.005523798])
 
         # NB: this test is NOT using the truncated version of Avogadro's # that
         # was used in the notebook, so the results are slightly different
@@ -1175,7 +1206,7 @@ def test__calc_ogu_cell_counts_df_for_sample(self):
         per_sample_info_df = pd.DataFrame(TestCalcCellCountsData.mass_and_totals_dict)
 
         expected_additions_dict = {
-            k: TestCalcCellCountsData.example1_ogu_full_outputs_short_avogadro_dict[k] for k in
+            k: TestCalcCellCountsData.example1_ogu_full_outputs_full_avogadro_dict[k] for k in
             (OGU_ID_KEY, OGU_GDNA_MASS_NG_KEY,
              OGU_GENOMES_PER_G_OF_GDNA_KEY,
              OGU_CELLS_PER_G_OF_GDNA_KEY,
@@ -1190,7 +1221,7 @@ def test__calc_ogu_cell_counts_df_for_sample(self):
 
         output_df, output_msgs = _calc_ogu_cell_counts_df_for_sample(
             sample_id, TestCalcCellCountsData.linregresses_dict, per_sample_info_df, input_df,
-            min_rsquared, is_test=True)
+            min_rsquared, is_test=False)
 
         pd.testing.assert_frame_equal(expected_out_df, output_df)
         self.assertListEqual([], output_msgs)
@@ -1232,7 +1263,7 @@ def test__calc_ogu_cell_counts_df_for_sample_w_log_msgs_low_rsquared(self):
 
         self.assertIsNone(output_df)
         self.assertListEqual(['R^2 of linear regression for sample example1 '
-                              'is 0.9731883614079868, which is less than the '
+                              'is 0.9731883614079859, which is less than the '
                               'minimum allowed value of 0.99.'],
                              output_msgs)
 
@@ -1255,42 +1286,15 @@ def test__calc_gdna_mass_to_sample_mass_by_sample_df(self):
         output_series = _calc_gdna_mass_to_sample_mass_by_sample_df(inputs_df)
         pd.testing.assert_series_equal(expected_series, output_series)
 
-    def test__calc_ogu_gdna_mass_ng_series_for_sample(self):
-        input_dict = {k: TestCalcCellCountsData.example1_ogu_full_inputs_dict[k] for k in
-                      (OGU_ID_KEY, OGU_READ_COUNT_KEY)}
-
-        # Inputs are taken from the values for A1_pool1_Fwd in the
-        # linear models file at https://github.com/lzaramela/SynDNA/blob/main/data/saliva_linear_models.tsv ,
-        # EXCEPT the values of the a_intercept and b_intercept columns are
-        # negated (because the Zaramela code generates regression models that
-        # predict the *negative* log10 of the read weight while the code under
-        # test predicts just log10 of the read weight).
-        slope = 1.24487652379132
-        intercept = -6.77539505390338
-
-        # This number comes from summing all the reads in the input_df.
-        # This matches what was done for the Zaramela calculations.  I
-        # suspect that this should perhaps be the total reads for the
-        # whole sample, but for testing this will do.
-        sample_total_reads = 472140
-
-        input_df = pd.DataFrame(input_dict)
-        expected_series = pd.Series(
-            TestCalcCellCountsData.example1_ogu_full_outputs_short_avogadro_dict[
-                OGU_GDNA_MASS_NG_KEY],
-            index=TestCalcCellCountsData.example1_ogu_full_inputs_dict[OGU_ID_KEY],
-            name=OGU_GDNA_MASS_NG_KEY)
-        expected_series.index.name = OGU_ID_KEY
-
-        output_series = _calc_ogu_gdna_mass_ng_series_for_sample(
-            input_df, slope, intercept, sample_total_reads)
-
-        assert_series_equal(expected_series, output_series)
-
     def test__calc_ogu_genomes_per_g_of_gdna_series_for_sample(self):
         # this is the default value for our experimental system
         total_sample_gdna_mass_ng = 5
 
+        # NOTE: the input mass values here are the originals from the Zaramela
+        # R notebook, which had an issue in the calculation. HOWEVER,
+        # that doesn't matter here because they are inputs, not outputs: IF
+        # you input these values, you get these outputs (regardless of whether
+        # these input values are meaningful).
         input_dict = {k: TestCalcCellCountsData.example1_ogu_full_outputs_short_avogadro_dict[k]
                       for k in
                       (OGU_ID_KEY, OGU_LEN_IN_BP_KEY, OGU_GDNA_MASS_NG_KEY)}
@@ -1335,6 +1339,11 @@ def test__calc_ogu_genomes_series_for_sample(self):
                                     28574.60346]
         }
 
+        # NOTE: the input mass values here are the originals from the Zaramela
+        # R notebook, which had an issue in the calculation. HOWEVER,
+        # that doesn't matter here because they are inputs, not outputs: IF
+        # you input these values, you get these outputs (regardless of whether
+        # these input values are meaningful).
         input_dict = {k: TestCalcCellCountsData.example1_ogu_full_outputs_short_avogadro_dict[k]
                       for k in
                       (OGU_ID_KEY, OGU_LEN_IN_BP_KEY, OGU_GDNA_MASS_NG_KEY)}
@@ -1350,3 +1359,54 @@ def test__calc_ogu_genomes_series_for_sample(self):
             input_df, is_test=True)
 
         assert_series_equal(expected_series, output_series)
+
+    def test__calc_ogu_gdna_mass_ng_series_for_sample(self):
+        input_dict = \
+            {k: TestCalcCellCountsData.example1_ogu_full_inputs_dict[k]
+             for k in (OGU_ID_KEY, OGU_READ_COUNT_KEY)}
+
+        # The slope and intercept values are taken from
+        # those calculated in Excel (see
+        # "sample A regression of log10_syndna_ng from log10_raw_counts"
+        # for full data on "zaramela linear reg CPM&counts" sheet of
+        # "absolute_quant_example.xlsx").
+        # Note that the intercept does not and *should* NOT be expected to
+        # match the intercept in Zaramela's linear models because we are
+        # fitting log10(raw counts) not log10(CPM), but this comes
+        # out in the wash when one actually *uses* the models to predict mass
+        # (HOWEVER, this is not true for the masses taken directly from the
+        # Zaramela R notebook because it unintentionally used a different total
+        # read value when calculating the OGU CPM than when calculating
+        # the syndna CPM to get the fit.)
+        slope = 1.24487652379132
+        intercept = -7.40709604550579
+
+        # This list contains the gdna mass from the
+        # "Local mass using RawCounts (no TotalReads)" column in the
+        # "linear regressions counts" sheet of "absolute_quant_example.xlsx"
+        expected_ogu_masses = \
+            [0.04969441309413190000,
+             0.06000552485579740000,
+             0.05456646428684080000,
+             0.02459526358752070000,
+             0.01539258341742420000,
+             0.01164819449827620000,
+             0.00049599588089929300,
+             0.01235085741392710000,
+             0.01014096594158730000,
+             0.00873488502026095000,
+             0.00592205420878227000,
+             0.00475854893636541000,
+             0.00598883339989535000]
+
+        input_df = pd.DataFrame(input_dict)
+        expected_series = pd.Series(
+            expected_ogu_masses,
+            index=TestCalcCellCountsData.example1_ogu_full_inputs_dict[OGU_ID_KEY],
+            name=OGU_GDNA_MASS_NG_KEY)
+        expected_series.index.name = OGU_ID_KEY
+
+        output_series = _calc_ogu_gdna_mass_ng_series_for_sample(
+            input_df, slope, intercept)
+
+        assert_series_equal(expected_series, output_series)
\ No newline at end of file
diff --git a/pysyndna/tests/test_fit_syndna_models.py b/pysyndna/tests/test_fit_syndna_models.py
index 3f0aa85..96966e1 100644
--- a/pysyndna/tests/test_fit_syndna_models.py
+++ b/pysyndna/tests/test_fit_syndna_models.py
@@ -77,29 +77,37 @@ class FitSyndnaModelsTestData():
     # The slope, intercept, rvalue, stderr (of slope),
     # intercept_stderr, and p-value (of slope) values for these results
     # match those calculated in Excel (see results for full data
-    # on "linear regressions" sheet of "absolute_quant_example.xlsx").
+    # on "linear regressions counts" sheet of "absolute_quant_example.xlsx").
     # Note that these do not and *should* NOT be expected to match any results
-    # in Zaramela's linear models (see modelling_output.tsv) because
-    # (i) sample B is a chimera of realistic data from multiple Zaramela
-    # samples but isn't directly comparable to a single one of them, and
-    # (ii) sample A is directly comparable to Zaramela's sample
+    # in Zaramela's linear models because:
+    # (i) we are fitting log10(raw counts) not log10(CPM).  Note this comes out
+    # in the wash when one actually *uses* the models to predict mass:
+    # you get the same masses using the model fit to log10(raw counts)
+    # as you do using the model fit to log10(CPM) (which makes sense since all
+    # the CPM calculation does is divide the raw counts by a constant
+    # [total reads] and then multiply by another constant [a million]) but
+    # the models themselves are slightly different--same slope but different
+    # intercepts.
+    # (ii) sample A has the same counts as Zaramela's sample
     # "A1_pool1_Fwd" *but* we use a different pool mass than Zaramela,
     # so the same syndna counts are based on different masses.
+    # (iii) sample B is a chimera of realistic data from multiple Zaramela
+    # samples but isn't directly comparable to a single one of them
     lingress_results = {
         'A': {
-            "slope": 1.244876523791319,
-            "intercept": -6.7242381884894655,
-            "rvalue": 0.9865030975156575,
-            "pvalue": 1.428443560659758e-07,
-            "stderr": 0.07305408550335003,
-            "intercept_stderr": 0.2361976278251443},
+            "slope": 1.24487652379132,
+            "intercept": -7.35593916054843,
+            "rvalue": 0.986503097515657,
+            "pvalue": 1.42844356065977E-07,
+            "stderr": 0.0730540855033502,
+            "intercept_stderr": 0.271274537363401},
         'B': {
             "slope": 1.24675913604407,
-            "intercept": -7.155318973708384,
-            "rvalue": 0.9863241797356326,
-            "pvalue": 1.505381146809759e-07,
-            "stderr": 0.07365795255302438,
-            "intercept_stderr": 0.2563956755844754}
+            "intercept": -7.45004083037736,
+            "rvalue": 0.986324179735633,
+            "pvalue": 1.5053811468097E-07,
+            "stderr": 0.073657952553024,
+            "intercept_stderr": 0.2729411999326}
     }
 
     prep_info_dict = copy.deepcopy(
@@ -117,128 +125,6 @@ class FitSyndnaModelsTestData():
 
 
 class FitSyndnaModelsTest(TestCase):
-    # # concentrations taken from table in Fig. 1A of
-    # # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765022/
-    # syndna_concs_dict = {
-    #     SYNDNA_ID_KEY: ["p126", "p136", "p146", "p156", "p166", "p226",
-    #                     "p236", "p246", "p256", "p266"],
-    #     SYNDNA_INDIV_NG_UL_KEY: [1, 0.1, 0.01, 0.001, 0.0001, 0.0001,
-    #                              0.001, 0.01, 0.1, 1],
-    # }
-    #
-    # # made-up placeholders :)
-    # sample_ids = ["A", "B"]
-    #
-    # # Total reads come from the "TotalReads" column of
-    # # https://github.com/lzaramela/SynDNA/blob/main/data/synDNA_metadata_updated.tsv
-    # # for the record with "ID" = "A1_pool1_Fwd".
-    # # Syndna pool mass is the default value expected in our experimental
-    # # system.
-    # a_sample_syndna_weights_and_total_reads_dict = {
-    #     SAMPLE_ID_KEY: [sample_ids[0]],
-    #     SAMPLE_TOTAL_READS_KEY: [3216923],
-    #     SYNDNA_POOL_MASS_NG_KEY: [0.25],
-    # }
-    #
-    # # Total reads come from the "TotalReads" column of
-    # # https://github.com/lzaramela/SynDNA/blob/main/data/synDNA_metadata_updated.tsv
-    # # for the record with "ID" of "A1_pool1_Fwd" and "C1_pool1_Fwd".
-    # # Syndna pool masses are plausible values for our experimental system.
-    # a_b_sample_syndna_weights_and_total_reads_dict = {
-    #     SAMPLE_ID_KEY: sample_ids,
-    #     SAMPLE_TOTAL_READS_KEY: [3216923, 1723417],
-    #     SYNDNA_POOL_MASS_NG_KEY: [0.25, 0.2],
-    # }
-    #
-    # # Total reads come from the "TotalReads" column of
-    # # https://github.com/lzaramela/SynDNA/blob/main/data/synDNA_metadata_updated.tsv
-    # # for the record with "ID" of "A1_pool1_Fwd", "C1_pool1_Fwd", and
-    # # "D1_pool1_Fwd".
-    # # Syndna pool masses are plausible values for our experimental system.
-    # a_b_c_sample_syndna_weights_and_total_reads_dict = {
-    #     SAMPLE_ID_KEY: [sample_ids[0], sample_ids[1], "C"],
-    #     SAMPLE_TOTAL_READS_KEY: [3216923, 1723417, 2606004],
-    #     SYNDNA_POOL_MASS_NG_KEY: [0.25, 0.2, 0.3],
-    # }
-    #
-    # # The below sample values come from the
-    # # "A1_pool1_S21_L001_R1_001.fastq_output_forward_paired.fq.sam.bam.f13_r1.fq_synDNA"
-    # # and "A1_pool2_S22_L001_R1_001.fastq_output_forward_paired.fq.sam.bam.f13_r1.fq_synDNA"
-    # # columns of https://github.com/lzaramela/SynDNA/blob/main/data/synDNA_Fwd_Rev_sam.biom.tsv ,
-    # # while the syndna ids are inferred from the contents of the "OTUID"
-    # # column and a knowledge of the Zaramela naming scheme.
-    # reads_per_syndna_per_sample_dict = {
-    #     SYNDNA_ID_KEY: ["p126", "p136", "p146", "p156", "p166", "p226",
-    #                     "p236", "p246", "p256", "p266"],
-    #     sample_ids[0]: [93135, 15190, 2447, 308, 77, 149, 1075, 3189, 25347, 237329],
-    #     sample_ids[1]: [90897, 15002, 2421, 296, 77, 148, 1059, 3129, 24856, 230898],
-    # }
-    #
-    # # The slope, intercept, rvalue, stderr (of slope),
-    # # intercept_stderr, and p-value (of slope) values for these results
-    # # match those calculated in Excel (see results for full data
-    # # on "linear regressions" sheet of "absolute_quant_example.xlsx").
-    # # Note that these do not and *should* NOT be expected to match any results
-    # # in Zaramela's linear models (see modelling_output.tsv) because
-    # # (i) sample B is a chimera of realistic data from multiple Zaramela
-    # # samples but isn't directly comparable to a single one of them, and
-    # # (ii) sample A is directly comparable to Zaramela's sample
-    # # "A1_pool1_Fwd" *but* we use a different pool mass than Zaramela,
-    # # so the same syndna counts are based on different masses.
-    # lingress_results = {
-    #     'A': {
-    #         "slope": 1.244876523791319,
-    #         "intercept": -6.7242381884894655,
-    #         "rvalue": 0.9865030975156575,
-    #         "pvalue": 1.428443560659758e-07,
-    #         "stderr": 0.07305408550335003,
-    #         "intercept_stderr": 0.2361976278251443},
-    #     'B': {
-    #         "slope": 1.24675913604407,
-    #         "intercept": -7.155318973708384,
-    #         "rvalue": 0.9863241797356326,
-    #         "pvalue": 1.505381146809759e-07,
-    #         "stderr": 0.07365795255302438,
-    #         "intercept_stderr": 0.2563956755844754}
-    # }
-    #
-    # prep_info_dict = copy.deepcopy(
-    #     a_b_sample_syndna_weights_and_total_reads_dict)
-    # prep_info_dict["sequencing_type"] = ["shotgun", "shotgun"]
-    # prep_info_dict["syndna_pool_number"] = [1, 1]
-    #
-    # # combine each item in self.reads_per_syndna_per_sample_dict["A"] with
-    # # the analogous item in self.reads_per_syndna_per_sample_dict["B"]
-    # # to make an array of two-item arrays, and turn this into an np.array
-    # reads_per_syndna_per_sample_array = np.array(
-    #     [list(x) for x in zip(
-    #         reads_per_syndna_per_sample_dict["A"],
-    #         reads_per_syndna_per_sample_dict["B"])])
-
-    # def assert_dicts_almost_equal(self, d1, d2):
-    #     """Assert that two dicts are almost equal.
-    #
-    #     Parameters
-    #     ----------
-    #     d1 : dict
-    #         The first dict to compare
-    #     d2 : dict
-    #         The second dict to compare
-    #
-    #     Raises
-    #     ------
-    #     AssertionError
-    #         If the dicts are not almost equal
-    #     """
-    #     self.assertIsInstance(d1, dict)
-    #     self.assertIsInstance(d2, dict)
-    #     self.assertEqual(d1.keys(), d2.keys())
-    #     for k in d1.keys():
-    #         for m in d1[k].keys():
-    #             m1 = d1[k][m]
-    #             m2 = d2[k][m]
-    #             self.assertAlmostEqual(m1, m2)
-
     def setUp(self):
         self.maxDiff = None
         self.data_dir = os.path.join(os.path.dirname(__file__), 'data')
@@ -253,19 +139,19 @@ def test_fit_linear_regression_models_for_qiita(self):
 
         # These are text versions of the linear regression results
         # for the full data (see self.lingress_results and the
-        # "linear regressions" sheet of "absolute_quant_example.xlsx").
+        # "linear regressions counts" sheet of "absolute_quant_example.xlsx").
         expected_out = {
             'lin_regress_by_sample_id':
                 'A:\n'
-                '  intercept: -6.724238188489\n'
-                '  intercept_stderr: 0.236197627825\n'
+                '  intercept: -7.355939160548\n'
+                '  intercept_stderr: 0.271274537363\n'
                 '  pvalue: 1.42844e-07\n'
                 '  rvalue: 0.986503097515\n'
                 '  slope: 1.244876523791\n'
                 '  stderr: 0.073054085503\n'
                 'B:\n'
-                '  intercept: -7.155318973708\n'
-                '  intercept_stderr: 0.256395675584\n'
+                '  intercept: -7.450040830377\n'
+                '  intercept_stderr: 0.272941199932\n'
                 '  pvalue: 1.50538e-07\n'
                 '  rvalue: 0.986324179735\n'
                 '  slope: 1.246759136044\n'
@@ -292,19 +178,19 @@ def test_fit_linear_regression_models_for_qiita_w_casts(self):
 
         # These are text versions of the linear regression results
         # for the full data (see self.lingress_results and the
-        # "linear regressions" sheet of "absolute_quant_example.xlsx").
+        # "linear regressions counts" sheet of "absolute_quant_example.xlsx").
         expected_out = {
             'lin_regress_by_sample_id':
                 'A:\n'
-                '  intercept: -6.724238188489\n'
-                '  intercept_stderr: 0.236197627825\n'
+                '  intercept: -7.355939160548\n'
+                '  intercept_stderr: 0.271274537363\n'
                 '  pvalue: 1.42844e-07\n'
                 '  rvalue: 0.986503097515\n'
                 '  slope: 1.244876523791\n'
                 '  stderr: 0.073054085503\n'
                 'B:\n'
-                '  intercept: -7.155318973708\n'
-                '  intercept_stderr: 0.256395675584\n'
+                '  intercept: -7.450040830377\n'
+                '  intercept_stderr: 0.272941199932\n'
                 '  pvalue: 1.50538e-07\n'
                 '  rvalue: 0.986324179735\n'
                 '  slope: 1.246759136044\n'
@@ -333,15 +219,15 @@ def test_fit_linear_regression_models_for_qiita_w_alt_config(self):
         expected_out = {
             'lin_regress_by_sample_id':
                 'A:\n'
-                '  intercept: -8.198448239722\n'
-                '  intercept_stderr: 0.543935662662\n'
+                '  intercept: -9.005671912625\n'
+                '  intercept_stderr: 0.624713705225\n'
                 '  pvalue: 1.287067e-05\n'
                 '  rvalue: 0.958056670088\n'
                 '  slope: 1.590774502959\n'
                 '  stderr: 0.168235061352\n'
                 'B:\n'
-                '  intercept: -8.723558660515\n'
-                '  intercept_stderr: 0.586319521146\n'
+                '  intercept: -9.100255596079\n'
+                '  intercept_stderr: 0.624155431954\n'
                 '  pvalue: 1.2820953e-05\n'
                 '  rvalue: 0.958097757898\n'
                 '  slope: 1.593537551784\n'
@@ -366,13 +252,13 @@ def test_fit_linear_regression_models_for_qiita_w_negs(self):
         min_counts = 50
 
         # These are text versions of the linear regression results
-        # for the full data (see self.lingress_results and the
-        # "linear regressions" sheet of "absolute_quant_example.xlsx").
+        # for the full data (see self.lingress_results and the full data fit on
+        # "linear regressions counts" sheet of "absolute_quant_example.xlsx").
         expected_out = {
             'lin_regress_by_sample_id':
                 'B:\n'
-                '  intercept: -7.155318973708\n'
-                '  intercept_stderr: 0.256395675584\n'
+                '  intercept: -7.450040830377\n'
+                '  intercept_stderr: 0.272941199932\n'
                 '  pvalue: 1.50538e-07\n'
                 '  rvalue: 0.986324179735\n'
                 '  slope: 1.246759136044\n'
@@ -397,19 +283,19 @@ def test_fit_linear_regression_models_for_qiita_w_log_msgs(self):
 
         # These are text versions of the linear regression results
         # for the data with syndnas with <200 total counts removed (see
-        # "linear regressions" sheet of "absolute_quant_example.xlsx").
+        # "linear regressions counts" sheet of "absolute_quant_example.xlsx").
         expected_out = {
             'lin_regress_by_sample_id':
                 'A:\n'
-                '  intercept: -6.767160120684\n'
-                '  intercept_stderr: 0.301479875957\n'
+                '  intercept: -7.404604502655\n'
+                '  intercept_stderr: 0.34490591545\n'
                 '  pvalue: 2.170514e-06\n'
                 '  rvalue: 0.982777689569\n'
                 '  slope: 1.256194910944\n'
                 '  stderr: 0.089276147107\n'
                 'B:\n'
-                '  intercept: -7.196128673001\n'
-                '  intercept_stderr: 0.326579863246\n'
+                '  intercept: -7.49322862874\n'
+                '  intercept_stderr: 0.347041014613\n'
                 '  pvalue: 2.289073e-06\n'
                 '  rvalue: 0.982512701026\n'
                 '  slope: 1.25681918648\n'
@@ -522,23 +408,23 @@ def test_fit_linear_regression_models_w_log_msgs(self):
         # The slope, intercept, rvalue, stderr (of slope),
         # intercept_stderr, and p-value (of slope) values for these results
         # match those calculated in Excel (see results for data with
-        # syndnas with <200 total counts removed on "linear regressions" sheet
-        # of "absolute_quant_example.xlsx").
+        # syndnas with <200 total counts removed on "linear regressions counts"
+        # sheet of "absolute_quant_example.xlsx").
         expected_out_dict = {
             'A': {
-                "slope": 1.2561949109446753,
-                "intercept": -6.7671601206840855,
+                "slope": 1.25619491094468,
+                "intercept": -7.40460450265582,
                 "rvalue": 0.982777689569875,
-                "pvalue": 2.1705143708536327e-06,
-                "stderr": 0.08927614710714807,
-                "intercept_stderr": 0.30147987595768355},
+                "pvalue": 2.17051437085363E-06,
+                "stderr": 0.0892761471071481,
+                "intercept_stderr": 0.344905915450109},
             'B': {
-                "slope": 1.2568191864801976,
-                "intercept": -7.196128673001381,
-                "rvalue": 0.9825127010266727,
-                "pvalue": 2.2890733334160456e-06,
-                "stderr": 0.09002330756867402,
-                "intercept_stderr": 0.32657986324660143}
+                "slope": 1.2568191864802,
+                "intercept": -7.49322862874098,
+                "rvalue": 0.982512701026673,
+                "pvalue": 2.28907333341599E-06,
+                "stderr": 0.0900233075686737,
+                "intercept_stderr": 0.347041014613366}
         }
         expected_out_msgs = [
             "The following sample ids were in the experiment info but not in "